教师人机（人智）协作（协同）素养生成和提升机制

Artificial Intelligence (AI) literacy has come to the spotlight, empowering individuals to adeptly navigate the modern digitalised world. However, studies on teacher AI literacy in the English as a Foreign Language (EFL) context remain limited. This study aims to identify intraindividual differences in AI literacy and examine its associations with age and years of teaching experience among 782 English teachers. Given the absence of a reliable instrument to measure teacher AI literacy, we first constructed and validated a scale encompassing five sub‐scales: AI Knowledge, AI Use, AI Assessment, AI Design, and AI Ethics. Subsequently, latent profile analysis (LPA) was conducted using Mplus 7.4, with the results revealing four distinct profiles: Poor AI literacy (C1: 12.1%), Moderate AI literacy (C2: 45.5%), Good AI literacy (C3: 28.4%), and Excellent AI literacy (C4: 14.1%). Multinomial logistic regression analyses indicated significant associations between teacher AI literacy and both age and years of teaching experience. Additionally, 32 respondents participated in semi‐structured interviews. The qualitative data analysed with MAXQDA 2022 triangulated the quantitative results and offered deeper insights into teachers’ perceptions of their AI literacy. This study provides both theoretical and practical implications for understanding teacher AI literacy in the Chinese EFL context.

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Introduction. The rapid development of artificial intelligence (AI) technologies and their integration into the educational system necessitate that teachers become proficient in applying AI tools in their professional activities. Currently, the advancement of artificial intelligence literacy (AI literacy) is a focal point internationally, with educational programmes aimed at enhancing AI literacy for both students and teachers being implemented. However, despite a general interest in the topic of artificial intelligence, the term “AI literacy” is rarely used in Russian scientific publications. Therefore, there is a clear need to understand the concept of “literacy in the field of artificial intelligence” from a new perspective. In this context, the professional activities of teachers and the identification of their content become the aim of this study. Methodology and research methods. The primary research methods employed included the analysis of scientific, pedagogical, and methodological literature, as well as the synthesis, systematisation, and generalisation of facts and concepts. Additionally, content analysis was conducted on the materials sourced from leading quotation and analytical databases. Results and scientific novelty. In this paper, AI literacy is defined as a set of knowledge, skills, and abilities in the field of artificial intelligence that enables individuals to understand the fundamental principles of AI technologies and interact with them effectively while addressing both professional and personal tasks. Additionally, it allows individuals to critically evaluate the ethical risks and societal consequences associated with the use of these technologies. The authors have developed and presented a component structure of teachers’ AI literacy, which comprises five interrelated components: cognitive, activity-based, reflexive, personal, and ethical. The authors have elucidated the content of each component and proposed potential directions for the professional development of teachers in this domain.Practical significance. The materials presented in this paper can serve as a foundation for enhancing the professional development system for teachers in the context of digital transformation in education.

Conducting teacher AI literacy assessments is a key task in implementing the digital transformation of education. In 2024, UNESCO released the AI Competency Framework for Teachers, providing systematic guidance for developing AI literacy for teachers worldwide. This article first systematically introduces the framework's five aspects and three progression levels. Based on this framework, it designs and develops a teacher AI literacy assessment system, aiming to provide tools to support the diagnosis, training, and personalized development of teachers' AI competences.

Teacher education increasingly requires educators to engage with generative AI technologies, yet critical and reflective engagement opportunities remain scarce. While AI is often framed as a tool for automation, its broader pedagogical and ethical implications receive less attention. To address this gap, we implemented a critical co-discovery approach within an online AI in Education (AIEd) course to enhance educators’ AI literacy. This illustrative case study examines which AI literacy components can be developed through critical co-discovery and how this approach fosters educators’ reflective, critical, and participatory engagement with AI. Findings revealed that through co-discovery activities, educators co-constructed an understanding of AI concepts, ethical considerations, and context-specific applications. The study highlights the need for prolonged engagement with AI literacy by integrating it into teacher education program to ensure educators can critically navigate and assert their agency in AI’s complex role in education.

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With the recent advancements in generative artificial intelligence (AI) technology, the need for AI utilization in school education has increased, leading to active research on teacher AI literacy (competency). This study aims to develop self-assessment items for teacher AI literacy, recognizing the importance of developing and evaluating teachers' AI literacy in the context of school education where AI-integrated education is expected to become widespread. Using a research methodology that involved a meta-analysis of domestic and international literature on teacher digital literacy and digital competency frameworks, common core constructs were extracted. These constructs were integrated into Eisner’s (2002) conceptual framework of teachers' three types of professional knowledge, forming a novel AI literacy assessment framework. The resulting self-assessment items comprise 36 questions across nine domains: understanding the basic principles of AI, foundational knowledge of data science, educational applications of AI, practical use of AI tools, foundational programming skills, problem-solving capabilities, understanding AI ethics, analyzing social impacts, and responsible AI usage. To verify the content and structural validity of the items, a participatory design approach was employed, applying the developed AI literacy assessment items to in-service teachers. Feedback was collected through focus group interviews (FGI) with research participants, and the results were used to refine the final self-assessment items for teacher AI literacy. The self-assessment scale developed in this study serves as a practical tool for teachers to evaluate and enhance their educational competencies in alignment with the technological environment of the AI era.

The application of artificial intelligence in education prompts an evolution in the professional competencies required of teachers. Current discussions on teacher AI literacy are predominantly situated within the context of general education, failing to capture the unique characteristics of vocational education, such as industry-education integration and school-enterprise collaboration. Consequently, a specific framework for vocational college teachers is absent, and existing research has not addressed this need. This study, grounded in empowerment theory, constructs an AI literacy framework for vocational college teachers. It elaborates on the competency dimensions related to human-computer collaboration, including the use of AI to understand industry demands, design instructional scenarios, and align curriculum with workplace requirements. The research further analyzes the practical constraints on literacy enhancement from the perspectives of policy environments, institutional support mechanisms, and teacher cognition, proposing corresponding developmental pathways. This study aims to provide a theoretical reference and practical guidance for the professional development of educators in the vocational sector.

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The rapid integration of artificial intelligence (AI) in education requires teachers to develop AI competencies while preparing students for a society influenced by AI. This study evaluates the impact of an online teacher training program on German in-service teachers’ AI literacy, usage behaviors, and attitudes toward AI. A pre-post design study was conducted with teachers (N = 436 for attitude assessment, among whom N L  = 291 teachers for AI literacy) participating in the course. The program combined synchronous and asynchronous learning formats, including webinars, self-paced modules, and practical projects. The participants exhibited notable improvements across all domains: AI literacy scores increased significantly, and all attitude items regarding AI usage and integration demonstrated significant positive changes. Teachers reported increased confidence in AI integration. Structured teacher training programs effectively enhance AI literacy and foster positive attitudes toward AI in education.

Generative AI tools such as ChatGPT are reshaping educational practice, yet first-year teacher-education students often lack the prompt-engineering skills and confidence required to use them responsibly. This pilot study examined whether a concise three-session clinic on prompt engineering could simultaneously boost AI literacy and reduce technology anxiety in prospective teachers. Forty-five freshmen in a Peruvian teacher-education program completed validated Spanish versions of a 12-item AI-literacy scale and a 12-item technology-anxiety scale one week before and after the intervention; normality-checked pre–post differences were analysed with paired-samples t-tests, Cohen’s d, and Pearson correlations. AI literacy rose by 0.70 ± 0.46 points (t (44) = −6.10, p < 0.001, d = 0.91), while technology anxiety fell by 0.58 ± 0.52 points (t (44) = −3.82, p = 0.001, d = 0.56); individual gains were inversely correlated (r = −0.46, p = 0.002). These findings suggest that integrating micro-level prompt-engineering clinics in the first semester can help future teachers engage critically and comfortably with generative AI and guide curriculum designers in updating teacher-training programs.

Preparing new teachers for the reality of artificial intelligence in education (AIEd) has become a pressing issue. This study was conducted in a Canadian teacher education program that offers a course on digital technologies incorporating a module on AIEd. This paper addresses two research questions: 1) What were teacher candidates’ (TCs’) experiences with the module on AIEd? and 2) What were TCs’ views on the use of AI by themselves and their students? The study employed an explanatory mixed methods design, combining quantitative and qualitative data gathered via a survey administered to TCs directly following their module completion. Participants were two cohorts of TCs (108 TCs in 2024 and 104 TCs in 2025). Findings show TCs’ satisfaction with the module as they highlighted three major benefits: offering useful teaching resources; more acceptance to explore the technology and embrace it critically; and promoting AI literacy. TCs expressed an inclination to use AI as teachers. However, they expressed negative views toward their students’ use of AI. Additionally, most TCs demonstrated developing levels of critical AI literacy, especially among the most recent cohort. This research offers insights into promoting TCs’ AI literacy and presents implications for teacher education research, practice, and policy.

The paper presents research and preliminary findings aimed at improving curricula so that digital competencies are aligned with the required Artificial Intelligence (AI) literacy. The research was conducted at the Faculty of Technical Sciences in Čačak, University of Kragujevac (Serbia). The participants in the research were future computer science teachers and IT engineering students. The research tool for self-evaluation of AI literacy was a questionnaire based on the Serbian version of the AILS (Artificial Intelligence Literacy Scale), while digital competencies, based on the DigComp framework, were determined by objective testing. The research took into account the socioeconomic status of the students, demographic characteristics, and English language proficiency. Preliminary results indicated the persistence of significant relationships between certain digital competencies (such as programming, digital signal processing, and creative thinking) and all four constructs of AI literacy. The research findings highlight the impact of AI literacy on data analysis performance and problem solving.

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Artificial Intelligence (AI) is rapidly transforming education, demanding that educators possess a multidimensional literacy extending beyond technical skills to include affective, behavioural, cognitive, and ethical competencies. This study systematically investigates these four dimensions of AI literacy among 265 pre-service teachers (124 STEM, 141 non-STEM) at Institut Pendidikan Guru Kampus Pendidikan Teknik (IPGKPT) using the validated AI Literacy Questionnaire (AILQ) grounded in the ABCE framework. Results reveal that Ethical Learning (EL) and Affective Learning (AL) scored highest, indicating strong ethical awareness and motivation, while Cognitive Learning (CL) lagged, highlighting a persistent gap in foundational understanding. Notably, independent samples t-tests showed no significant differences between STEM and non-STEM groups in AL and BL, but a moderate advantage for STEM participants in CL and EL. These findings challenge the notion that AI literacy is exclusive to technical fields, underscoring the need for equitable, cross-disciplinary integration of AI literacy within teacher education. Building on previous research, the study identifies a disconnect between awareness and application, particularly in cognitive and behavioural domains. It recommends embedding hands-on, ethics-anchored, and discipline-inclusive AI training into teacher education curricula, aligned with the values of Education 5.0. The study further advocates for institutionalising a comprehensive AI literacy framework as a national competency model, ensuring that future educators are not only AI-literate but also equipped to lead ethical, inclusive, and transformative AI integration in Malaysian classrooms and beyond. This research offers actionable recommendations for curriculum reform and policy, aiming to empower educators with agency, adaptability, and ethical judgment in post-pandemic learning environments.

ABSTRACT This self-study highlights the collaborative journey of two elementary teacher educators from a rural institution as they navigated the integration of Generative AI (GenAI) into teaching and research. To grow their AI literacy, they engaged in reflective practices, collaborative discussions, and hands-on implementation of GenAI technologies. Through this process, they documented their experiences with AI-driven chatbots, offering insights into their learnings and concerns. Their reflections underscored the importance of understanding GenAI’s role in education, the challenges of integrating GenAI tools effectively, and the potential of GenAI in teaching and learning. The educators concluded that while GenAI can augment human capabilities, it cannot replace human expertise due to its limitations. By capturing their journey, this study contributed to the growing discourse on AI literacy among teacher educators, emphasizing the need for upskilling and ongoing professional development in the face of rapidly evolving technologies.

The development of artificial intelligence is rapidly transforming education systems worldwide, including those in the Czech Republic. This paper assesses the level of AI literacy among teachers and its integration into the Czech education system. The aim is to determine how well teachers are equipped with knowledge, skills, and understanding of AI, and how effectively they can integrate AI and AI awareness into their teaching. The methodology entails gathering data via a questionnaire survey distributed to teachers across various educational levels. The survey comprises questions that concentrate on AI literacy aspects, such as basic AI principles, the capacity to apply AI to teaching, discussing ethical issues related to AI use in the school environment, and critical thinking abilities. The questionnaire analyses the current state and challenges teachers face when integrating AI into teaching and learning practices. The results will be evaluated to identify areas where teachers' AI literacy needs strengthening and to propose strategies and recommendations for improving teacher training programmes and support. These strategies comprise teacher training, the provision of resources and support for integrating AI into the classroom, and reflection on the ethical and societal aspects of AI. It is equally important to invest in the long-term development of AI literacy among teachers as a fundamental step towards effectively harnessing the potential of AI in education and preparing students for the digital future. This article presents strategies and recommendations for the further development of AI literacy among teachers in the Czech Republic. The objective is to enhance their capacity and facilitate the more effective utilisation of AI in education.

While the transformative potential of artificial intelligence (AI) in education is widely recognized, the rapid evolution of these technologies necessitates a corresponding evolution in teacher education. This research sought to investigate the impact of a targeted training program on pre-service physics teachers’ AI literacy levels and their subsequent attitudes and intentions toward AI adoption in their future teaching.A pre-post-test control group quasi-experimental study was implemented among physics teacher education students. A 5 weeks long out-of-curriculum intervention was designed and implemented that combined theoretical grounding with practical, problem-based learning activities, with a focus on the use of various AI tools.There was a significant upswing in AI literacy performance post-intervention, showcasing that the training was effective in facilitating participants’ understanding and application of AI in educational contexts. Additionally, perceived usefulness of AI was found to be a partial mediator in the link between literacy scores and behavioral intention to embed generative solutions into potential teaching.The study concludes that incorporating comprehensive AI literacy programs into teacher education curricula is essential for fostering a technologically adept and pedagogically innovatively minded teaching workforce. Further research is needed to explore the long-term effects of AI literacy training on teacher practice and student learning outcomes.

In the realm of education, the integration of AI literacy into computer science teaching is becoming increasingly crucial (Walsh et al., 2023; Voulgari et al., 2022; Velander et al., 2023). Teachers play a pivotal role in bridging the gap between research and practical knowledge transfer of AI-related skills, necessitating a solid foundation in AI-related technological, pedagogical, and content knowledge (TPACK) (Velander et al., 2023). As AI systems permeate various aspects of society, including education, teachers must adapt and develop competencies in AI to effectively impart these skills to students (Kreinsen & Schulz, 2023). The incorporation of AI ethics into the curriculum requires teachers to navigate complex issues such as biases related to race, gender, and social class, challenging both computer science and humanities educators to step out of their comfort zones and collaborate to provide high-quality instruction (Walsh et al., 2023). By leveraging their expertise in different domains and receiving support from research teams, teachers can create engaging learning experiences that prepare students for the ethical and technical challenges posed by AI systems (Walsh et al., 2023).This article aimed to study the AI literacy level of teacher students major in digital technology who study at Nakhon Sawan Rajabhat University in Thailand. There were 98 students responded the AI literacy questionnaire which contained of 4 factors (Knowledge and Use of AI 24 questions, Creation of AI 3 questions, AI Self-Efficacy 6 questions, and AI Self-Competency 7 questions). The results showed that, 1) there were no statistically significant differences in gender among Knowledge and Use, AI Self-Efficacy, and AI Self-Competency while has statistically highly significant as P < 0.05 in Creation of AI factor, 2) there were no statistically significant different in level of study and use of time used of computer among AI literacy factors, and 3) there was relationship between AI literacy factors with statistically highly significant as P < 0.01.

This article highlights the significance of AI Literacy for promoting sustainable teacher education in an AI-driven world. Given the rapid progress of AI, a crucial aspect of organisational development for teacher education institutions involves fostering AI Literacy among teaching staff, and enabling them to use and teach AI ethically and responsibly. We underscore the necessity for teacher education institutions to create opportunities for developing AI Literacy as a fundamental goal for sustainable development. Further, we explore recommendations for sustainable organisational and professional development as well as future research directions.

Artificial Intelligence (AI) and its associated applications are ubiquitous in today's world, making it imperative that students and their teachers understand how it works and the ramifications arising from its usage. In this study, we investigate the experiences of seven teachers following their implementation of modules from the MIT RAICA (Responsible AI for Computational Action) curriculum. Through semi-structured interviews, we investigated their instructional strategies as they engaged with the AI curriculum in their classroom, how their teaching and learning beliefs about AI evolved with the curriculum as well as how those beliefs impacted their implementation of the curriculum. Our analysis suggests that the AI modules not only expanded our teachers' knowledge in the field, but also prompted them to recognize its daily applications and their ethical and societal implications, so that they could better engage with the content they deliver to students. Teachers were able to leverage their own interdisciplinary backgrounds to creatively introduce foundational AI topics to students to maximize engagement and playful learning. Our teachers advocated their need for better external support when navigating technological resources, additional time for preparation given the novelty of the curriculum, more flexibility within curriculum timelines, and additional accommodations for students of determination. Our findings provide valuable insights for enhancing future iterations of AI literacy curricula and teacher professional development (PD) resources.

Purpose This study aims to investigate how preservice teachers’ stages of concern, beliefs, confidence and interest in AI literacy education evolve as they deepen their understanding of AI concepts and AI literacy education. Design/methodology/approach AI literacy lessons were integrated into a technology integration course for preservice teachers, and the impacts of the lessons were evaluated through a mixed-methods study. The Concerns-Based Adoption Model was employed as the analytical framework to explore participants’ specific concerns related to AI. Findings Findings revealed that participants initially lacked AI knowledge and awareness. However, targeted AI literacy education enhanced preservice teachers’ awareness and confidence in teaching AI. While acknowledging AI’s educational benefits, participants expressed ongoing concerns after AI literacy lessons, such as fears of teacher displacement and the potential adverse effects of incorporating generative AI on students’ critical learning skills development. Originality/value Despite the importance of providing preservice teachers with AI literacy skills and knowledge, research in this domain remains scarce. This study fills this gap by enhancing the AI-related knowledge and skills of future educators, while also identifying their specific concerns regarding the integration of AI into their future classrooms. The findings of this study offer valuable insights and guidelines for teacher educators to incorporate AI literacy education into teacher training programs.

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Abstract This study assessed AI literacy among a group of preservice-teachers, and investigated the effectiveness of a suggested professional development program based on the Instructional Design Framework for AI literacy in developing pre-service teachers AI literacy skills. A quasi experimental approach with post-test pretest design was used for data collection. Thirty-seven undergraduate students from Palestine Technical University Kadoorie and Hebron University participated in this study. An AI literacy scale was developed, validated, and used for data collection. The results showed that the professional development program was effective in developing the AI literacy skills among the participant preservice teachers. Gender and specialty area of the participants did not significantly affect preservice teachers AI literacy skills after participating in the professional development program. Given the research results, it is recommended that teachers should be supported to integrate AI in classroom activities. AI technological tools should be included in pre-service and in-service training programs. Further AI tools combined with other professional development strategies should be researched among teachers from different subjects to enable generalizing the finding of this research and increasing it is validity.

This research employs a qualitative case study methodology to examine the experiences of eight teacher candidates enrolled in a Bachelor of Education program as they used Teachable Machine, an online tool for developing simple machine learning models. Informed by teacher candidate identity theory (Birch et al., 2025; Birch & Pike, in press), the study analyzed participants’ engagement with an assignment designed to foster familiarity with machine learning models. Findings reveal that hands-on experience with Teachable Machine enabled teacher candidates to recognize biases and limitations inherent in AI technologies, as well as devise strategies for integrating AI tools in classroom settings which may promote meaningful learning and mitigate bias. Participants identified strategies for integrating AI into their future classrooms, emphasizing the importance of fostering critical AI literacy and promoting equitable, inclusive teaching practices. This research provides data to inform teacher education programs as they seek to prepare their teacher candidates for the AI-infused classroom.  

Culturally Relevant Pedagogy (CRP) is vital in K-12 education, yet teachers struggle to implement CRP into practice due to time, training, and resource gaps. This study explores how Large Language Models (LLMs) can address these barriers by introducing CulturAIEd, an LLM tool that assists teachers in adapting AI literacy curricula to students' cultural contexts. Through an exploratory pilot with four K-12 teachers, we examined CulturAIEd's impact on CRP integration. Results showed CulturAIEd enhanced teachers' confidence in identifying opportunities for cultural responsiveness in learning activities and making culturally responsive modifications to existing activities. They valued CulturAIEd's streamlined integration of student demographic information, immediate actionable feedback, which could result in high implementation efficiency. This exploration of teacher-AI collaboration highlights how LLM can help teachers include CRP components into their instructional practices efficiently, especially in global priorities for future-ready education, such as AI literacy.

Artificial Intelligence (AI) is transforming the educational landscape, making AI literacy an essential competency for teachers. Teaching self-efficacy, defined as a teacher’s belief in their ability to facilitate learning effectively, is equally crucial for teacher preparedness. This study examines the relationship between AI literacy and teaching self-efficacy among 100 prospective teachers in Tamil Nadu. A descriptive correlational survey method was employed using two researcher-constructed instruments: the AI Literacy Scale (30 items) and the Teaching Self-Efficacy Scale (12 items). Data were analyzed using descriptive statistics, t-test, ANOVA, correlation, and regression analyses. Findings revealed a moderate level of AI literacy (M = 3.42, SD = 0.56) and high teaching self-efficacy (M = 3.78, SD = 0.49). A significant positive correlation (r = .46, p < .01) was found between AI literacy and teaching self-efficacy. Regression analysis showed that AI literacy significantly predicted teaching self-efficacy (β = .38, p < .01), explaining 18% of the variance. The study concludes that enhancing AI literacy may boost confidence and teaching competence among future educators. Recommendations are made for integrating AI literacy and pedagogical efficacy training in teacher education programs.

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With the breakthrough progress of Generative Artificial Intelligence (AIGC) technology, the teaching of Ideological and Political Theory Courses (hereinafter referred to as "Civics Courses") in universities is facing a critical node of transformation from digitization to intelligence. For a long time, technology has mostly played the role of an "auxiliary tool" in Civics teaching, suffering from pain points such as solidified scenarios, superficial interactions, and insufficient personalization. Generative AI, with its unique knowledge generation capabilities, multimodal context construction abilities, and human-like interaction logic, provides an opportunity for Civics teaching to shift from the paradigm of "Technical Assistance" to "Human-Machine Synergy." Based on the practical needs of the reform and innovation of Civics Courses in the new era, this paper deeply analyzes the theoretical connotation of the "Human-Machine Synergy" teaching model. It systematically reconstructs the practical model of scenario-based teaching in Civics Courses from three dimensions: the "Dynamic Knowledge Graph" in theoretical teaching, the "Virtual-Real Twinning" in practical teaching, and "Intelligent Decision-Making" in social services. Furthermore, addressing the potential loss of teacher subjectivity, algorithmic bias, and ethical risks in human-machine synergy, this paper proposes building a governance mechanism of "Value Leadership, Dual-Teacher Synergy, and Ethical Regulation," aiming to provide theoretical support and practical solutions for promoting the high-quality development of university Civics Courses.

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Critical AI literacy is an active area of scientific research and current scholarship on the integration of generative AI technologies in language education. However, there is a dearth of research into Canadian adult educators’ perceptions of and experiences with critical AI literacy development from an autoethnographic perspective. To address this research lacuna, the author conducted a narrative study of his college English for academic purposes classes over three academic semesters in 2024 and 2025. The data, generated from the researcher’s teacher learning journal and regular interactions with ChatGPT as a reflective partner, highlighted three main research results and implications for pedagogical practices. First, developing adult educators AI literacy is a form of teacher professional learning, which can position the learners as class collaborators and knowledge co-creators. Next, adapting teaching approaches to sustain more human-focused learning experiences involves three levels of complexities: between the educator and the chatbot, the learners’ interactions with AI technologies, and the teacher-learner relationship as one of partnership and exploration. Last, to engage the students as active agents in the process of learning, adult educators should craft sound pedagogical approaches to enhance language teaching, stimulate learner participation, and create human-focused teaching interventions in AI-enhanced higher education settings.

This research article explores the transformative role of Artificial Intelligence (AI) in the evolution of the modern teacher, examining both the significant opportunities it presents and the inherent challenges that must be addressed. AI holds immense potential to revolutionize educational practices by enabling personalized learning experiences, automating administrative tasks, providing intelligent tutoring systems, and offering real-time feedback. It can enhance student engagement, support diverse learning needs, and free up educators to focus on higher-order teaching functions like fostering critical thinking, creativity, and socio-emotional development. However, the integration of AI also brings forth critical challenges, including concerns about data privacy and security, algorithmic bias, the potential for over-reliance on technology, maintaining human interaction, the need for adequate teacher training, and ensuring equitable access. This paper synthesizes current research to provide a comprehensive overview of AI's impact on teaching pedagogy, aiming to inform educators, policymakers, and developers on how to harness AI's benefits while mitigating its risks for a more effective and inclusive educational future.

Generative AI, particularly Language Models (LMs), has the potential to transform real-world domains with societal impact, particularly where access to experts is limited. For example, in education, training novice educators with expert guidance is important for effectiveness but expensive, creating significant barriers to improving education quality at scale. This challenge disproportionately harms students from under-served communities, who stand to gain the most from high-quality education. We introduce Tutor CoPilot, a novel Human-AI approach that leverages a model of expert thinking to provide expert-like guidance to tutors as they tutor. This study is the first randomized controlled trial of a Human-AI system in live tutoring, involving 900 tutors and 1,800 K-12 students from historically under-served communities. Following a preregistered analysis plan, we find that students working with tutors that have access to Tutor CoPilot are 4 percentage points (p.p.) more likely to master topics (p<0.01). Notably, students of lower-rated tutors experienced the greatest benefit, improving mastery by 9 p.p. We find that Tutor CoPilot costs only $20 per-tutor annually. We analyze 550,000+ messages using classifiers to identify pedagogical strategies, and find that tutors with access to Tutor CoPilot are more likely to use high-quality strategies to foster student understanding (e.g., asking guiding questions) and less likely to give away the answer to the student. Tutor interviews highlight how Tutor CoPilot's guidance helps tutors to respond to student needs, though they flag issues in Tutor CoPilot, such as generating suggestions that are not grade-level appropriate. Altogether, our study of Tutor CoPilot demonstrates how Human-AI systems can scale expertise in real-world domains, bridge gaps in skills and create a future where high-quality education is accessible to all students.

Human-AI interaction, particularly in educational contexts, is a dynamic and cognitively demanding process that holds promise for enhancing goal-directed learning. Yet, there remains a scarcity of empirical studies that examine how learners’ interaction with generative AI (GenAI) varies in structure and how these patterns influence distinct learning outcomes. This study investigates the relationship between human-AI interaction processes and outcomes such as AI literacy, problem-solving skills, metacognitive strategies, and task performance. We conducted an experimental study with 45 secondary school physics student teachers engaged in a GenAI-supported lesson plan assessment task. Using questionnaire responses, trace data, and prompt logs, we coded human-AI interaction behaviours based on self-regulation and cognitive processing levels. Through sequence clustering analysis, we identified two distinct interaction patterns. Both clusters showed significant improvement in task performance, but with divergent benefits. Cluster 1 exhibited diverse regulation processes characterized by exploratory, divergent prompting and low-level cognitive engagement in the early stages. This group showed significant gains in problem-solving skills through active idea generation and broad reflection. Cluster 2 demonstrated structured regulation behaviours, initiating interaction with deep-level cognitive processing and convergent prompting. These learners made more deliberate modifications and completed full self-regulated learning (SRL) cycles—planning, monitoring, and reflecting—which led to enhanced AI literacy and metacognitive strategy use. Our findings suggest that effective human-AI collaboration goes beyond prompt diversity; structured regulation behaviours serve as a key mediator between prompting and learning gains. GenAI served as both cognitive and metacognitive scaffolding, facilitating critical assessment and productive delegation. These results contribute to SRL theory in AI contexts and emphasize the importance of process-level analysis. Limitations include a small sample and limited prompt feature analysis. Future research should explore emotion-aware AI systems, multimodal interaction data, and the impact of task complexity on interaction dynamics. This study provides practical insights for educators and designers of AI-integrated learning systems. Specifically, it highlights the importance of tailoring AI scaffolds to different learner regulation styles: for exploratory learners, scaffolds can encourage broad idea generation and reflection, while for structured learners, scaffolds should support iterative planning and monitoring. These findings underline both opportunities and limitations of current GenAI use in classrooms, suggesting concrete directions for teacher practice and instructional design.

This article explores the ongoing interaction between human intelligence and artificial intelligence (AI), with a particular focus on the concept of algorithmic empathy – a type of simulated affective response with tangible educational value – and on the dynamics of digital co-creation. Drawing on interdisciplinary perspectives and qualitative data collected through semi-structured interviews with 57 teachers and 542 students from Romania, the study explores how AI systems can support learning, foster innovation, and adapt to users’ cognitive and emotional needs thematically analysed to provide in-depth insights. The analysis identifies key factors that shape effective human-AI communication, such as digital literacy, trust, personalization, and ethical awareness. While AI is perceived as a valuable tool for enhancing educational processes and decision-making, challenges related to transparency, over-reliance, and reduced human connection are also highlighted. The findings show that meaningful human-AI collaboration requires not only technological refinement but also a critical and ethical rethinking of the roles both actors play in a shared digital ecosystem. The study underscores the importance of algorithmic empathy and conversational digital literacy in sustaining user motivation, while warning against dependency and the erosion of critical inquiry. In educational settings, AI is best positioned as a complement to, not a substitute for, the human educator. These insights reinforce the need for human-centred, ethically grounded AI integration strategies, especially in environments where learning, equity, and emotional support intersect.

As generative AI (GenAI) models are increasingly explored for educational applications, understanding educator preferences for AI-generated lesson plans is critical for their effective integration into K-12 instruction. This exploratory study compares lesson plans authored by human curriculum designers, a fine-tuned LLaMA-2-13b model trained on K-12 content, and a customized GPT-4 model to evaluate their pedagogical quality across multiple instructional measures: warm-up activities, main tasks, cool-down activities, and overall quality. Using a large-scale preference study with K-12 math educators, we examine how preferences vary across grade levels and instructional components. We employ both qualitative and quantitative analyses. The raw preference results indicate that human-authored lesson plans are generally favored, particularly for elementary education, where educators emphasize student engagement, scaffolding, and collaborative learning. However, AI-generated models demonstrate increasing competitiveness in cool-down tasks and structured learning activities, particularly in high school settings. Beyond quantitative results, we conduct thematic analysis using LDA and manual coding to identify key factors influencing educator preferences. Educators value human-authored plans for their nuanced differentiation, real-world contextualization, and student discourse facilitation. Meanwhile, AI-generated lesson plans are often praised for their structure and adaptability for specific instructional tasks. Findings suggest a human-AI collaborative approach to lesson planning, where GenAI can serve as an assistive tool rather than a replacement for educator expertise in lesson planning. This study contributes to the growing discourse on responsible AI integration in education, highlighting both opportunities and challenges in leveraging GenAI for curriculum development.

In the era of artificial intelligence (AI), translation education faces pressing challenges to integrate human–machine collaboration into talent cultivation. This study protocol outlines a two-year mixed-methods project that focuses on developing, validating, and applying systematic tools for assessing human–machine collaborative competence among translation students and teaching competence among translation instructors. In the first stage, measurement instruments will be constructed and validated through thematic analysis, exploratory and confirmatory factor analysis, based on interviews, pilot testing, and large-scale surveys. The second stage will employ grounded theory, structural equation modeling, and regression analysis to identify the factors influencing students’ and teachers’ competence development. The third stage will investigate industry needs through semi-structured interviews with translation service professionals and descriptive statistical analysis across multiple domains. The fourth stage will assess classroom effectiveness via multimodal analysis of teaching and learning processes across five types of universities. Finally, a comprehensive training strategy will be designed, refined through action research pilot implementations, and validated by Delphi consultation with translation educators, industry specialists, and policymakers. By integrating empirical rigor with iterative validation, this study advances theoretical modeling of human–AI collaboration, establishes robust assessment tools for students and teachers, and delivers actionable training strategies that align translation education with evolving professional and industry needs.

This research investigates how Artificial Intelligence (AI) tools are being embedded into translation education through a Content and Language Integrated Learning (CLIL) oriented framework. It focuses on two central concerns raised by the teaching community: first, how do translation educators view the integration of AI tools in their teaching, and what difficulties do they encounter when trying to balance these tools with more traditional approaches? Second, what skills do students need in order to collaborate effectively with AI in translation, and how might the curriculum be reshaped to help them build these skills? Through the semi-structured interviews with 9 instructors and focus group discussions with 21 students across three different university types: application-oriented, normal, and academic,the research offers grounded insights into how AI is being experienced in day-to-day translation teaching . The findings reveal that educators view AI as a valuable tool for enhancing translation efficiency but express concerns regarding its impact on critical thinking and cultural sensitivity. Students emphasize the importance of mastering AI tools such as ChatGPT and DeepL, while also critically engaging with AI outputs, particularly in terms of cultural nuances and ethics. The study proposes the integration of AI tools into translation curricula within a CLIL framework, aligning AI-enhanced tasks with the development of content knowledge, language proficiency, as well as cognitive and cultural competencies. These findings point to a pressing need to rethink translation training—not by replacing traditional methods, but by weaving AI use into tasks that still cultivate linguistic nuance, reflective practice, and ethical reasoning.

Multiple-choice questions (MCQs) serve as fundamental assessment tools in computing education, where high-quality distractors are critical for evaluating conceptual understanding and debugging skills. While large language models (LLMs) show promise in automating distractor generation, their effectiveness in reasoning-intensive programming domains remains understudied. Another challenge is that current evaluation metrics often emphasize surface-level semantics rather than the logical reasoning required in programming tasks, limiting their practical utility for educators. To compare AI-generated and human-authored distractors, this study collected 925 MCQs from two online high school courses. The collected data include the question stem, correct answer, and three human-authored distractors for each question. For AI-generated distractor generation, we employed the GPT-4 API through a structured prompt containing: (1) question stem, (2) correct answer, (3) Bloom’s taxonomy level, and (4) instructional constraints. To determine the Bloom’s taxonomy level for each question, two assessment experts independently classified all questions based on Bloom’s taxonomy (Remember, Understand, Apply, Analyze, Evaluate, Create), achieving moderate inter-rater reliability (Cohen’s K = 0.67). Discrepancies, which occurred in 33% of cases, were resolved by a third expert to ensure accurate cognitive-level categorization. The GPT-4 model generated three plausible distractors per question while maintaining cognitive-level alignment. This study proposes a human–AI collaborative framework to evaluate distractor quality in programming education. Our human–AI collaborative evaluation framework combined human expertise with AI analysis (using GPT-4 and DeepSeek-V3) through a three-phase process: First, human-created and AI-generated distractors were anonymized and randomized. Next, human experts and AI models independently selected the three most pedagogically effective distractors per question based on plausibility and challenge potential. Finally, we implemented a ranking system prioritizing distractors with the highest selection frequency across evaluators, with ties resolved by cross-validator agreement. Results demonstrate that AI-generated distractors achieve comparable quality to human-crafted ones for foundational programming concepts (e.g., syntax recall and basic logic). However, significant gaps emerge in higher-order cognitive domains, particularly in questions requiring code behavior analysis (35% lower selection rate by human evaluators) or solution evaluation (42% lower). Qualitative analysis reveals AI’s tendency to produce superficially plausible but pedagogically weaker distractors for complex reasoning tasks, often missing subtle misconceptions that human instructors capture. This work makes three key contributions: (1) a validated framework for evaluating programming distractors across cognitive levels, (2) empirical evidence of LLM capabilities and limitations in reasoning-intensive question types, and (3) practical guidelines for integrating AI in MCQ design—particularly recommending human oversight for higher-order questions. Our findings equip educators with evidence-based strategies to leverage AI while maintaining assessment rigor, especially in introductory programming courses where misconception targeting is crucial. Future research will investigate how contextual learner modeling can support more personalized and pedagogically aligned distractor generation.

In an era of rapid technological advancement, the integration of Artificial Intelligence (AI) is reshaping online education and redefining professional development for educators. This reflective manuscript adopts a joint practitioner-research perspective, combining applied research and practical experience in online teaching. We explore how virtual humans can support educators, foster innovation, strengthen teacher agency, and contribute to inclusive and ethical AI adoption in online education. This contribution emphasizes the importance of ethical frameworks, collaborative international ecosystems, and practice-driven innovation to ensure that human values and pedagogy remain at the heart of AI-enhanced online learning environments.

The fast evolution of Artificial Intelligence (AI) and the developing Artificial General Intelligence (AGI) capabilities transform how education operates, particularly through its effect on teacher training. AI-based systems provide adaptable learning spaces, and they offer both real-time assessment capabilities and data-driven educational method improvements. With its capability for human-level cognitive operations, AGI creates conditions to transform educator skill advancement processes. The article examines AI and AGI integration within teacher education programs by discussing their practical uses and advantages, together with the encountered challenges and ethical dilemmas. The analysis combines evaluative and creative AI tools like Gradescope and ChatGPT, and Carnegie Learning, with developing capabilities in AGI. The article uses detailed analysis, together with tables, along pictorial representations to show the necessity of achieving optimal teacher training through AI-human balanced cooperation. The research finds that AI brings efficiency benefits, but AGI's prospective function needs strict governance together with educational alignment, to maintain ethical, unbiased teacher education.

A new domain Media and Artificial Intelligence Literacy (MAIL) will be assessed in PISA 2029, and will involve four dimensions: engaging with AI, creating with AI, managing AI and designing AI. While this assessment primarily targets secondary education, it will also evaluate and enhance teachers’ preparedness for AI integration across educational levels. We believe that pre-service teacher education should integrate substantial AI literacy initiatives, as graduates will take on positions as educators in either secondary or primary school. In this conceptual paper, we propose that teacher preparation programmes at higher education institutions should be prepared and oriented towards the AI Competency Framework for Teachers. In this article, we propose the framework established by UNESCO in 2024. The AI Competency Framework for Teachers (AI CFT) includes fifteen skills and dispositions across five strands: human-centred mindset, AI ethics, technical foundations, pedagogy, and professional learning. Our proposal advocates for embedding an AI literacy curriculum within subject-specific courses and teaching methods courses while also offering hands-on AI integration experiences based on the demonstrable connection between the MAIL areas and AI CFT categories. By helping teacher-candidates rise from Acquire to Deepen—and, where possible, to Create in each strand, the programme is already preparing them to design the learning experiences that PISA 2029 will assess. This study is a foundational stage in developing a nationally validated scale to assess pre-service teachers’ readiness for AI-enabled classrooms in Australia.

The integration of generative artificial intelligence (AI) into educational assessment has shown potential in addressing inefficiencies in traditional evaluation methods, particularly in time-constrained STEM classrooms. This study proposes a hybrid framework that synergizes teacher expertise with generative AI to streamline the evaluation of verbal skills-a critical yet underexplored competency in STEM education. By focusing on collaborative dialogue, problemsolving explanations, and conceptual reasoning, this research aims to develop a system that enhances assessment efficiency while preserving the nuanced judgment of educators. Verbal skill evaluation in STEM contexts-such as assessing students’ ability to articulate hypotheses, defend solutions, or collaborate in technical discussions-remains labor-intensive and subjective. Teachers spend significant time analyzing spoken or written responses, often sacrificing opportunities for personalized instruction. While generative AI model proficiency in language processing standalone using in education raises concerns: (1) lack of contextual awareness in STEM-specific discourse, (2) potential biases in automated scoring, and (3) displacement of teachers’ formative feedback roles. In this research using a mixed-methods approach was employed across three phases: Framework Development, Pilot Testing, Scalability. By bridging the divide between automation and human judgment, this hybrid framework demonstrates that generative AI need not replace teachers but can instead amplify their capacity to nurture critical verbal skills in STEM. Future work explored by adaptive AI tutoring systems that leverage this model to provide real-time dialogue support during student presentations or group discussions.

The rapid development of information technology has put forward higher requirements for teachers, and the traditional training model is difficult to meet the demand. The article constructs a teacher digital competency framework based on the ASTD model, realizes the division of teachers' professional competence, and explains the professional core connotation of teacher digital competency in detail. A personalized resource recommendation model for teachers is constructed using artificial intelligence technology, which provides accurate recommendations for teachers through candidate resource extraction and learning resource screening. At the same time, with the help of Google Cloud Services digital tools, the design of teachers' digital teaching and research activities was accomplished, and communication and cooperation with users in the virtual community was promoted. The combination of the two is integrated into the development of teachers' professional skills to enhance their teaching competence. The mean values of accuracy, applicability, timeliness, personalization, and diversity of learning resource recommendations under artificial intelligence technology ranged from 4.123 to 4.544, with good recommendation performance. The Google Cloud Services platform can promote teaching and research exchange activities among teachers. The use of artificial intelligence and digital tools makes teachers improve their professional skills in knowledge base, instructional design, teaching and research between 24.04% and 91.00%, and with their intervention, teacher competency shows significant improvement.

AI holds substantial and profound implications for teaching and learning, as well as for the enhancement of teachers' roles and capabilities. The integration of AI in educational contexts demonstrates considerable potential to enable innovative approaches to instruction, learning, and administration, while also improving learning experiences and facilitating teachers' professional responsibilities. As AI continues to transform modes of production, daily life, and educational practices, it is steering human society into a new developmental phase (Zhu & He, 2012).This research employs the Delphi method and integrates the "AI Competency Framework for Teachers" (AI CFT; Miao, 2024) with the Teacher AI Competence Self-efficacy (TAICS) scale, initially explored by Thomas et al. (2024) in their study on teacher competency self-efficacy. Grounded in Thomas’s scale and Bandura’s self-efficacy theory, the present study aims to develop a scale for assessing Teacher AI Competence Self-efficacy (TAICS) among university faculty. The scale design process will incorporate the perspectives of educators and account for the specific contextual factors of AI-mediated teaching and learning within higher education institutions in China’s border ethnic regions.

No abstract available

Currently, the education sector is undergoing a transformation centered around artificial intelligence, continually optimizing resource allocation, promoting educational equity, and enhancing the personalization, interactivity, and intelligence of learning, making education simpler, more enjoyable, and sustainable. To ensure that teachers can use artificial intelligence responsibly and effectively, UNESCO has released the “AI Competency Framework for Teachers” aimed at promoting lifelong professional development for educators. In this context, research on instructional design that can achieve this goal becomes particularly important. This study proposes an expanded “5P Funnel” framework based on the “3P Funnel”. The framework includes five key elements: Purpose (teaching objectives), Product (learning outcomes), Priority (teaching contents), Pare down (eliminate redundancies), and Provide (appropriate AI tools). The study employs a quasi-experimental research design to explore the potential impact of the “5P Funnel” framework on the professional development of pre-service teachers in Zhaoqing, Guangdong. In this study, 204 candidates in the “Modern Educational Technology Application” course were randomly divided into an intervention group (n=102) and a control group (n=102), with the experiment lasting one semester (15 weeks). By analyzing the differences and relationships in the dimensions of professional development abilities between the two groups, the study evaluates the experimental effects and draws conclusions. Although the study is not yet complete, the paper will focus on the research methodology and preliminary progress, laying the foundation for future contributions. Once the experimental results are obtained, they will provide new ideas and methods for teacher training and practice in the AI era.

Creativity is an essential competency for teachers in the 21st century, yet it remains underrepresented in many teacher education programs. This study explores how teaching creativity can be systematically integrated into BA-level teacher training applying AI-driven tools to enhance learning outcomes. Drawing on the Hierarchical Pyramid, AI TPACK, and UNESCO AI Competency Frameworks, the study investigates teacher-students’ and faculty perceptions of creativity and AI integration in three Azerbaijani pedagogical universities. A qualitative methodology, including semi-structured interviews with 15 teacher-students and 6 faculty members, revealed significant gaps in structured creativity training and AI literacy. Findings highlight the complementary potential of the three frameworks in fostering creativity and professional development. To address these gaps, this study proposes a structured process for integrating creativity teaching and AI tools into teacher education, providing practical strategies aligned with global competency standards. Implications for curriculum design, professional development, and further research are discussed.

In today’s digital era, teachers are expected to incorporate artificial intelligence (AI) into the classroom. Teacher educators must therefore model its use while evaluating their own AI-related knowledge to guide future teachers effectively. Existing assessments often rely on self-reporting questionnaires, which may introduce bias, and the TPACK (Technological-Pedagogical-Content-Knowledge) framework, which overlooks distinctive AI characteristics. This study develops and validates an AI-TPACK assessment tool for teacher educators, grounded in authentic pedagogy and systematically designed through the ADDIE model (Analysis, Design, Development, Implementation, and Evaluation). The study aims to identify AI-relevant TPACK components and add new ones; test the tool’s validity; and analyze teacher-educator competency patterns. The development involved dual literature reviews (22 TPACK studies; 34 AI studies) and empirical analysis of 60 authentic instructional artifacts. Five experts confirmed their content validity (CVR = 0.86, CVI = 0.91) and the inter-rater reliability (ICC = 0.84, range 0.76–0.88). The tool comprises 4 components—AIK, AIPK, AICK, and Integration—14 criteria, and 65 indicators, and reveals four competency patterns: technological innovator; pedagogical integrator; content developer; and beginner. The strong correlation (r = 0.78) between AIPK and integration underscores the importance of synergy. The tool contributes theoretically and practically to advancing teacher-educators’ AI knowledge and competency assessments.

: We designed a 6-hour teacher development course aimed at enhancing teachers’ competency in teaching STEM activities. The course focused on teaching teachers how to develop learners’ problem-solving abilities and digital creativity using both introductory concepts of the Internet of Things (IoT) and artificial intelligence (AI) data model training skills in teaching STEM activities. This study evaluated the teachers’ competency in teaching STEM activities and the outcomes of their creative ideas in solving problems using what they had learned in this course. Two hundred and one teachers from 108 primary schools attended the course, of whom 191 responded to the pre-and post-course surveys on the TPACK framework, and 176 of them produced artefacts demonstrating their digital creativity. The paired t-test results indicated statistically significant improvement on all 17 TPACK items, with a large effect size (Cohen’s d = 1.213). In the digital creativity evaluation, 82.20% of the teachers demonstrated digital creativity and expressed their ideas in designing introductory IoT systems, and 72.77% of the teachers included AI components in their design. One future research direction is to evaluate primary students’ learning outcomes in STEM activities with these introductory concepts of IoT and AI data model training skills.

This mixed-methods study proposes a computational framework for AI-TPACK development among 120 pre-service English teachers in China, integrating GPU-accelerated NLP training (reducing model fine-tuning time by 89%), edge computing for equity (lowering technical barriers by 31%), and adversarial debiasing targeting regional/gender biases (reducing accent discrimination by 42% and stereotypes by 39% via Stereotype Score metrics). Structural equation modeling reveals that self-efficacy (β=0.59) and curriculum integration (β=0.53) mediate 62% and 74% of AI-TPACK development, respectively, while rural-urban disparities persist but decrease from 18% to 9% (p=0.03) after controlling for digital literacy and device access. A three-tiered micro-credential system shows pass rate gaps (68% urban vs. 32% rural), highlighting the need for technology equity policies. Longitudinal tracking confirms GPU-accelerated training retains 89% competency gains at 8 weeks. The study redefines AI-TPACK as a socio-technical construct grounded in SCOT framework and critical AI studies, emphasizing scalable, ethically designed systems for global teacher education.

Abstract Artificial intelligence (AI), especially generative AI (GenAI), is rapidly permeating every aspect of our lives, driving an accelerated evolution of how we work, play, and learn, thus necessitating new competencies for teachers and students. This study develops and validates an AI competency framework tailored for teachers and students, with an emphasis on researcher-teacher co-creation. The researcherteacher collaboration highlights the importance of teacher involvement in the design process, ensuring the framework’s alignment with real-world educational practices. The framework identifies four key skills: identification of AI mechanisms and their operation; effective and informed use of AI; AI agency: proactive and value-generating utilization of AI; and ethical use of AI, each with specific abilities and components. It also outlines necessary values, attitudes, and knowledge for engaging with AI in education, aiming to prepare teachers and students for an AI-saturated world. This study discusses the need for assessment indicators and assimilation models.

No abstract available

This study aimed to develop and validate the Mathematics Teacher Competency Diagnostic Tool (MTCDT) to systemati-cally assess mathematics teachers' professional competencies. In response to recent competency-based teacher development policies, five key domains were identified through an analysis of teacher training programs from 2019 to 2024: (1) mathematical content knowledge, (2) lesson design and implementation, (3) AI and digital integration, (4) learning guidance and coaching, and (5) reflection and improvement. A two-round Delphi survey was conducted with 13 experts in mathematics education and teacher training. All items demonstrated strong validity (CVI ≥ .90, CVR ≥ .80). The MTCDT offers a structured framework for diagnosing elementary to high school mathematics teachers' competencies and provides a foundation for designing modular, needs-based professional development programs.

Abstract The assessment of writing competency in French as a Foreign Language presents significant challenges due to the multidimensional nature of writing and heterogeneous evaluation practices. This research introduces an innovative framework integrating artificial intelligence and interactive visualization to support the formative assessment of writing competency. The developed Interactive Curves Graph system combines automated analysis with stakeholder engagement in a learner-centered approach. Through a qualitative study involving 15 French as a Foreign Language teachers across 3 primary schools in Morocco, we investigated the effectiveness of an AI-enhanced progress monitoring system. The participating teachers (mean age = 37.25 years, mean teaching experience = 17 years) implemented the system with 190 students across 7 classes. The methodology employed a design-based research approach, incorporating iterative development phases and comprehensive validation through teacher feedback and usage analysis. Data collection spanned 1.5 months and included semi-structured interviews, system interaction statistics, and detailed feedback analysis using Atlas.ti coding, with two distinct examination periods. Results reveal three major contributions: (1) successful stakeholder integration through interactive visualization capabilities that support assessment objectivity, (2) AI-enhanced analysis facilitating pattern detection and immediate feedback, and (3) a multi-directional communication framework strengthening educational collaboration and decision-making. Teacher responses demonstrate that the Interactive Curves Graph system effectively addresses formative assessment challenges by combining computational rigor with pedagogical accessibility. This research advances theoretical understanding and practical implementation of digital technologies in writing assessment. The findings suggest that the developed approach can significantly enhance writing competency eAssessment practices while opening perspectives for broader educational applications. The study’s limitations, including sample size and context specificity, indicate directions for future research in cross-cultural validation and longitudinal impact assessment.  

The accelerating demands of engineering education in the 21st century, coupled with the emergence of artificial intelligence (AI), require a redefinition of the competencies of engineering educators. This study develops an integrated competency model tailored to the needs of modern engineering education. It synthesizes five established frameworks and incorporates recent research achievements on engineering teacher competencies. Through a detailed comparative analysis, this study refines a model that balances technical competence, pedagogical competence, and professional and ethical competence. The model is visually represented to highlight the interconnections between these domains. This study provides a foundation for improving faculty development and aligning teaching with industry and societal demands. It offers both theoretical insight and practical guidance for building educational excellence in engineering for a sustainable future.

The pedagogical promise of Competency-Based Education (CBE) has been historically undermined by profound challenges of scalability, creating an implementation gap between its theoretical merits and practical application. This paper proposes a testable mechanism model wherein Artificial Intelligence (AI) enables the scaling of CBE through three interconnected pathways—diagnostic tracking, adaptive supply, and teacher orchestration—formalized within a distributed cognition framework. To operationalize this model, this paper introduces novel constructs including the "Adaptive-Autonomy Curve" for systematically cultivating self-regulated learning in personalized environments, and a "Situated Performance-Based Assessment Pipeline" for authentic, scalable evaluation of complex skills. The primary contributions of this work are fourfold: first, it provides a rigorous conceptual taxonomy that delineates CBE from adjacent paradigms such as mastery learning and personalized learning; second, it advances a set of falsifiable propositions to guide future empirical research; third, it formalizes the human-AI pedagogical relationship with operational design principles; and fourth, it presents an integrated governance and interoperability protocol for the responsible and effective implementation of AI in competency-based systems.

The increasing integration of artificial intelligence (AI) in education highlights the need for teacher preparation programs to support pre-service teachers in developing pedagogically grounded and ethically responsible AI competencies. This study designed and preliminarily examined an Experiential Design Learning model within a Digital Learning Ecosystem (EDL–DLE) to support the development of AI competencies and instructional innovation in pre-service science teacher education. A four-phase research and development framework was employed, including conceptual synthesis, model design and expert validation, implementation, and evaluation. Participants were 19 second-year pre-service science teachers from a university in Bangkok. Research instruments included a 40-item AI competency assessment and an instructional innovation evaluation rubric. Paired-sample t-test results indicated statistically significant pre–post difference across all AI competency dimensions, with large effect sizes (Cohen’s d = 0.82–1.59), reflecting notable within-group changes observed within the EDL–DLE learning context. The instructional innovation lesson plans were evaluated as generally strong across multiple dimensions, particularly in learner-centered pedagogy, creativity, and collaboration, while relatively lower performance was observed in appropriate AI technology selection and ethical use. Overall, the findings provide preliminary evidence supporting the feasibility of the EDL–DLE model as an exploratory instructional approach for fostering foundational AI-related pedagogical competencies in pre-service science teacher education.

The rapid integration of generative AI in education often frames teachers as technology users who primarily need technical training. Existing prompt engineering frameworks offer technical guidance but have limited grounding in theories of teacher professional development or reflective practice. This misses a key feature of prompt engineering: prompting can externalize pedagogical thinking, making AI interaction a process of knowledge externalization. Through systematic conceptual analysis, this paper proposes a reconceptualization of prompt engineering from a technical competency to a reflective professional practice. The methodology integrates three theoretical traditions: Schön’s reflective practice theory (for externalizing tacit knowledge), Wiggins and McTighe’s backward design (for structuring instructional decisions), and Celik’s AI-TPACK framework (as integrated knowledge base). This synthesis suggests that effective prompting can be understood as an act of pedagogical externalization requiring integrated professional knowledge. The paper develops a seven-strategy framework (RPE framework) as an analytic lens for examining prompt engineering sophistication. This theoretical framework offers theory-derived hypotheses that require future empirical validation rather than presenting verified outcomes. Ultimately, the RPE framework offers a conceptual basis for potentially shifting the focus from technical training to teacher professional development by repositioning educators as AI-assisted instructional designers rather than mere AI users.

Purpose: The aim of this study is to design and apply AI convergence education courses for pre-service teachers and analyze their educational effects. Methods: The course focused on developing the ability to understand and utilize generative AI and apply it to the educational field. To this end, the educational content was designed to allow for experiences such as understanding AI concepts and principles, the concept of AI convergence education, sharing of AI convergence education cases based on subjects, understanding and practicing generative AI, and developing a teaching and learning process using the GATe framework. The course was applied through theory and practice for 15 weeks to 74 pre-service teachers. The changes in pre-service teachers' AI convergence education capabilities and satisfaction with the course were confirmed through a pre- and post-matched sample t-test. Results: All sub-factors of ‘knowledge’, ‘utilization’, and ‘value’ of AI convergence education competency of pre-service teachers who participated in the class improved to a statistically significant degree, and the satisfaction with the course was also high. Conclusion: In this study, we confirmed the educational effect of improving the AI convergence education capacity of pre-service teachers through the AI convergence education course designed and applied. Based on this, we suggested the direction of the education curriculum and education content design to enhance the AI convergence education capacity of pre-service teachers.

The incorporation of Artificial Intelligence in education is revolutionizing the ways in which teaching, learning, and assessment are conducted. The National Curriculum Framework for School Education (NCF-SE 2023) acknowledges AI's ability to enhance personalized education, adaptive evaluations, and computational thinking, while also emphasizing the significance of ethical considerations, data protection, and the partnership between humans and AI. This study explores AI's influence on competency-based education, where AI-powered tools offer real-time feedback, intelligent automation, and tailored learning experiences. Framework also integrates AI into vocational education, preparing students with the digital literacy and problem-solving skills needed for future careers. Nonetheless, the extensive use of AI in education brings up issues related to algorithmic bias, the security of student data, and an over-dependence on AI-driven teaching. The framework underscores the necessity for responsible AI integration, teacher training, and regulatory oversight to ensure AI enhances rather than replaces traditional educational methods. This paper critically assesses the advantages, challenges, and ethical considerations of AI in school education as outlined in the framework. It contends that AI should be implemented strategically and fairly, promoting an inclusive, human-centred learning environment that balances technological innovation with educational integrity.

This study examines the transformation of technological literacy in the context of the AI era and proposes a school-based educational framework for its development. Through a comprehensive literature review, it analyzes the evolving definition of technological literacy— from practical tool use to a broader competency encompassing algorithmic thinking, ethical reasoning, and critical understanding of AI systems. Drawing on frameworks such as ITEEA’s Standards for Technological Literacy and the DigComp model, the study identifies current barriers including infrastructure gaps, insufficient teacher training, and fragmented curricula. Key findings highlight that technological literacy must be scaffolded across educational levels: First, elementary education should focus on basic digital fluency and ethical awareness; Second, middle school should introduce structured AI concepts and data reasoning; Third, high school should engage students in interdisciplinary, project-based learning that critically examines AI’s societal implications. The study concludes that integrating AI literacy into school curricula is both essential and achievable, requiring updates in policy, curriculum design, and educator training. This research contributes a developmental roadmap for cultivating technologically literate, ethically grounded citizens prepared to thrive in an AI-driven society. Keyword : AI Literacy, Algorithmic Thinking, Data Literacy, K–12 Education, Technological Literacy, Technology Education

This study examines the transformation of technological literacy in the context of the AI era and proposes a school-based educational framework for its development. Through a comprehensive literature review, it analyzes the evolving definition of technological literacy— from practical tool use to a broader competency encompassing algorithmic thinking, ethical reasoning, and critical understanding of AI systems. Drawing on frameworks such as ITEEA’s Standards for Technological Literacy and the DigComp model, the study identifies current barriers including infrastructure gaps, insufficient teacher training, and fragmented curricula. Key findings highlight that technological literacy must be scaffolded across educational levels: First, elementary education should focus on basic digital fluency and ethical awareness; Second, middle school should introduce structured AI concepts and data reasoning; Third, high school should engage students in interdisciplinary, project-based learning that critically examines AI’s societal implications. The study concludes that integrating AI literacy into school curricula is both essential and achievable, requiring updates in policy, curriculum design, and educator training. This research contributes a developmental roadmap for cultivating technologically literate, ethically grounded citizens prepared to thrive in an AI-driven society. Keyword : AI Literacy, Algorithmic Thinking, Data Literacy, K–12 Education, Technological Literacy, Technology Education

The role of artificial intelligence (AI) and digital technologies in education can be transformative for K–12 learning in Bangladesh. New-age education framework must be free from conventional shackles as the nation moves towards competency-driven and future-ready curricula. This paper investigates the ways in which AI-enabled tools and educational technologies help build the so-called smart curriculum, which warrant personalized learning, continuous assessment and inclusive education addressing students from different socio-economic strata. In terms of the national context, it reflects on the need for greater action to address the urban-rural digital divide, increase teacher readiness and policy implementation. Based on current research and case examples, the paper discusses what ethical considerations and infrastructure are needed. Finally, it lays out policy recommendations to facilitate equitable and sustainable AI integration in the K–12 curriculum to prepare Bangladeshi students for a rapidly changing world, equipped with the skills, values, and adaptability needed for the future.

This study presents findings from a professional development (PD) webinar aimed at sensitizing and gathering teacher educators’ knowledge of Generative Artificial Intelligence (GAI). The primary objective of the webinar was to deepen teacher educators’ understanding and applications of GAI within the context of teacher education in Ghana and to identify areas requiring additional development. Three hundred and seven participants from a diverse group, including teacher educators, administrators, and in-service teachers participated in the PD session. The session was conducted online via Zoom. The video and audio recordings were transcribed and analyzed thematically using MAXQDA version 2022.4. Findings indicate a diverse range of familiarity with GAI among participants. While some expressed knowledge of GAI tools, others were learning about GAI for the first time. Further, the findings showed an increasing curiosity among participants for the inspiring functions of GAI in education, such as automatic scoring, academic writing, assisting teachers with image generation for their classroom practices, etc. The participants demonstrated a willingness to include GAI in their classroom practices and support their students. However, they also identified infrastructural gaps, such as the expense of premium GAI tools, training on GAI promptings, and ethical issues such as transparency, as potential barriers to the successful implementation of GAI in teacher education. Therefore, the study suggests that institutional support should be provided to teacher educators. This support would expand their access to various GAI tools and features. The study further recommends integrating GAI, including explainable GAI and prompt engineering, as a core component of teacher education and continuous professional development programs. Additionally, it emphasizes the importance of strengthening educators' skills in innovative assessment practices.

This study aimed to propose a Professional Development Model (PDM) for chemistry teachers to enhance their professional development in Artificial Intelligence (AI). The research group consisted of 17 chemistry teachers. The study was designed using a particular case study suitable for qualitative research methods. Document review, teacher interviews, and AI opinions were utilized to create the model. Data were analyzed using inductive content analysis. The document analysis emphasized the teachers' knowledge of various topics, such as AI knowledge, AI tools, AI skills, AI ethics, AI attitudes, and AI literacy, to enable them to incorporate AI into their lessons. It was also highlighted that teachers should acquire domain-specific knowledge, skills, and competencies in the areas where artificial intelligence will be integrated. When examining the recommendations of artificial intelligence (ChatGPT and Gemini), it was found that they addressed similar content to the information included in the document analysis. Furthermore, chemistry teachers stated their deficiencies in AI literacy, AI competencies, and developing AI lesson plans. They also stated that AI applications could be included in various subjects such as organic chemistry, chemical experiments, and chemical reactions. Following the analysis of documents and teacher and AI opinions, a 10-step PDM has been proposed to enhance chemistry teachers' professional development in AI.

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ABSTRACT The introduction of artificial intelligence (AI) in education is seen as a promising tool to enhance learning outcomes and provide students with engaging learning environments in developing countries such as Colombia. This case study aimed to investigate teachers’ perceptions of AI in K-12 education in public schools located in the Amazonian department of Caquetá, Colombia. The study focuses on teachers’ views on the integration of AI into teaching and learning activities. A total of 190 teachers were surveyed, of whom 30 were selected for semi-structured interviews. The main findings are as follows: (a) AI benefits teachers by facilitating and providing virtual assistance, (b) the challenges are the limited knowledge about AI and a lack of resources, (c) the concerns reported are that AI may hinder students’ development of critical thinking and problem-solving skills, and (d) ongoing professional development for integrating AI in education is suggested.

This study examines the effectiveness of artificial intelligence in teacher professional development and training, with specific emphasis on how it influences individualized learning, continuous skill improvement, and ethics. A quantitative approach was utilized to collect information from 244 Pakistani university teachers through a standardized questionnaire. The correlation test (r = 0.336, p < 0.01) indicated a positive moderate relationship between AI-based learning tools and teacher training success, which implies that teachers find AI to be useful. Regression analysis (R² = 0.006, p = 0.219) indicated a weak predictive relationship between AI and the effectiveness of teaching, which implies that AI is not fully responsible for improving the effectiveness of teaching. The findings indicate the need for blended models of learning, AI literacy education, and ethical application of AI to achieve the full potential benefits of AI. The present study contributes to increasing research studies on artificial intelligence in education and indicates additional research studies on long-term adoption and impacts of AI in different learning environments.

This paper conducts an in-depth inquiry into the impact of Artificial Intelligence (AI) on the roles assumed by teachers in the classroom setting. It formulates strategies for the professional advancement of teachers within the AI - enhanced educational environment. This research underscores the transformative ways in which AI is reconfiguring teacher roles, underscoring the necessity for teachers to adeptly adapt to and efficiently integrate AI - enabled tools. The paper expounds on the continuously evolving relationship between teachers and AI, delving comprehensively into both the opportunities it engenders and the challenges that surface. Ultimately, it meticulously details practical strategies for professional development, with the objective of arming teachers with the requisite skills essential for successfully traversing this dynamic educational landscape.

The rapid integration of Artificial Intelligence (AI) into education necessitates a re-evaluation of teacher professional development (TPD) paradigms, particularly for pre-service teachers (PSTs) at the threshold of their careers. This paper explores new pathways for TPD through a case study focused on PSTs’ utilization of AI tools for two fundamental pedagogical tasks: lesson planning and teaching reflection. The study investigates how engagement with selected AI applications, such as those designed for generating lesson frameworks and facilitating reflective practice, influences PSTs' skill acquisition, pedagogical thinking, and overall professional growth. Key findings from the synthesized case study reveal that while AI offers considerable benefits—including enhanced efficiency in planning, the generation of novel instructional ideas, and more structured, data-informed reflection—PSTs also encounter significant challenges. These include navigating the variable quality of AI-generated outputs, developing the critical AI literacy and prompt engineering skills required for effective use, addressing ethical considerations, and achieving deep pedagogical integration beyond superficial task completion. The study underscores the importance of AI-related Technological Pedagogical Content Knowledge (AI-TPACK) and suggests that AI can foster a cycle of continuous pedagogical improvement. The implications of these findings point towards a need to reimagine TPD, embedding AI literacy and critical engagement with AI tools within teacher education curricula to prepare PSTs for an AI-augmented educational landscape. This paper contributes to the field of Artificial Intelligence Education Studies by providing empirical insights into the practical application of AI in foundational teacher training and by proposing more dynamic, personalized, and practice-oriented professional development pathways.

The integration of Artificial Intelligence (AI) into English as a Foreign Language (EFL) teaching presents both opportunities and challenges for teacher education. While AI's role in enhancing language learning is increasingly recognized, limited research has explored how EFL teacher educators perceive and navigate AI integration. Addressing this gap, the present study investigates Indonesian EFL teacher educators’ perceptions of AI integration, focusing on the challenges they face, the competencies they require, and their professional development needs. Employing an interpretivist qualitative approach, the study analyzed data from semi-structured interviews with eight teacher educators at a private university in West Java. Thematic analysis—conducted both manually and with ChatGPT assistance—revealed key challenges including overreliance on AI, issues of academic integrity, and inadequate institutional support. Educators emphasized the need for AI literacy, pedagogical adaptability, and ethical awareness. They also identified the importance of structured, ongoing AI-focused professional development supported by institutional collaboration. Findings underscore the urgent need for responsive policies, ethical guidelines, and capacity-building efforts to support responsible AI adoption in teacher education. These insights contribute to the growing discourse on AI integration and inform the design of context-sensitive professional development programs in EFL contexts.

The rapid advancement of artificial intelligence (AI) and the growing demand for continuous professional development have transformed the educational landscape in Southeast Asia. Traditional teacher training models are often lengthy, rigid, and inaccessible for educators in remote or under-resourced areas. AI-driven microlearning offers a promising alternative by delivering personalized, bite-sized learning experiences that can be accessed anytime and anywhere. This study aims to explore the potential of AI-driven microlearning platforms as an innovative solution for teacher professional development (TPD) in Southeast Asia, addressing issues of accessibility, relevance, and engagement. A mixed-methods research design was employed, combining a systematic literature review with qualitative interviews involving 20 education policymakers and teacher trainers from Indonesia, Malaysia, the Philippines, and Thailand. Additionally, a pilot implementation of an AI-based microlearning application was conducted with 150 teachers in rural and urban schools over three months. Data were analyzed using thematic analysis and descriptive statistics. The findings indicate that AI-driven microlearning significantly enhances teacher engagement, improves access to relevant content, and supports adaptive learning paths based on individual needs. Policymakers and educators recognized the scalability and cost-effectiveness of AI-powered solutions for national TPD programs. However, challenges such as digital infrastructure disparities, data privacy concerns, and the need for localized content remain critical issues for large-scale adoption. AI-driven microlearning has the potential to reshape the future of teacher professional development in Southeast Asia by providing flexible, inclusive, and personalized learning opportunities. Strategic collaboration between governments, tech developers, and educational institutions is essential to address existing barriers and maximize its impact.

This research paper investigates the potential of AI-based chatbots to support teachers in identifying professional development (PD) opportunities tailored to subject-specific, pedagogical, and technological requirements. Drawing on the Technological, Pedagogical, and Content Knowledge (TPACK) framework, Self-Determination Theory (SDT), and reflexivity, we conducted a six-week field study with 1,125 teachers at the University College of Teacher Education Burgenland (Austria). A mixed-methods design was employed, integrating quantitative approaches (logistic regression, correlation) and qualitative techniques (content and sentiment analysis) to capture the breadth and depth of teacher–chatbot interactions. Results show a moderate positive correlation (ρ = 0.36, p < 0.05) between the specificity of user queries and user satisfaction, while targeted keywords (e.g., “digital didactics”) increased the likelihood of positive feedback by a factor of 2.05 (p < 0.01). Qualitative findings reveal that teachers have a pronounced interest in digital competencies, artificial intelligence, and inclusion, with 85% of user feedback on chatbot performance being positive. These findings suggest that AI-based chatbots can facilitate a more individualized, context-sensitive search for PD opportunities, thereby promoting teacher autonomy, competence, and relatedness. The paper discusses methodological and practical implications in the context of the EDEN Digital Learning Europe Annual Conference 2025 theme, “Empowering Inclusion, Innovation and Ethical Growth,” highlighting how AI-enabled PD tools align with broader European policy initiatives.

The continuous professionalization of teachers is crucial for sustaining high-quality education. However, traditional professional development (PD) programs often neglect individual needs, specific subject-area demands and distinct career stages, leading to limited relevance and uptake. This study addresses that gap by deploying an AI-based chatbot to provide context-sensitive, personalized PD recommendations at scale. Grounded in technological pedagogical content knowledge (TPACK)and self-determination theory, the research aims to evaluate how tailored chatbot interactions can enhance teachers' motivation, autonomy and technological proficiency while meeting pedagogical and content-specific requirements. Using a convergent parallel mixed-methods design, this study analyzed 2,030 valid chatbot interactions from 1,125 teachers in Austria's Burgenland region. Data collection incorporated the information systems success model (ISSM), the technology acceptance model (TAM) and TPACK as guiding frameworks. Quantitative metrics included fallback rates, implicit intent interpretation, sentiment analysis and confidence scores, whereas qualitative feedback examined perceived relevance. Descriptive and inferential statistics, alongside content analyses, were used to assess the chatbot's performance. This design enabled a comprehensive evaluation of both measurable indicators and user perspectives regarding chatbot-enabled PD recommendations. Results demonstrated a moderate fallback rate of 14.4%, significantly below established benchmarks and an overall positive user sentiment (85%). Quantitative analyses indicated that teachers submitting highly specific queries reported greater satisfaction, while logistic regression revealed that targeted pedagogical keywords significantly increased the likelihood of positive feedback. Qualitative insights underscored the importance of both detailed query formulations and domain-specific terminology. Collectively, these findings highlight robust chatbot performance and emphasize the critical role of contextualized, technology-oriented PD solutions for meeting teachers' individualized professional needs. Due to the relatively brief observation period and the self-selecting nature of participating teachers, these findings may not be generalizable across broader educational settings. The sample, drawn from a single Austrian region, may limit external validity. Future research should incorporate larger, more diverse populations, extend the timeframe to measure long-term outcomes and collect additional demographic data to assess subgroup variations. Longitudinal investigations into the sustained impact of chatbot-based recommendations on teaching practice can further elucidate the role of AI-driven PD in different educational contexts. Institutional stakeholders can optimize AI-based PD tools by encouraging teachers to submit more detailed queries and employ targeted pedagogical terminology. Additionally, systematic refinements, such as updating domain-specific vocabularies and improving natural language processing algorithms, can reduce fallback rates and enhance user satisfaction. Training programs aimed at familiarizing educators with chatbot functionalities and best practices can further increase engagement. By aligning professional development offerings with teachers' immediate needs and contexts, these strategies can strengthen the relevance, accessibility and overall impact of AI-driven PD initiatives. By providing accessible, context-sensitive PD resources, AI-driven chatbots may help democratize professional learning for teachers across diverse settings, including those with limited institutional support. This can contribute to narrowing digital skill gaps, especially in remote or underserved schools, thereby promoting educational equity. Enhanced teacher engagement in technology-enhanced PD aligns with broader objectives of fostering lifelong learning cultures and continuously improving educational quality. As teachers refine their digital competencies, they may experience greater autonomy and motivation, fostering a ripple effect on student outcomes and broader societal advancement. This research uniquely synthesizes the ISSM, TAM and TPACK to evaluate chatbot-supported teacher PD, offering a multi-faceted assessment of both user experience and educational relevance. By emphasizing the significance of query specificity and targeted pedagogical language, the study advances understandings of how AI-driven tools can address individualized teacher needs in diverse contexts. The findings deliver practical guidance for refining chatbot technologies and theoretical insights into the interplay of technology acceptance, pedagogical content knowledge and AI-based support systems, thereby contributing to ongoing discourse on data-informed professional development.

This study explores the diffusion of disruptive innovation in Islamic primary education through the implementation of Smart Teacher AI, an AI-driven professional learning platform designed to strengthen teachers’ digital competence. The research employs a Research and Development (R&D) approach based on Borg and Gall’s model, focusing on the implementation phase of Smart Teacher AI in SD Islam Al Ittihad, Cibubur. Data were collected from 25 teachers using observation sheets, digital competence rubrics, focus group discussions, and system usage analytics. The findings reveal that the implementation of Smart Teacher AI aligns with Rogers’ diffusion of innovation framework, showing gradual adoption from innovators to early and late adopters. The platform significantly improves teachers’ digital skills in lesson planning, designing interactive learning media, and evaluating digital learning outcomes. The novelty of this study lies in integrating AI-assisted professional learning within the framework of Islamic values, bridging the gap between technological disruption and faith-based education. The results contribute to both theoretical and practical insights on innovation diffusion in education, emphasizing that digital transformation in Islamic schools requires not only technology readiness but also value-driven leadership and sustainable pedagogical adaptation.

The pervasive integration of Artificial Intelligence (AI) into the educational landscape has precipitated a profound paradigm shift, compelling a systematic re-evaluation of pedagogical methodologies and educator roles across various disciplines. This paper focuses on the field of International Chinese Language Education (ICLE), a domain characterized by its unique intercultural and linguistic complexities. It posits that the advent of AI is not merely an introduction of new tools but a catalyst for a fundamental transformation of the ICLE teacher's identity and professional responsibilities. This conceptual study moves beyond a utilitarian discussion of AI applications to theoretically delineate the multifaceted role transitions required of ICLE educators—from knowledge transmitters to learning architects, from assessors to diagnosticians, from content creators to resource curators, from cultural ambassadors to intercultural competence cultivators, and from classroom managers to digital learning community orchestrators. By analyzing these evolving roles, the paper subsequently proposes a structured framework for professional development, emphasizing the cultivation of AI literacy, advanced pedagogical design principles, data-informed instructional strategies, and enhanced socio-affective competencies. The study argues that embracing a symbiotic human-AI collaborative model is imperative for the sustainable and effective development of ICLE. It concludes that the future-ready ICLE professional is not one who is replaced by AI, but one who is empowered by it, strategically leveraging technology to augment the irreplaceable human dimensions of language teaching and intercultural communication. This paper aims to provide a theoretical foundation for scholars, educators, and institutions to navigate the complexities of the AI era and to proactively shape the future of international Chinese language instruction.

This study explores the integration of generative AI (GenAI) tools in the professional development of English language teachers (ELT) using the Technological Pedagogical Content Knowledge (TPACK) framework. Subscribing to narrative inquiry research design, in-depth interviews were conducted with four educators in Kathmandu Valley, Nepal, in August 2024 to understand their experiences integrating GenAI in teaching practices. Four themes were identified using thematic analysis—enhanced teaching skills and methods, professional growth as a continuous learning process, GenAI dependency, and ethical challenges and coping mechanisms for further discussion. The findings disclose that GenAI enhances pedagogical strategies by providing personalized learning resources, dynamic classroom activities, and automated feedback, which foster student engagement and teacher adaptability. Participants noted that GenAI supports continuous professional growth by offering real-time insights to refine instructional methods and address diverse learner needs. However, challenges such as technical skill gaps, ethical concerns about data privacy, and the risk of over-reliance on AI, which may hinder critical thinking and teacher-student rapport, were identified. The study emphasizes the need to balance GenAI's technological benefits with human-centric pedagogy, underlining the importance of ethical guidelines, institutional training, and collaborative peer learning to reduce the dependency and algorithmic biases by aligning GenAI integration with TPACK principles—harmonizing technological, pedagogical, and content knowledge—the research advocates for structured support systems to empower educators in using AI responsibly. The implications call for policy frameworks prioritizing AI literacy, equitable access, and mindfulness practices to sustain professional development while preserving the irreplaceable role of human interaction in education.

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AI in the education sector has become an intense topic of discussion and there have been mixed feelings about its integration into teacher training programmes.Through the following study,an attempt has been made to understand the impact of integrating AI in teacher training programmes on teachers' professional development and the students' outcomes. The primary method was used to achieve the purpose of this study. 50 educators participated in a survey questionnaire in which they were asked a variety of questions such as their satisfaction levels concerning AI teacher training programme and improvement in their skills and overall student performance.The results were largely positive with slightly negative responses from very few participants.The majority of educators felt satisfied and engaged with AIled training platforms. It was discovered that teachers were positive that their pedagogical knowledge had improved with the utilisation of AI-led tools and teachers were able to share teaching materials with colleagues conveniently.

The integration of artificial intelligence (AI) in educational settings offers transformative potential for enhancing active learning strategies in science classrooms. This paper explores how AI-driven tools can support the implementation of active learning methodologies such as problem-based learning (PBL), interactive simulations, and personalized learning pathways. These strategies have been shown to increase student engagement, foster critical thinking, and deepen the understanding of scientific concepts. The analysis highlights the role of AI in creating adaptive learning environments where students receive real-time feedback and differentiated instruction tailored to their individual learning needs. An essential aspect of leveraging AI for active learning is ensuring that teachers are adequately prepared to implement these technologies effectively. The discussion delves into professional development programs that equip educators with the skills and knowledge to incorporate AI tools into their teaching practices. Such programs should emphasize hands-on training, collaborative workshops, and continuous learning opportunities that align with current advancements in educational technology. By fostering teacher confidence and proficiency, these initiatives ensure that educators can maximize the benefits of AI to enhance student learning outcomes. The paper also considers the implications of adopting AI in teaching for long-term educational practices, including ethical considerations, data privacy concerns, and the importance of maintaining a human-centric approach in classrooms. Examples of successful implementations and case studies provide insights into best practices and the challenges encountered. This comprehensive approach underscores the value of combining innovative technology with strategic teacher development to create enriched, interactive, and sustainable learning environments that promote critical thinking and environmental awareness among students.

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Despite existing research on AI applications in education (AIEd), the release of ChatGPT has disrupted the status quo in the educational landscape. Although this technology can personalize learning, decrease teacher workload, and offer access to a wealth of information, concerns around generative AI (GenAI) tools have emerged, including academic integrity, data accuracy, and bias in information. Given research highlights and acknowledging educators’ varied levels of awareness and conflicting views toward AIEd, two teacher educators (also authors of this paper) in the Faculty of Education at Brock University facilitated three workshops among different groups of teacher educators. The workshops focused on the emerging nature of GenAI tools, their affordances, and their implications for educators’ practices. Adopting a narrative inquiry approach, the authors describe the details of these workshops and present their reflections on the process of preparing for and facilitating them. Implications for teacher education research and practice are also presented and discussed.Keywords: artificial intelligence (AI), artificial intelligence in education (AIEd), teacher education, professional development, generative AI (GenAI)

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This study examines how data-informed professional development can strengthen teacher assessment literacy amid the growing demands of the digital era. Using a qualitative design, the research combines library investigation and content analysis of scholarly literature published over the past decade related to digital assessment, teacher data literacy, and technology-supported instructional decision-making. Findings reveal that digital transformation in education requires teachers to master data interpretation skills, utilize technology-enhanced assessment tools, and develop ethical awareness regarding privacy and algorithmic bias. However, many teachers continue to face competency gaps, inadequate professional development, and challenges in integrating digital assessment practices effectively. The study highlights that meaningful professional development must be continuous, collaborative, evidence-based, and incorporate data literacy, AI literacy, and reflective pedagogical practice. Such an approach equips teachers to interpret complex data, design valid digital assessments, and make ethically responsible, evidence-driven instructional decisions. This research provides conceptual clarity and a foundation for designing comprehensive professional development frameworks aimed at enhancing teacher assessment literacy within rapidly evolving digital learning environments.

In the wave of the digital intelligence era, artificial intelligence (AI) technology has deeply integrated into the education sector, reshaping the educational ecosystem and bringing profound changes to teachers 'professional development. The new teaching models, vast knowledge resources, and intelligent evaluation systems driven by AI not only provide opportunities for educational innovation but also pose challenges to teachers, such as a weakened role in teaching, increased pressure to update knowledge, and the need to reconstruct teaching evaluation models. This article, based on the impact of AI technology on education, systematically analyzes the key challenges faced by teachers' professional development in the digital intelligence era. It proposes strategies from dimensions such as reshaping teaching abilities, updating knowledge systems, and enhancing evaluation literacy, aiming to help teachers adapt to the demands of the digital intelligence era, achieve the iterative upgrade of their professional capabilities, and contribute to building a high-quality education system.

The rapid increase in new challenges of the combination of the Internet of Things (IoT) and artificial intelligence (AI), which are emerging technologies, can play a compelling role in prompting the development of artificial intelligence Internet of Things (AIoT). Therefore, the demand for AI competencies for everyone will increase. Educational institutes focus on encouraging AI education because the demand for AI-literate workers will increase in the industrial sector. However, teachers’ lack of AI knowledge is a significant barrier to AI education. Thus, developing the teacher’s AI competencies and educating them about how to use and teach students is critical. In this study, we proposed artificial intelligence of things professional development (AIoT-PD) training to prepare the AI competencies of teachers ready to teach. A quasi-experimental design with a two-day training workshop was conducted among 13 teachers to examine its impact on AI competencies, including AI knowledge, AI skill, and AI attitude. The quantitative data were collected via a pretest and posttest after the training activity, while qualitative data were collected via interviews. This study showed that teachers’ AI knowledge significantly improved. These findings revealed the AIoT training workshop’s effectiveness in enhancing teachers’ AI competencies, which can help them effectively teach students in AI education.

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Rapid technological advancements are reshaping pedagogical expertise development, offering novel pathways to equip educators with 21st-century professional competencies. This study proposes an innovative artificial intelligence (AI)-driven professional development approach and investigates its impact on student teachers’ competence development. In total, 28 third-year student teachers participated in tasks to mentor AI learners, applying mentor-acquired knowledge and skills. Task performance and task processes were used to delineate teacher knowledge and teaching practices, respectively, while data from professional development surveys were thoroughly analyzed to gain in-depth insights into teacher perspectives. Findings reveal that AI teaching practice significantly enhanced participants’ knowledge acquisition. Notably, high-performance groups demonstrated complex mentoring patterns emphasizing procedural mentoring. Conversely, the low-performance group preferred a more directive and factual approach, whose behavioral patterns appeared less significant. Furthermore, AI teaching practice also had a positive effect on student teachers’ perspectives toward professional knowledge and AI literacy. The findings of this study contribute to the theoretical and practical understanding of integrating AI-based learning activities into teacher education.

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This study aimed to understand how future teachers are integrating generative artificial intelligence into their academic routines during initial teacher education. Using a mixed-methods approach, quantitative data were collected through a Likert-scale questionnaire administered to 94 students at a School of Education in Portugal, complemented by a qualitative analysis of open-ended responses. The findings reveal widespread use of tools such as ChatGPT for clarifying doubts, supporting study practices, and enhancing academic writing. At the same time, ethical and pedagogical concerns emerge, particularly around plagiarism, the reliability of information, and the impact on critical thinking. Students show a simultaneously receptive and critical attitude, acknowledging the value of generative AI as a support tool while calling for transparency, regulation, and proper training to ensure its ethical and responsible use. The study highlights the importance of integrating critical digital literacy and ethical reflection on AI into initial teacher education programs, preparing future educators for an increasingly technological educational landscape that must remain focused on human and pedagogical development.

This study examines the transformative potential of Generative AI (GenAI) in teacher education within developing countries, focusing on Ghana, where challenges such as limited pedagogical modeling, performance-based assessments, and practitioner-expertise gaps hinder progress. GenAI has the capacity to address these issues by supporting content knowledge acquisition, a role that currently dominates teacher education programs. By taking on this foundational role, GenAI allows teacher educators to redirect their focus to other critical areas, including pedagogical modeling, authentic assessments, and fostering digital literacy and critical thinking. These roles are interconnected, creating a ripple effect where pre-service teachers (PSTs) are better equipped to enhance K-12 learning outcomes and align education with workforce needs. The study emphasizes that GenAI's roles are multifaceted, directly addressing resistance to change, improving resource accessibility, and supporting teacher professional development. However, it cautions against misuse, which could undermine critical thinking and creativity, essential skills nurtured through traditional teaching methods. To ensure responsible and effective integration, the study advocates a scaffolding approach to GenAI literacy. This includes educating PSTs on its supportive role, training them in ethical use and prompt engineering, and equipping them to critically assess AI-generated content for biases and validity. The study concludes by recommending empirical research to explore these roles further and develop practical steps for integrating GenAI into teacher education systems responsibly and effectively.

The main objective of this research is to explore how ESL pre-service teachers perceived ChatGPT, a generative AI tool for writing, and the factors shaping their perception of ChatGPT for writing skills in an educational context. The research aims to investigate ESL pre-service teachers’ awareness, acceptance, perception, and the interplay between these factors regarding ChatGPT for writing. A sequential explanatory research design was employed and utilized complete enumeration sampling. The sample consisted of 80 undergraduate students enrolled in the Teacher Education Department English majors were used to collect students’ engagement data including students’ perceptions and usage of the platform, and user experiences were examined using the data using a 5-point Likert scale. Also, an in-depth interview was conducted with ten (10) students based on those who got the highest and lowest scorers in the Quantitative phase to understand deeper experiences in using ChatGPT and to explore design opportunities for leveraging ChatGPT as a generative AI tool for writing in the field of ESL education. The findings emphasized the awareness and acceptance of ChatGPT in writing. However, there were limitations to consider. These challenges encompassed issues such as inaccurate automated responses and the possibility of plagiarism. Despite these limitations, the research findings demonstrate that ChatGPT is effective writing tool. As researchers who experienced the process of different writing styles using ChatGPT, researchers believe that its potential needs to be maximally utilized. We suggest its application across different subjects and disciplines to examine its strengths and weaknesses in depth thoroughly.

This study explored the integration of generative artificial intelligence (GenAI) in supporting pre-service teachers (PSTs) during their work-integrated learning placements, focusing on its role in lesson planning, teaching and WIL crisis resolution. Using the unified theory of acceptance and use of technology framework, the study investigated how AI literacy, self-efficacy and social influences affect PSTs’ acceptance and use of GenAI tools. Data collected from surveys and focus-group interviews with 126 PSTs reveals that GenAI enhances PSTs' efficiency, improves stress management and provides timely support in managing professional relationships. Results highlight differences in perceptions of GenAI across demographic groups, teaching subjects and school contexts. The findings emphasise raising awareness of GenAI’s potential in supporting PSTs, as well as the need for discipline-specific AI training in initial teacher education programmes to foster confident, ethical and effective application in placements.   Implications for practice or policy: Initial teacher education programmes should incorporate AI literacy and prompt engineering training, in combination with other educational technology tools and in alignment with specific disciplinary subjects. Schools and mentor teachers need training and preparation to support PSTs in integrating GenAI into work-integrated learning. Educational policy should address the disparities in access to GenAI tools, ensuring equitable opportunities for all PSTs.

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The emergence of artificial intelligence (AI) innovations like ChatGPT presents new opportunities and challenges for world languages (WL) education. WL teacher education programs must prepare preservice teachers with AI literacy to help them effectively integrate these technologies into teaching. This multiple case study—part of our on‐going self‐study of teacher educator practice—investigated how AI literacy can be leveraged as a core practice in a WL teacher licensure program. Drawing on pre‐surveys, course artifacts, structured reflections, and interviews, the study explored how three teacher candidates (TCs) engaged with Generative AI (chatbots) in the instructional activity of lesson planning and developed emergent forms of AI literacy. Participants demonstrated varying levels of development of AI literacy across four domains: technological proficiency, pedagogical compatibility, professional work, and ethical use. They developed critical stances toward AI, which were shaped by their evolving professional identities. This study contributes to growing conversations about AI in teacher education by showing the potential of the AI literacy core practice as a scaffolded, reflective approach to building AI competencies. It also underscores the importance of centering TC's professional identity development in AI integration while providing support for prompt design, noninstructional use of AI, and facilitating conversations about responsible AI use with students.

A primary medium through which culture is transmitted is the narrative ecology of early childhood education (ECE), involved in implicitly inculcating society in the young psyche via cultural norms and values. The ecology is prolifically endowed with archetypes of the oral traditions, local folklore, and the modern media, where the obstinate Bakri (goat) is usually masculinized and corrected about her wrath, and the beautiful Pari (fairy) is usually romanticized as silent and delicate. The inductive incorporation of Artificial Intelligence (AI) with a specific focus on generative AI, conditioned on these already existing collections of corpora, is not a dialectic of nonexistence, but rather a powerful lubricant to the mass production and strengthening of these damagingly held stereotypes. The paper supposes the idea that technical integration is not the pressing condition to apply AI in Pakistani ECE, but rather the strong framework based on critical AI literacy that should be developed among the teacher body, curriculum planners, and policy makers. Leaving the Western-centrist idea of AI bias, the study is based on the socio-cultural setting of Pakistan to first systematize the existing gender and behavioral stereotypes in a selection of trendy children's stories, cartoons, and textbooks. The paper extends to suggest a revolutionized pedagogical model of training ECE practitioners. In this way, the creation results in the innovative model of the decolonization of early -learning AI in Pakistan. The framework encourages community-driven redesign, inclusion of marginalized voices, regional languages, and challenges patriarchal norms for AI-driven empowerment.

With the educational application of big data and generative artificial intelligence (Gen AI) technology, people are paying more and more attention to improving the artificial intelligence literacy of rural teachers in China. Based on the Chinese "Teacher Digital Literacy" standard and the framework and indicators in the UNESCO "Teacher Artificial Intelligence Competency Framework", we have developed a model for rural teachers to assess their level of artificial intelligence, and based on this, we have developed a self-assessment tool. We have identified three challenges that constrain the use of AI technology among rural teachers in western China, namely technological factors, personal factors, and institutional factors. To address these challenges, we have designed a system framework for automated evaluation and adaptive training of rural teachers using AIGC technology. Within this framework, different scenarios such as teacher lesson preparation, smart teaching, academic evaluation, teaching research, and home school collaborative model training are targeted to promote the skill development of rural teachers in applying AI technology for innovative teaching, thereby improving the artificial intelligence literacy of rural teachers in China.

Introduction Highlighting the critical role of organizational context in Generative AI (GAI) integration, this study proposes a multilevel model to explore the interplay between individual teacher development and school-level factors. Specifically, we examine how teacher digital literacy and pedagogical leadership mediate the link between GAI adoption and deep integration, alongside the moderating role of school innovation climate. Methods Data from 512 teachers across 45 schools in China were analyzed using multilevel structural equation modeling (MSEM) and hierarchical linear modeling (HLM). This design allowed for the simultaneous assessment of sequential mediation pathways at the individual level and cross-level interactions involving organizational climate. Results Results confirmed a sequential mediation: GAI adoption enhances digital literacy, which boosts pedagogical leadership, ultimately driving deep integration. Crucially, school innovation climate functioned as a significant cross-level moderator (γ = 0.21, p < 0.01); the positive impact of pedagogical leadership on integration was markedly stronger in supportive environments. Discussion These findings demonstrate that GAI’s transformative potential relies on a synergy between individual competencies and a supportive organizational environment. The study suggests that to maximize technological efficacy, policymakers and leaders must prioritize cultivating innovation-supportive school climates alongside individual training.

Teacher education students play double role as present learners and future educators. Hence, they need targeted training to navigate the growing influence of Generative Artificial Intelligence (GenAI) on teaching, learning and professional identity. However, existing artificial intelligence (AI) literacy programmes predominantly emphasize technical AI knowledge and pre‐GenAI tools or are offered by GenAI platforms that focus on their own technologies' features, thereby lacking pedagogically structured frameworks that address non‐technical dimensions such as ethics, contextual understanding and the development of human‐centered, critical and lifelong learning mindsets to adapt to rapid technology changes. Furthermore, limitation of time in teacher education syllabus and faculty's lack of AI literacy require targeted intervention. This design‐based research (DBR) aims to fill this gap by developing and evaluating a set of design principles for GenAI literacy training in teacher education. Integrating contemporary AI competency frameworks for learners and teachers, the study implements these principles in a workshop that serves as the research prototype. The prototype was first piloted with 14 master's students and then evaluated with 29 teacher education (TE) students. Results show significant gains in participants' AI competence self‐efficacy, attitudes towards GenAI and commitment to critical, ethical and pedagogical engagement with GenAI tools. The findings highlight the need for teacher education programmes to integrate GenAI literacy that supports teachers' evolving roles as reflective practitioners, co‐creators and lifelong learners in an AI‐driven world. Students' self‐efficacy and mindset influence their adoption of GenAI in learning and teaching. GenAI competency is essential for teacher education students, encompassing technological, ethical and sociocultural dimensions. Implementing AI literacy training faces challenges such as educators' limited AI literacy, the lack of pedagogical frameworks and the need to integrate technical knowledge with ethical reasoning, self‐regulated learning and metacognitive skills. Transformative learning is crucial in preparing teacher education students as co‐creators and facilitators in AI‐enhanced education. This study presents the design and evaluation of an AI competency training programme to enhance teacher education students' self‐efficacy, critical and human‐centered AI mindsets and human–AI interaction skills. Using design‐based research, it identifies key principles for AI literacy training, demonstrating how transformative, active and experiential learning approaches can prepare teacher education students for evolving roles in AI‐enhanced education. Educational technologists can apply the proposed design principles to develop GenAI tools and AI systems that enhance AI competency and self‐regulated learning (SRL). Educators can integrate these AI training principles into their teaching strategies and enhance their task assignments and assessments to foster deep learning, self‐regulated learning and metacognitive skills in the age of GenAI. Policymakers should update teacher education curriculum to include AI competency, strengthen content and pedagogical knowledge, especially in social emotional learning, formative and authentic assessment methods and establish guidelines for selecting GenAI tools that support human intelligence and mitigate over‐reliance.

: This autoethnography explores the impact of generative artificial intelligence (GenAI) on teaching and research practices within the educational landscape. The author’s experiences with generative AI are examined through a dual-layered exploration, encompassing academic research and educational practices. The paper emphasizes the pivotal role of AI in revolutionizing classroom dynamics and alleviating the workload of educators and researchers. Ethical considerations surrounding the use of AI are critically examined, ensuring responsible research practices. The reflective journey reveals the extensive time dedicated to tasks outside the classroom, highlighting the impact of AI on the workload of educators and researchers. The paper calls for a shift in curriculum design to incorporate comprehensive digital and AI literacy training and emphasizes the necessity for future research to delve into effective pedagogical approaches and long-term impacts of AI integration. Overall, this autoethnographic methodology sheds light on the profound impact of AI on both teaching and research practices within the educational landscape.

This research investigated pre-service science teachers’ intention to use generative artificial intelligence (AI) in inquiry-based teaching using Azjen’s Theory of Planned Behavior. Quantitative data were collected by means of a survey that was administered to pre-service teachers enrolled for an undergraduate teacher education degree at a South African public university. Thereafter, through interviews, the study investigated how the pre-service teachers explained their intentions to use AI in inquiry-based teaching. The survey results showed that pre-service teachers’ AI literacy, subjective norm, attitude to the use of AI, perceived behavioral control, and perceived usefulness have significant effects on their intention to use AI for inquiry-based teaching. However, pre-service teachers’ concern about generative AI and their perceived skill readiness had no significant effect on their intention to use AI. The findings of the interviews that were presented as themes provided some corroboration of the results of the survey. In exploiting the use of AI to drive inquiry-based teaching, the findings of this study provide some insight into possible enablers and inhibitors in the use of AI for inquiry in the classroom. From a practical perspective, these findings could inform teacher educators on issues they may engage pre-service science teachers when discussing the uses of AI.

The advent of generative artificial intelligence (GAI) and the prominence of core literacy in senior high school have jointly promoted innovative transformations in teaching. This paper employs literature review methodology to first examine current problems in senior high school English reading class, subsequently proposing GAI’ s integrative advantages. Then further identifies implementation challenges and corresponding countermeasures. It is found that traditional English reading class has problems of single reading resources, lacking thinking training and one- sided reading evaluation system, while GAI can enrich reading materials, promote critical thinking, and achieve diversified evaluation. However, there are problems of lacking emotional interaction, teachers’ technical dependence and difficulties in changing teaching concepts. Therefore, this paper proposes a teacher-led, GAI-assisted emotional interaction mechanism, teacher-student-AI triadic collaboration, and progressive conceptual iteration strategies. The paper shows that the deep integration of GAI and English reading teaching needs to balance technological empowerment and traditional educational values.

This study investigates the integration of artificial intelligence (AI) tools into Russian language teaching within a hybrid educational model. It distinguishes AI from generative AI and outlines their analytical and content-production functions in language education. The literature suggests that AI enhances learner motivation through personalization, interaction, and automated feedback, yet studies focusing specifically on Russian language instruction are limited. Conducted at Van Yüzüncü Yıl University during the 2023–2024 and 2024–2025 academic years, this research employs qualitative observation to evaluate the effectiveness, benefits, and limitations of AI-supported Russian courses offered as a second foreign language. The eight-stage assessment indicates that AI use increases students’ curiosity, confidence, and participation, while cultural nuances remain difficult to fully convey through technology. The study concludes that effective implementation requires teacher AI literacy and careful tool selection, providing a methodological reference for future research

While artificial intelligence (AI) is transforming many sectors, its integration into pre-service teacher education in higher education remains limited. This study investigates the iterative development and effects of a concise, two-session educational intervention designed to foster AI literacy among pre-service physics teachers. Following a design-based research approach, the intervention was implemented in two iterations at the University of Cologne (n = 31 across two cohorts). Structured according to the 5E instructional model, the intervention required students to use generative AI tools as didactic instruments to create lesson plans and reflect on their usage. AI literacy was measured using a validated 30-item test, while attitudes toward AI were assessed via a 4-point Likert survey. Results indicate only small, non-significant increases in overall AI literacy, with selective gains observed in competencies explicitly supported by hands-on activities and targeted scaffolding. However, attitudinal measures demonstrated that even brief interventions can strengthen participants’ openness toward AI and their perceived preparedness to use AI tools in teaching. Additionally, the iterative comparison highlighted format-sensitive effects. These findings suggest that while short design-based interventions can selectively activate elements of AI literacy and foster professional confidence, they are insufficient for broader skill Acquisition. Consequently, more sustained, context-rich engagements are likely required to achieve comprehensive and durable AI literacy development in pre-service teacher education.

This paper explores the application of Generative AI in education, with a particular focus on its role in human-computer collaborative teaching. The study highlights the crucial contributions of Generative AI in areas such as instructional design, resource creation, assessment feedback, and the enhancement of personalized learning experiences. It also introduces various teaching models, including personalized learning, intelligent tutoring, interactive environments, and virtual integration. Additionally, the paper addresses the challenges encountered in implementing these models and offers corresponding solutions, such as technology adaptation, teacher literacy development, privacy protection, and the promotion of educational equity. Lastly, the paper discusses the future direction of education, emphasizing the importance of fostering AI literacy among both educators and students.

The present paper suggests an all-encompassing model of applying the Generative Artificial Intelligence in art education in the form of Hybrid Pedagogical Model of AI-Art Learning (HPM-AI). The model reformulates creativity as a collaborative activity between the human intent and algorithmic production, focusing on reflective learning, ethical consciousness and open participation. The analysis of the study brings together the cognitive theory, computational modeling, as well as case-based validation, which is why the study proves that generative AI can improve the diversity of creativity but without cultural and moral responsibility. Three contexts of implementation that included an AI-based design studio, a course on digital heritage restoration, and community-based art workshops were evaluated to determine the flexibility of the framework and its effect on pedagogy. Quantitative visualizations and qualitative thoughts show that HPM-AI contributes to different educational goals in settings: the conceptual innovation of studio learning, the cultural integrity of the practice of restoration, and the inclusive creative empowerment of the learning environment. The paper also discusses implications on authorship, data ethics, and psychology of the learner, that AI should not be viewed as a substitute to artistic skill, and instead it is an intelligent companion, which reflects and magnifies human imagination. The paper concludes, that the ethical design of generative AI in creative education, interdisciplinary faculty development and institutional transparency are the necessary conditions of sustainable integration. HPM-AI framework, therefore, promotes an image of symbiotic creativity, placing art pedagogy on a crossroad of human cognition in relation to cultural continuity in connection with the computational intelligence.

This paper provides a systematic deconstruction of the education-focused provisions within China's 2025 State Council directive, the "AI+" Action (Guo Fa No. 11). It introduces and develops the theoretical framework of "state-engineered pedagogy"—the deliberate, top-down use of technology by a state to systematically reshape teaching and learning processes to align with national economic and strategic objectives. Utilizing a methodology of policy text analysis, comparative policy mapping, and illustrative case studies substantiated with independent evidence, this paper argues that China's strategy aims to construct a new human-machine collaborative learning paradigm with a dual purpose: to drive domestic educational reform and to cultivate a future workforce capable of securing long-term technological self-reliance and shaping global standards. Through a multi-layered analysis of the policy's architecture, historical evolution, international context, and ethical risks, this paper contends that while the "AI+" mandate promises unprecedented personalization, it also introduces profound risks related to data privacy, algorithmic governance, and the creation of an "algorithmic panopticon." The execution of this policy thus serves as a critical case study in 21st-century statecraft and the global contest over the future of technology and society.

This paper explores the paradigm reconstruction of interpreting pedagogy driven by generative AI technology. With the breakthroughs of AI technologies such as ChatGPT in natural language processing, traditional interpreting education faces dual challenges of technological substitution and pedagogical transformation. Based on Kuhn’s paradigm theory, the study analyzes the limitations of three traditional interpreting teaching paradigms, language-centric, knowledge-based, and skill-acquisition-oriented, and proposes a novel “teacher-AI-learner” triadic collaborative paradigm. Through reconstructing teaching subjects, environments, and curriculum systems, the integration of real-time translation tools and intelligent terminology databases facilitates the transition from static skill training to dynamic human-machine collaboration. The research simultaneously highlights challenges in technological ethics and curriculum design transformation pressures, emphasizing the necessity to balance technological empowerment with humanistic education.

For today's Net generation (especially the Alpha generation born between 2010 and 2024) and all subsequent generations, it will be imperative to redefine educational strategies in the context of rapid technological and societal change. It is necessary to consider how to proceed, as the educational challenge must be placed in a broader philosophical and cultural context, with an emphasis on the ever-rapidly evolving technology as well as the nature of knowledge and human experience. Based on the paradigm of the shift from Web 2.0 to Web 4.0 and the consequent shift from Education 4.0 to Education 5.0, potential guidelines for the development of modern education 4.0 based on digital pedagogy that combines personalized learning, real-time feedback and collaborative, interdisciplinary environments in Education 5.0 are indicated. This reflection will place particular emphasis on the role of teachers as mentors and not merely transmitters of information, as well as on the ethical, social and emotional dimensions of digital learning, and emphasize the importance of adapting educational practices to real-life contexts and future humanistic challenges of education (Flogie et al., 2025).

This article examines the pedagogical transformations emerging in architectural education through a conceptual and critical perspective focused on human–AI co-creativity. Co-creativity specifically refers to collaborations between human designers and artificial intelligence, in contrast to broader notions of collaborative creativity. The paper argues that AI functions not merely as a technical instrument, but as a co-creative partner that reshapes studio culture, authorship, and creative work. Drawing on selected studio-based cases, the study explores how AI-supported workflows influence ideation, representation, critique culture, prompt literacy, and ethical reasoning. Thematically, it engages with concepts such as cognitive augmentation and conceptual ambiguity to demonstrate how design pedagogy is evolving in response to intelligent systems. Rather than viewing AI as a generative tool alone, the article positions it as an epistemic and ethical agent that prompts a rethinking of studio environments as cultural and pedagogical spaces. Methodologically, the study adopts a case-based approach, analysing selected 16 design studios in which AI was integrated into early-stage ideation, feedback sessions, and conceptual development. These cases extent strategies from prompt-driven speculation to hybrid critique practices, revealing a dynamic landscape of experimentation and adaptation. The findings suggest that AI can foster deeper conceptual inquiry, student reflection, and new modalities of authorship and collaboration. Eventually, the study underscores the need for reflexive pedagogical frameworks that integrate AI meaningfully enhancing, rather than displacing, human creativity.

This study investigates how deep learning–based artificial intelligence (AI) can effectively support hybrid learning in English for Business courses. In response to rapid technological change in higher education, the research explores how digital tools can complement human teaching and learning. Using a qualitative case study design, data were collected from 10 lecturers and 15 students through interviews, classroom observations, and document analysis. The findings reveal that AI-enabled features, such as personalized learning paths, instant feedback, and gamified activities, enhanced students’ motivation, supported self-paced learning, and improved awareness of individual strengths and weaknesses. However, participants also reported challenges, including limited digital literacy, unstable internet access, and difficulty managing multiple platforms. Importantly, both lecturers and students emphasized that human interaction remains crucial, with real-time discussion, collaborative activities, and instructor guidance playing key roles in developing confidence and business communication skills. The study concludes that successful AI-enhanced hybrid learning requires not only technological innovation but also strong institutional support and thoughtful pedagogical practice. AI should be viewed as a complementary tool rather than a replacement for the human connections essential to meaningful language learning.

Artificial Intelligence (AI) is increasingly transforming vocational undergraduate education, reshaping how English is taught, learned, and assessed. English instruction in this context faces the dual task of developing students’ linguistic competence and learning capacity in digital literacy. Integrating AI offers opportunities for personalized learning, adaptive assessment, and immersive language practice, yet also poses challenges in pedagogy, technology, and ethics. This paper examines AI-enhanced English teaching, drawing on recent research and case studies. Key affordances include adaptive learning systems, automated feedback, and vocationally relevant language practice, while barriers involve limited digital infrastructure, teachers’ AI literacy, and concerns over data privacy and equity. To address these issues, a human–AI collaborative framework is proposed, emphasizing competence-oriented, task-based, and ethically guided teaching. Findings highlight that effective AI integration requires rethinking the pedagogical ecosystem, with teachers evolving from knowledge transmitters to learning facilitators and AI collaborators. Sustainable implementation depends on coordinated teacher training, curriculum redesign, ethical governance, and institutional support. This study contributes to the discourse on digital transformation in vocational education and offers practical strategies for optimizing AI-assisted English instruction under the "smart vocational education" framework.

Artificial Intelligence (AI) is transforming education by enabling intelligent tutoring systems (ITS) to provide personalized learning experiences. This paper proposes a new paradigm of Human-AI Co-Creative Intelligent Tutoring Systems, where AI functions as a collaborative partner with teachers and students in the learning process. We present a theoretical framework integrating cognitive science, pedagogy, and human-computer interaction (HCI) principles to model how AI can augment human creativity and problem-solving. A conceptual model and technical architecture are introduced in an ITS to support co-creation. We also outline practical strategies for implementing co-creative tutoring, including task allocation between humans and AI based on Bloom's Taxonomy,multi-modal interaction design,and guidelines for teacher-AI collaboration. The proposed approach aims to leverage AI's strengths in data processing and content generation alongside human strengths in critical thinking and emotional intelligence,creating a synergistic learning environment. The findings underscore the importance of balancing human and machine roles in education,with AI acting as an enabler rather than a replacement for human educators.

This review article examines the integration of human-machine teaming principles into college English speaking classroom design with the specific objective of enhancing students' willingness to communicate. As educational technology continues to evolve, the convergence of human expertise and artificial intelligence capabilities presents unprecedented opportunities for language learning pedagogy. This comprehensive review synthesizes current research across multiple domains including second language acquisition theory, educational technology, human-computer interaction, and artificial intelligence to propose a novel framework for classroom design. Through systematic analysis, the research identifies key HMT components that directly impact WTC: adaptive feedback mechanisms, empathetic AI interactions, collaborative task design, and personalized learning environments. The findings indicate that well-designed human-machine partnerships can significantly reduce speaking anxiety, increase learner autonomy, and enhance communicative competence. The review proposes a multi-layered theoretical framework that positions educators as orchestrators of human-AI collaboration rather than sole content deliverers, while AI systems serve as adaptive learning partners providing real-time feedback, conversation practice, and anxiety-reducing interventions. Key recommendations include implementing transparent AI systems that build trust, designing collaborative speaking tasks that leverage both human creativity and AI analytical capabilities, and developing teacher training programs for effective HMT integration. This work contributes to the growing body of knowledge on AI-enhanced language education and provides practical guidelines for educators seeking to modernize speaking instruction through human-machine collaboration.

As Artificial intelligence (AI) continues to transform industries and redefine professional roles, integrating AI competence development into education has become a strategic priority. This exploratory study implements a LLM-based Delphi methodology to identify essential AI competencies, examine barriers to AI integration in academic settings, and develop actionable strategies for competency development in higher education. The research process employed large language models (LLMs) to conduct a simulated exploration with inductive thematic analysis of interdisciplinary perspectives, prioritize critical themes through iterative rating cycles, and resolve polarization via structured deliberation of disputed concepts. The key outputs include the development of a consensus framework outlining universal AI literacy standards, human-AI collaborative pedagogy models, equity-centered implementation protocols, and ethical guardrails for responsible adoption, together with a toolkit with practical guidelines to operationalize the consensus findings. The study aims to assess AI's potential as a collaborative agent in educational design and to evaluate to what extent an AI-generated framework meets established OECD criteria for quality and robustness. This report addresses three primary audiences: curriculum designers developing AI competency models, institutional leaders implementing equity-focused AI policies, and researchers examining pedagogical impacts of human-AI collaboration.

Since the advent of the postdigital era, technologies have been dramatically transforming human lives, shifting the ways humans communicate, learn, and create. This article aims to envision how entanglements with nonhuman intelligences can unsettle and reshape pedagogical approaches in the field of art education. I argue for the need to reconceptualize relations between humans and artificial intelligence (AI) as collaborative and symbiotic relationships beyond instrumental understandings, which are informed by posthuman perspectives. Reframing human–AI relations will help art educators and researchers not only explore the potential of AI for new inquiry and creativity in art curricula, but also critically examine possible ethical issues for its implementation in art classrooms. By looking at how AI has been explored in new media art projects that intersect emergent intelligences and art pedagogy, I investigate their implications for future art education.

At present, artificial intelligence(AI) technology has penetrated into all aspects of the education field, and the teaching system has been reformed. In the background of AI, deep learning, personalized learning and human-computer collaborative learning have gradually become the mainstream learning methods. The change of learning style directly promotes the transformation of curriculum and teaching paradigm. This paper analyzes the current situation of the use of AI technology in the teaching mode of psychology and Pedagogy in Colleges and universities, and puts forward the research on the teaching mode of psychology and Pedagogy in Colleges and Universities Based on AI technology. In this paper, 290 teachers and students were investigated and analyzed, and their recognition of AI technology reached more than 90%. This paper makes a comparative analysis on the satisfactory learning environment, learning methods and learning effect after the use of AI in psychology teaching, and discusses and analyzes the results. It also puts forward the use suggestions of AI in psychology teaching, which provides guarantee for the development of intelligent teaching system. The research in this paper is of great significance for the further integration and development of the two.

The emergence of generative AI, particularly large language models (LLMs), has opened the door for student-centered and active learning methods like project-based learning (PBL). However, PBL poses practical implementation challenges for educators around project design and management, assessment, and balancing student guidance with student autonomy. The following research documents a co-design process with interdisciplinary K-12 teachers to explore and address the current PBL challenges they face. Through teacher-driven interviews, collaborative workshops, and iterative design of wireframes, we gathered evidence for ways LLMs can support teachers in implementing high-quality PBL pedagogy by automating routine tasks and enhancing personalized learning. Teachers in the study advocated for supporting their professional growth and augmenting their current roles without replacing them. They also identified affordances and challenges around classroom integration, including resource requirements and constraints, ethical concerns, and potential immediate and long-term impacts. Drawing on these, we propose design guidelines for future deployment of LLM tools in PBL.

This paper explores the transformative potential of artificial intelligence (AI) in reshaping kindergarten teaching practices through a socio-technical systems lens. Drawing on Vygotsky's sociocultural theory and the SAMR model, it critiques the limitations of existing technology integration frameworks in early childhood education. By synthesizing 127 empirical studies and 35 conceptual papers, the research identifies three critical dimensions of AI implementation: personalized cognitive scaffolding, adaptive assessment systems, and human-AI collaborative pedagogy. The proposed AI-Enhanced Early Learning (AI-EL) model introduces dynamic feedback loops between technological affordances and developmental milestones, addressing gaps in constructivist theories. Findings highlight how AI tools can foster metacognitive skills in 3 – 6-year-olds through interactive storytelling and gamified assessments while redefining teacher roles as learning orchestrators. Ethical considerations emphasize the need for algorithmic transparency and culturally responsive design to avoid exacerbating educational inequalities. This theoretical contribution advances the discourse on human-technology symbiosis in foundational education, providing a heuristic framework for future empirical investigations.

Against the backdrop of rapid AIGC (AI-Generated Content) technological development and the intelligent transformation of higher education, design education is undergoing profound structural changes. Traditional courses like Ethnic Cultural Creative Design have long been constrained by dilemmas such as "superficial cultural understanding," "inefficient creative generation," "lagging technology application," and "simplified evaluation systems," making it difficult to meet the cultural and creative industry's demand for interdisciplinary talent. Based on the latest research in educational technology, higher education pedagogy, and theories of educational change, this paper constructs a three-dimensional "culture-technology-design" integrated curriculum reform framework. It proposes a human-AI collaborative teaching model with AIGC at its core and develops an actionable practical teaching path within real classroom settings. The research indicates that AIGC, serving as a "creative partner," can significantly enhance students' depth of cultural decoding, efficiency of idea generation, and quality of technology application. The "dual-mentor guidance + AI collaboration" model facilitates learners' identity shift from "technology users" to "human-AI co-creative designers." The construction of a multi-dimensional evaluation system enables systematic assessment of the learning process, cultural value, and degree of technological integration. This reform practice holds significant implications for the paradigm shift in higher education design programs, the sustainable development of culture, and the construction of smart education.

This essay aims to analyze how artificial intelligence (AI) is transforming education by providing tools that simulate human cognitive processes, enabling the personalization of content, the optimization of tasks, and support for pedagogical decision-making. Emerging models such as the Digital Pedagogy for Sustainable Educational Transformation, AI-Based Cognitive Education, Transformative Pedagogy with AI, and Student-Centered Learning are examined, as they enhance adaptive and collaborative learning experiences. Furthermore, the essay critically reflects on the challenges posed by AI, such as the risk of limiting students’ autonomy and reflective capacity if algorithmic logic predominates. Therefore, it is argued that the teacher’s role must evolve into that of mediator and pedagogical guide, balancing technological innovation with humanistic principles to ensure that education remains comprehensive, ethical, and centered on the holistic development of the student.

ABSTRACT This special issue explores the transformative impact of artificial intelligence (AI) on translation and interpreting education. While translation pedagogy has long been rooted in humanistic traditions, the rise of generative AI calls for new approaches that integrate technological efficiency without compromising creativity, ethics, and cultural nuance. The contributions comprising this special issue span four thematic areas: Human-Centered AI and Ethical Literacy in Translation Education, AI as a Feedback Source in Translation and Interpreting, AI Applications in Post-Editing Training, and AI-enhanced Pedagogy in Interpreting. Through empirical studies, case analyses and theoretical reflections, the articles demonstrate how AI can support translation training – from automating assessments and enhancing cognitive engagement to fostering ethical awareness and critical thinking. This issue highlights the need for a pedagogical paradigm shift: one that positions AI as a collaborative tool while reaffirming the translator’s human agency and interpretive judgement in the digital age.

This paper explores the evolving role of Artificial Intelligence (AI) in primary education, highlighting its potential to personalise learning, alleviate teacher workload and enhance student outcomes. It critically examines the integration of AI technologies, ranging from intelligent tutoring systems to voice-activated assistants, and their implications for pedagogy, student wellbeing and digital literacy. While acknowledging AI’s transformative capabilities, the paper similarly addresses ethical concerns, including data privacy, bias and overreliance on automated systems. Drawing on current research, educational policy and practical examples, it advocates for the responsible adoption of AI guided by informed teacher judgement and robust digital literacy education. Ultimately, it calls for collaborative efforts among educators, technologists and policymakers to ensure AI enriches learning without compromising human values or professional integrity.

The pre-pandemic era saw the integration of educational technology into traditional classroom pedagogy as a supplementary tool, enhancing hands-on and interactive teaching approaches. While there was a general acknowledgment that information technology was transforming learning, its centrality in education was not fully realized until the COVID-19 pandemic. During the pandemic, the physical presence of learners was replaced by virtual engagement, compelling both educators and students to adapt to a new learning paradigm. This shift necessitated the development of adaptive teaching strategies to preserve the interactive, collaborative and inclusive nature of conventional classrooms while elevating the role of educational technology from a peripheral to a central position. In the post-pandemic world, educational practices are divided between those who favor a return to traditional, human-centered models and those who advocate AI-integrated, technology-driven learning. This study examines the teaching of language and linguistics before, during, and after the pandemic, focusing on approaches in Oman and the United Arab Emirates (UAE).

Receiving timely and personalized feedback is essential for second-language learners, especially when human instructors are unavailable. This study explores the effectiveness of Large Language Models (LLMs), including both proprietary and open-source models, for Automated Essay Scoring (AES). Through extensive experiments with public and private datasets, we find that while LLMs do not surpass conventional state-of-the-art (SOTA) grading models in performance, they exhibit notable consistency, generalizability, and explainability. We propose an open-source LLM-based AES system, inspired by the dual-process theory. Our system offers accurate grading and high-quality feedback, at least comparable to that of fine-tuned proprietary LLMs, in addition to its ability to alleviate misgrading. Furthermore, we conduct human-AI co-grading experiments with both novice and expert graders. We find that our system not only automates the grading process but also enhances the performance and efficiency of human graders, particularly for essays where the model has lower confidence. These results highlight the potential of LLMs to facilitate effective human-AI collaboration in the educational context, potentially transforming learning experiences through AI-generated feedback.

The role of Artificial Intelligence (AI) in education is undergoing a rapid transformation, moving beyond its historical function as an instructional tool towards a new potential as an active participant in the learning process. This shift is driven by the emergence of agentic AI, autonomous systems capable of proactive, goal-directed action. However, the field lacks a robust conceptual framework to understand, design, and evaluate this new paradigm of human-AI interaction in learning. This paper addresses this gap by proposing a novel conceptual framework (the APCP framework) that charts the transition from AI as a tool to AI as a collaborative partner. We present a four-level model of escalating AI agency within human-AI collaborative learning: (1) the AI as an Adaptive Instrument, (2) the AI as a Proactive Assistant, (3) the AI as a Co-Learner, and (4) the AI as a Peer Collaborator. Grounded in sociocultural theories of learning and Computer-Supported Collaborative Learning (CSCL), this framework provides a structured vocabulary for analysing the shifting roles and responsibilities between human and AI agents. The paper further engages in a critical discussion of the philosophical underpinnings of collaboration, examining whether an AI, lacking genuine consciousness or shared intentionality, can be considered a true collaborator. We conclude that while AI may not achieve authentic phenomenological partnership, it can be designed as a highly effective functional collaborator. This distinction has significant implications for pedagogy, instructional design, and the future research agenda for AI in education, urging a shift in focus towards creating learning environments that harness the complementary strengths of both human and AI.

As artificial intelligence becomes increasingly integrated into professional and personal domains, traditional metrics of human intelligence require reconceptualization. This paper introduces the Artificial Intelligence Quotient (AIQ), a novel measurement framework designed to assess an individual's capacity to effectively collaborate with and leverage AI systems, particularly Large Language Models (LLMs). Building upon established cognitive assessment methodologies and contemporary AI interaction research, we present a comprehensive framework for quantifying human-AI collaborative intelligence. This work addresses the growing need for standardized evaluation of AI-augmented cognitive capabilities in educational and professional contexts.

Amidst the profound reconstruction of the educational ecosystem by digital-intelligent technologies, teachers face dual challenges of role transition pains and technological adaptation crises. This study focuses on the core issue of "teachers' role adaptation and pedagogical innovation in human-AI collaborative teaching," integrating Social-Technical Systems (STS) theory with an ecological model of teacher professional development to reveal teachers' irreplaceable value as instructional designers, emotional connectors, and ethical guardians. Key findings include: Three predominant scenarios of human-AI collaborative teaching have emerged-intelligent diagnosis, virtual-physical inquiry, and generative collaboration, yet three critical adaptation gaps persist among teachers: weak technological integration capabilities, role identity anxiety, and deficient algorithmic ethics judgment; Fundamental conflicts stem from the tension between technological efficiency orientation and educational process values, manifested through AI's compression of student trial-and-error space and tool fragmentation undermining holistic education; Accordingly, a "Three-Phase Five-Dimension" collaborative model is proposed, adopting dynamic equilibrium principles to allocate responsibilities (AI handles standardized tasks while teachers lead value-rational domains) with embedded ethical review mechanisms; Teacher adaptation pathways are suggested: developing technological integration and interdisciplinary design capabilities at individual level; innovating virtual teaching communities and competition-incubation mechanisms at organizational level; and creating teacher-friendly interfaces at technological level. The study concludes that human-AI collaboration must center on teacher agency, advocating future trustworthy AI educational infrastructure and teacher ethical certification to build a "Humanities as Essence, Technology as Utility" educational ecosystem.

The integration of artificial intelligence (AI) in education highlights the growing need for AI literacy among K–12 teachers, particularly to enable effective human–machine cooperation. This study investigates Saudi K–12 educators’ AI literacy competencies across four key dimensions: awareness, ethics, evaluation, and use. Using a survey of 426 teachers and analyzing the data through descriptive statistics and structural equation modeling (SEM), this study found high overall literacy levels, with ethics scoring the highest and use slightly lower, indicating a modest gap between knowledge and application. The SEM results indicated that awareness significantly influenced ethics, evaluation, and use, positioning it as a foundational competency. Ethics also strongly predicted both evaluation and use, while evaluation contributed positively to use. These findings underscore AI literacy skills’ interconnected nature and point to the importance of integrating ethical reasoning and critical evaluation into teacher training. This study provides evidence-based guidance for educational policymakers and leaders in designing professional development programs that prepare teachers for effective and responsible AI integration in K–12 education.

Objectives This study aimed to analyze the current status of AI competency among nursing students in a teacher training program and propose practical strategies to effectively integrate AI-based education into the curriculum. Methods A survey and focus group interviews were conducted with nursing students at a university in South Korea. The survey examined AI concept understanding, human-AI relationship perception, application competency, and AI ethics awareness. Focus group interviews explored curriculum design, instruction, and assessment improvements for AI integration. Results Students highly recognized the need for AI and digital technology education within the teacher training curriculum. They emphasized the importance of project- and practice-based learning, AI simulation tools, blended learning, and team-based problem-solving. Additionally, they expressed the need to integrate AI education into existing courses, apply performance-based assessments, and include AI competency evaluation in teaching practicums. Conclusions To enhance AI competency, expanding practice-based learning, systematically integrating AI content, and establishing performance-focused assessments are essential. This study presents specific improvement directions and highlights the need for broader follow-up research to build a sustainable, AI-integrated teacher education model.

The present study explored the role of teacher attitudes towards artificial intelligence as a mediator on the relationship between ICT competency and innovative instructional practices. Utilizing a cross-sectional survey design, a proposed conceptual model was examined. Data were obtained from 593 pre-service teachers enrolled in three teacher education programs in Egypt. Structural equation modeling (SEM) was employed to analyze the structural relationships among the variables. The findings are crucial for pre-service teacher education, offering insights for curriculum designers and policymakers aiming to enhance teacher preparedness for future classrooms, because attitudes towards AI, ICT competencies, and innovative instructional practices can be cultivated through learning experiences. Prior to initiating their professions, professional learning activities may be designed to pre-service teachers, emphasizing ICT competency and attitudes towards AI to indirectly enhance their innovative practices.

Objectives The purpose of this study is to assess the level of AI and digital competencies among vocational high school teachers and to analyze their educational needs in order to propose effective support strategies for enhancing teacher competencies. Methods A survey was conducted with 62 vocational high school teachers who participated in the 2024 Big Data and AI Education Training Program. To assess AI and digital competency levels and educational needs, descriptive statistics, Borich Needs Assessment, and the Locus for Focus model were used. Results The results revealed that vocational high school teachers perceived the importance of all AI and digital competency items to be higher than their current competency levels, indicating a strong awareness of the significance of these technologies despite limited actual proficiency. Among the competencies, the highest educational need was identified in the area of ethics and security, followed by professional development, assessment and reflection, and teaching and learning competencies. Conclusions The findings highlight the need for policy-level support and the development of systematic training programs to enhance AI and digital competencies among vocational high school teachers. Priority should be given to strengthening competencies related to data anonymization and secure management processes, establishing ethical standards through targeted training, providing AI-based personalized learning feedback, and improving the effective integration of digital technologies into teaching practices.

Amid calls to redefine core teacher competencies in the era of generativeAI, this study aimed to diagnose in-service early childhood teachers’ AI competency levels and educational needs and identify the core components of a professional development (PD) program to strengthen those competencies. Building on prior research, a survey instrument was developed and administered to 180 in-service early childhood teachers; descriptive statistics and reliability indices (Cronbach’s α) were calculated. Open-ended responses were analyzed using content analysis, and three focus group interviews (FGIs) were conducted with 18 teachers (≥5 years of experience) to derive in-depth qualitative insights. Findings indicated relatively high levels ofAI conceptual understanding, human-AI relationship perception, and AI ethics awareness, but a lower level of AI utilization competency, revealing a gap between awareness and classroom practice. Synthesizing the open-ended and FGI results, the proposed PD should adopt an integrated design (embedding AI within the existing cycle of observation-analysis-lesson planning-assessment), experiential/project-based blended delivery, and performance-based assessment (portfolio, product demonstration, and classroom application). The recommended content triad comprises ethics/privacy/copyright, AI-based instructional material development, and child developmentdata analysis. In addition, standard guidelines, verification checklists, post-training coaching, practitioner communities, and periodic updates, supported by institutional infrastructure and time allocation, are essential for sustainable scaling. By proposingan evidence-based, modular PD framework — Foundations (concepts/ethics) - Practice (materials/data) - Application (play/assessment) — this study provides baselineevidence to support safe and sustainable AI use in early childhood education

In exploring the collaborative engagement of AI in envisioning the future through Art Education, a critical focus emerges on the role of preservice teacher education. This inquiry underscores the necessity of equipping educators with the essential skills and resources to integrate AI-driven content creation, thereby nurturing students' futuristic imagination. To empower educators in this endeavor, it is imperative to provide structured curriculum frameworks and technical proficiency in utilizing AI software tools. By adeptly training preservice teachers in leveraging AI resources, it is necessary to catalyze a new wave of aesthetic innovation transcending the digital era. The competency of educators in integrating AI will significantly shape the capacity of the next generation to innovate and co-create with this technology. Hence, there is a pressing need to invest in preservice training and foster a culture of AI experimentation within educational environments. There exist issues of bias, accuracy, ethics, and safety when utilizing AI, and these also need to be addressed in the classroom space. This paper aims to delve into instructional models for seamlessly integrating AI into preservice training. It will deliberate on strategies for incorporating AI tools to redefine the trajectory of art and design education. Through thoughtful exploration and implementation, teachers can harness the transformative potential of AI to inspire creativity and drive innovation in the realm of Art Education.

Artificial intelligence (AI) is transforming education, making AI literacy a vital competency for teachers. Defined across four dimensions of awareness, usage, evaluation, and ethics, AI literacy enables educators to integrate technology effectively while upholding ethical standards. Although robust instruments exist internationally, Malaysia lacks culturally and linguistically relevant tools for assessing pre-service teachers’ AI literacy. This study adapted and validated the Artificial Intelligence Literacy Scale (AILS) developed by Wang et al. (2023) for use in northern Malaysia. Using back-to-back translation, expert review, and quantitative survey methods with 385 pre-service teachers, the scale underwent face and content validity testing followed by reliability analysis. Results confirmed strong face validity, unanimous content validity (S-CVI = 1.00), and high internal consistency (α = 0.933 overall). The validated scale provides an essential diagnostic tool for teacher education, supporting curriculum design, research, and policy development aimed at cultivating future-ready educators.

No abstract available

The rapid evolution of cloud-native technologies demands agile pedagogical approaches to cultivate engineering competencies. However, current education struggles with cognitive overload caused by tool abstractions, outdated curricula, and ineffective skill transfer. To bridge these gaps, this paper introduces AI-Augmented Pedagogy (AIAP), an innovative teacher-AI collaborative approach integrating outcome-based education (OBE) with AI engines. Its core contribution is a teacher-driven optimization loop which empowers teachers to leverage AI engines to: (1) align curriculum with industry demands in real time; (2) generate authentic scenario-based experiments contextualizing theoretical knowledge; (3) structure adaptive visual knowledge graphs that decompose technical complexity; and (4) identify multidimensional skill gaps using AI-powered assessments. Validation in cloud-native courses demonstrated AIAP's tangible efficacy. Teachers dynamically aligned curricula with innovations in the cloud-native ecosystem and cultivated students’ essential abilities to design, deploy, diagnose, and optimize, resulting in a significant improvement in high-score rates. This establishes a reusable paradigm where teachers, guided by AI, convert technical abstractions into diagnosable scenarios, enabling effective skill transfer in evolving technical domains.

The latest developments in artificial intelligence (AI) have paved the way for new AI-integrated pedagogical skills and competencies in language teaching. AI literacy and AI competency are related terms; however, they differ in educational contexts. AI literacy refers to the skills to understand and evaluate AI technologies, as well as to engage with them. In contrast, AI competency extends beyond conceptual proficiency and is related to the practical application of AI knowledge in educational domains. Considering the significance of effective and ethical AI use in language teaching, this study focused on the role of AI literacy training in teacher education programs and aimed to investigate pre-service EFL teachers’ perceptions of their AI literacy and competency as well as their experiences regarding AI literacy training in EFL teacher education programs. A small-scale exploratory approach was adopted in this qualitative study, which was conducted at the English Language Teaching Department at a state university in Türkiye. The participants were 15 pre-service EFL teachers. Semi-structured interviews were utilized to gather qualitative data, which was analyzed using thematic analysis. The study results revealed that pre-service EFL teachers’ perceptions of AI use in language teaching were positive; however, they were unconfident about their AI knowledge and skills. Also, the study enlightened a lack of exposure to AI education, including AI knowledge, practices, and ethical issues in EFL teacher education programs. Based on pre-service EFL teachers’ needs and expectations, the results of this study may provide beneficial insights for teacher educators, practitioners, and policymakers.

Generative artificial intelligence (AI) tools are rapidly transforming mathematics education by enabling automated problem generation, dynamic visualizations, and adaptive learning experiences. This study presents an empirical study on incorporating MATH41 into a 15-week pre-service teacher preparation course for mathematics majors. Twenty-four participants learned to create parameterized math problems, generate vector graphics. Following guided training, each participant designed and micro-taught a 20-minute lesson incorporating MATH41-generated resources. Reflection journals and final lesson plans were analyzed thematically, while 20 participants completed a self-assessment survey on lesson design competency. Results reveal that the automated problem generation capabilities motivated pre-service teachers to explore a broader range of instructional strategies, including personalized tasks and diverse problem variants. Reflection data indicate that while integrating AI tools can significantly boost confidence and creativity in lesson planning, careful pedagogical alignment remains essential to avoid superficial learning. Participants underscored the importance of maintaining teacher oversight—especially when adapting AI-generated problems for particular learner needs. Additionally, their post-course self-assessments showed high confidence in digital tool integration, yet they acknowledged that anticipating student misconceptions requires further field experience. Overall, this study contributes to understanding how teacher education programs can enhance lesson design competencies via structured AI tool integration. It also highlights the critical role of reflective practice in ensuring that automated content creation fosters deeper instructional effectiveness rather than uncritical AI dependency.

Teachers, as educational personnel, are expected to enhance their competence in mastering information technology, which can later be utilized in classroom learning activities. However, in reality, teachers' knowledge and skills in mastering technology to support teaching and learning activities are still limited. Based on a survey conducted, teachers at SD Indriyasana, Baleendah, are eager to gain knowledge and skills in using open-source applications, such as Chat GPT, Gemini AI, and MajickPen AI, to support their tasks, both in teaching and administrative duties. Therefore, training and mentoring are needed in learning the use of these open-source applications to support teachers' activities. The aim of this training is to introduce and train teachers at SD Indriyasana, Baleendah, in using open-source applications based on artificial intelligence (AI), such as Chat GPT, Gemini AI, and MajickPen AI, to support teaching and administrative tasks. The method used involves training, direct practice by teachers, guided by speakers and instructors using AI-based open-source applications, namely ChatGPT, Gemini AI, and MajickPen. The implementation method includes the following steps: 1) user needs analysis, 2) literature study, 3) training module development, 4) module testing, 5) training implementation, 6) evaluation. The training results indicate that teachers acquired knowledge and practical experience in line with their needs, enabling them to use AI-based applications (Chat GPT, Gemini AI, and MajickPen AI) to support learning and administrative activities. It can be said that the training went well, as shown by the questionnaire results: 1) alignment of the training program with objectives (94%), 2) alignment of the program with partners' needs (71%), 3) adequacy of program implementation time (65%), 4) team’s ability to execute the program (88%), and 5) program sustainability (77%). This training activity is beneficial for both teachers and students because it can make learning more interesting and enjoyable for students. By utilizing AI-based applications, teachers can generate ideas for learning activities, create quizzes, or provide relevant and up-to-date teaching materials, thereby facilitating class preparation and easing teachers' administrative tasks.

With the rapid development of Artificial Intelligence (AI) technology, the traditional role of teachers in vocational colleges and universities is undergoing profound changes. Combining the theory of educational technology integration and the theory of personalized learning, this study systematically explores the transformation of teachers' roles, including from knowledge transmitters to learning facilitators, from teaching evaluators to data analysts, from curriculum designers to technology integrators, and from classroom managers to learning community builders. Through literature analysis and case study methodology, the key competency requirements of teachers in these new roles are analyzed in depth and specific competency enhancement strategies are identified. These strategies include systematic theoretical learning, technology application practice, reflective improvement, and peer communication and sharing, aiming to help teachers effectively improve their teaching quality and professional competence while coping with the challenges of AI technology. The results of the study show that through the implementation of rational strategies, teachers are not only better able to adapt to the new technological environment, but also play a greater role in the sustainable development of vocational education.

Digital transformation in education has driven the integration of coding and artificial intelligence (AI) into learning at the primary and secondary levels. This integration presents complex ethical challenges, particularly in ensuring the teacher's role as a moral guide in technology-based instruction. This study aims to analyze the role of teacher professional ethics in coding and AI education and to evaluate emerging ethical issues, such as algorithmic bias, privacy violations, and digital access inequality. The research subjects include a collection of scientific literature obtained from academic journals, reference books, and relevant policy documents. This study employs a qualitative descriptive approach with a literature review method and thematic analysis techniques. The findings indicate that teachers are not only responsible for delivering technical content but also play a crucial ethical role in cultivating students’ social awareness regarding the moral implications of technology. The study concludes that strengthening teacher ethics through continuous training and integrating digital ethics into the curriculum is essential. The implications point to the urgency of adaptive educational policies in response to the ethical dynamics of AI-based learning.

As artificial intelligence (AI) gains more traction in education in the digital era, questions arise about the number of empirical studies available that have useful outcomes on the protocols for deploying the technology in the sector. This univariate descriptive survey was consequently conducted to examine whether the ethics of AI predict teacher integrity in the application of smart technologies in public primary schools in the digital age in Cross River State, Nigeria. Two hypotheses were formulated for the research. 1,600 teachers were recruited from 16 public primary schools across four education zones of the state to participate. Ethics of AI and Teacher Integrity in the Application of Smart Technologies Questionnaire (EAITIASTQ) was adopted to generate data. Based upon the Value Sensitive Design (VSD), simple linear regression was used to analyze data, aided by SPSS. Findings suggest that user transparency significantly predicts teacher integrity in the application of AI in public primary schools; user accountability significantly predicts teacher integrity in the utilization of AI in public primary schools. It is recommended that a sound ethical protocol on the application of AI in school be codified in documents and made available for all primary school teachers; experienced and skilful personnel in computer and AI operations have to conduct regular supervision of teachers in relation to the use of AI in elementary schools

This study investigates pre-service teachers’ dependency on Generative Artificial Intelligence (GenAI), their perceptions of its effects, and their awareness of academic ethics. Employing a descriptive quantitative research design, data were collected through an online questionnaire adapted from Chan & Hu (2023) and the Indonesian Ministry of Education’s Guidebook on GenAI Usage (2024). The study involved 100 pre-service teachers from the English Education Study Program, with 46 valid responses. The results indicate that while most participants are uncertain about their dependency on GenAI, many acknowledge its benefits in saving time, providing unique insights, and offering personalized feedback. However, concerns remain regarding its impact on digital competence, social interaction, teamwork, critical thinking, and leadership skills. Additionally, perceptions of GenAI’s effect on problem-solving skills are evenly divided. In terms of academic ethics, more than half of the respondents are unsure whether using GenAI undermines ethical values. Nonetheless, most pre-service teachers report that they rewrite AI-generated content in their style and provide references. Given the high level of uncertainty in responses, this study highlights the need for universities and lecturers to provide clearer and more intensive guidance on responsible GenAI usage. Future research should explore its impact on academic skill development and employ alternative research designs for deeper insights.

In an era shaped by artificial intelligence (AI) and digital transformation, ethical and impactful scholarly writing remains a core competency for educators and researchers. This study evaluated the “Training EDGE: Empowering Development and Global Excellence” webinar hosted by the Nueva Ecija University of Science and Technology (NEUST). The program aimed to strengthen participants’ academic writing skills using the IMRaD format and to promote ethical AI integration in research. Employing a mixed-methods design, data from 988 participants were analyzed through pre- and post-training self-assessments, structured evaluations, and qualitative feedback. Findings reveal a substantial increase in perceived knowledge (mean score rising from 2.90 to 3.60), with strong participant satisfaction across content, delivery, and trainer effectiveness dimensions. Thematic analysis further showed increased ethical awareness, confidence in research writing, and appreciation for interdisciplinary learning. The study underscores the importance of inclusive and interactive training models that address both technical and ethical dimensions of scholarship in a technology-driven academic landscape.

As the Fourth Industrial Revolution unfolds, artificial intelligence (AI) technologies are advancing rapidly. However, the pace of ethical and legal consensus building has lagged behind these technological developments. Simultaneously, there is a growing societal demand for AI ethics education. This study aims to design and implement an AI ethics education program for prospective ethics teachers and evaluate its effectiveness. The study was conducted during the second semester of 2024, targeting 12 prospective ethics teachers enrolled at a teacher training institution in the Chungbuk region of South Korea. The program addressed key topics such as understanding AI concepts, exploring the ontology of AI, recognizing the necessity of AI ethics, and applying AI ethics in education. To assess the program's effectiveness, pre- and post-tests were conducted, and the data were analyzed using the Wilcoxon signed-rank test. The analysis revealed no significant changes in the participants' AI ethical awareness. However, their competence in integrating AI into education showed statistically significant improvement. These findings suggest that AI ethics education can contribute to enhancing the practical educational competencies of prospective teachers.

The integration of Artificial Intelligence (AI) into education is reshaping pedagogical practices, policy frameworks, and ethical standards worldwide. This paper explores how AI is transforming educational policy and teaching in Albania, a country still adapting to the post-pandemic digital era. Drawing upon comparative policy analysis and global frameworks from UNESCO, the European Union, and the OECD, the study identifies significant gaps between Albania’s current legal structures and international standards. While European education systems emphasize ethical governance, teacher AI literacy, and digital inclusion, Albania’s higher education law (Law No. 80/2015) remains silent on online learning, AI ethics, and data protection. The COVID-19 crisis accelerated technological adoption but revealed the absence of systemic readiness. The paper argues for a human-centered AI policy that links innovation to ethics, proposing reforms for 2025–2030 to align with European directives. Additionally, the research highlights the critical role of teachers as ethical mediators in AI-supported classrooms, emphasizing that technological integration must be accompanied by moral awareness and cultural adaptation. The findings also suggest that Albania’s alignment with the EU Artificial Intelligence Act (2024) could foster a coherent framework for accountability, transparency, and fairness in digital education. Through a multidisciplinary approach, this study provides both analytical and policy-oriented insights into how developing educational systems can balance innovation with human dignity, ensuring that Artificial Intelligence becomes a catalyst for inclusive, equitable, and ethically grounded education.

No abstract available

This study aims to examine the effectiveness of an artificial intelligence (AI) literacy training and mentoring program in enhancing teachers’ AI-related competencies. A quantitative one-group pretest–posttest design was employed, involving 25 junior high school teachers who participated in a three-day training program focusing on fundamental AI concepts and their practical application in instructional planning, assessment, and learning content development. Data were collected using standardized pretest and posttest instruments and analyzed through descriptive statistics, nonparametric inferential tests, effect size estimation, and normalized gain analysis. The results revealed a statistically significant improvement in posttest scores compared to pretest scores, indicating meaningful learning gains following the training. The magnitude of improvement was substantial, as reflected by a very large effect size and a mean normalized gain of 0.672, suggesting a medium category of achievable learning improvement. Furthermore, no statistically significant differences in learning gains were found between male and female teachers, indicating that the training outcomes were broadly equitable across gender groups. This is confirmed by effect size and the normalized gain values obtained which are almost the same in male and female.

Artificial intelligence has significantly transformed educational practices across disciplines. This study investigated the cognitive–behavioral mechanisms underpinning mathematics teachers’ engagement with AI teaching tools through an extended technology acceptance model. Utilizing structural equation modeling with data from 500 mathematics educators, we delineated psychological pathways connecting perceptual variables to technology engagement and pedagogical outcomes. Results revealed that perceived usefulness functioned as the primary determinant of AI engagement, while perceived ease of use operated exclusively through sequential mediational pathways, challenging conventional technology acceptance paradigms. Domain-specific factors, such as teacher AI literacy and mathematics teaching beliefs, emerged as significant mediators that conditioned technology-related behavioral responses. The mediators in this study illustrated differential attitudinal mechanisms through which perceptual variables transformed into engagement behaviors. These findings extended technology acceptance theories in educational contexts by demonstrating how domain-specific cognitive structures modulated perception–behavior relationships in professional technology adoption in mathematics education.

Teacher education programs face the challenge of integrating multiple standards into their licensure courses with limited instructional time. To address this, we need innovative approaches to effectively integrate topics like computational thinking (CT) and Artificial Intelligence (AI) literacy, which are increasingly included in teacher standards. We developed a core educational technology course for teachers from diverse fields-deaf education, math, music, early childhood, and more-to introduce these concepts. Using ''scaffolded critique rubrics,'' teachers evaluated tools and resources from platforms like Code.org or PBS Kids, assessing their suitability for their students. The rubrics helped teachers critique computing tools for accessibility issues, differentiate AI biases from automation ethics, and engage deeply with CT concepts. By connecting these topics to their student needs, teachers reflected on how to adapt resources for their classrooms. This report outlines the course development and its impact on teacher engagement

This study examined the association between teacher education students’ artificial intelligence (AI) literacy and their digital competence during educational internships. Grounded in the AI-TPACK framework, the study explored to what extent the three dimensions of AI literacy, AI Knowledge (AIK), AI Affectivity (AIA), and AI Thinking (AIT) affect five dimensions of digital competence: Digital Technology Proficiency (DTP), Digital Teaching Competence (DTC), Digital Learning and Innovation (DLI), Digital Values (DV), and Digital Personality Traits (DPT). Based on the survey data collected from 304 teacher education students, the findings revealed that AIA, reflecting positive attitudes and confidence toward AI, was the strongest predictor across all dimensions of digital competence. In contrast, AIK, representing theoretical knowledge about AI, showed no significant predictive effect, while AIT significantly predicted only DTP, DTC, and DLI. These findings suggest that teacher education programs must emphasize the affective and cognitive aspects of AI literacy and practical experiences to cultivate comprehensive digital competence among future educators effectively.

Objectives The purpose of this study is to explore the relationship between pre-service early childhood teachers' play expertise, AI literacy, and AI-enabled play support competence. Methods The subjects of the study were 187 pre-service early childhood teachers studying in young children-related majors at universities located in the Seoul metropolitan area. The measurement tools were play expertise, AI literacy, and AI-enabled play support competence, and descriptive statistics, Pearson correlation analysis, and stepwise multiple regression analysis were conducted using SPSS 27.0. Results The results of the study showed that, first, pre-service early childhood teachers' play expertise is significantly and positively related to their AI literacy and AI-enabled play support competence. Second, AI literacy of pre-service early childhood teachers is a static predictor of play expertise, and among the subfactors of AI literacy, AI play and teaching support is the most influential static predictor, and AI understanding is also a static predictor. Third, understanding of AI-enabled play, a subfactor of AI-enabled play support competence, was found to be a static predictor of pre-service early childhood teachers' play expertise. Conclusions Therefore, this study helped teacher educators in higher education institutions understand the importance of play expertise for preparing pre-service early childhood teachers to perform the educationally meaningful AI education with young children, and proposed the development of a pre-service teacher education program that can foster AI literacy and AI-enabled play support competence to enhance pre-service early childhood teacher's play expertise.

This study investigates the relationships among Al literacy, teacher efficacy, and professional identity among English teachers in Korea's EFL context. As Al increasingly integrates into educational environments, it is crucial to understand how these factors interplay in shaping teachers' professional identities. The findings suggest that fostering Al literacy can significantly enhance teachers' sense of professionalism and efficacy, thereby reinforcing their unique roles in the Al era. However, the initial study's limited sample size necessitates ongoing research with a larger cohort to validate these results. Future studies should aim to refine research tools, expand sample sizes, and explore Al literacy development strategies tailored to different career stages and generations. Such efforts will contribute to a more comprehensive understanding of Al's impact on education and offer practical insights for teacher development in an Al-driven world.

Generation-Artificial Intelligence (Gen-AI) is widely used in education and has been shown to improve students’ mathematical abilities. However, dependency on Gen-AI may negatively impact these abilities and should be approached with caution. This study uses Structural Equation Modeling (SEM) to determine the relationship between AI literacy, AI trust, AI dependency, and 21st-century skills in preservice mathematics teachers. This research utilizes a self-designed questionnaire with 469 preservice mathematics teachers as respondents. SPSS and AMOS software were used for data analysis. The findings reveal that both AI trust and AI literacy significantly influence preservice mathematics teachers’ dependency on Gen-AI. Furthermore, this dependency on Gen-AI among preservice mathematics teachers has a significant negative effect on their problem-solving ability, critical thinking, creative thinking, collaboration skills, communication skills, and self-confidence. This research provides new information to governments, schools, and teachers that caution should be exercised when attempting to enhance AI literacy and trust in AI among preservice mathematics teachers.

The use of artificial intelligence in education (AIEd) has become increasingly significant globally. In China, there is a lack of research examining the behavioral intention toward AIEd among pre-service special education (SPED) teachers in terms of digital literacy and teacher self-efficacy. Building on the technology acceptance model, our study evaluated the aspects influencing pre-service special education teachers’ intention toward AI in education. Data was gathered from 274 pre-service SPED teachers studying at a Chinese public normal university of special education and analyzed using structural equation modeling (SEM). The results show that digital literacy is associated with the perceived usefulness and ease of use of AIEd, which influences SPED teachers' intention to use AIEd. Additionally, digital literacy significantly impacts the self-efficacy of SPED teachers. Given these results, AI designers in special education should comprehend the effectiveness and usability of AIEd for fostering behavioral intention formation. Simultaneously, special educational programs that identify key content and activities for digital literacy training should be developed, and educators should attempt to execute the relevant pre-service training to enhance the intention of pre-service SPED teachers toward AIEd.