教师人机协同（人机协作）人智协同（人智协作）

To improve English competence, this study examines the complex requirements and benefits of implementing artificial intelligence (AI) tools in Chinese primary schools. Semi-structured interviews, focus groups, and classroom observations offer multifaceted perspectives from educators and learners who have personally navigated the implementation of intelligent technology. The findings demonstrate divergent views among teachers, with optimism regarding the success of tailored instruction balanced against pessimism regarding the lack of nuanced cultural contextualisation in algorithmic materials. While students concur with these ideas, they also emphasise that teacher supervision creates a balance between directive feedback and creative or emotional development. Direct observations in the classroom show growing digital divides, technology limitations, and a reluctance to abandon tried-and-true instructional strategies. The possibility of upending paradigms necessitates reevaluating assumptions, such as the inevitability of data-driven, automated Education. The findings raise questions about whether conversational bots and intelligent tutors can enhance skill efficiency to the extent that they do so without compromising equity, holistic development, and the risks associated with passive student dependency. The empirical study encourages methodical execution, maximising personalised and interactive learning experiences offered by AI systems, while maintaining strong teacher-student relationships and avoiding fragmented development. It informs policies that prioritise thoughtful, culturally appropriate design without using rhetoric associated with "techno-solutionism". The findings present a novel integration philosophy that reconciles quantifiable productivity with high-quality learning experiences and self-actualisation. As intelligent technology becomes more ubiquitous, academics must continue to be cautiously optimistic while providing a clear description of implementation realities as they move from theory to practice. This study prompts us to consider what Education needs to become and should become in an era of increased immersion.   本研究通过半结构化访谈、焦点小组和课堂观察，探讨人工智能在中国小学英语教学中的应用现状。教师群体对AI个性化教学持谨慎乐观态度，但普遍担忧算法材料缺乏文化适应性；学生则认为教师监督能平衡AI的反馈与创造力培养。课堂观察揭示了数字鸿沟、技术局限性和传统教学惯性等问题。研究提出量化效率与人文关怀并重的整合路径，为AI教育产品的文化适配性和伦理设计提供政策建议。  Article visualizations:

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AI technologies are reshaping our world and prompting education scholars to rethink both the aims and methods of schooling to prepare learners for the future (Holmes et al., 2019). Meanwhile, interest in integrating AI into science education has grown, with much discussion focusing on the impact of AI on student engagement and learning performance. Among those interests and debates, questions arise about AI’s ability to provide instructional, learning, and evaluative tools, as well as the practices and challenges of teacher-AI collaboration in education. To conclude, human–AI collaboration in science education offers substantial potential to enrich teaching and learning, on the condition that AI functions as a collaborative partner guided by teacher expertise, ethical principles, and a commitment to equity. Realizing this potential requires deliberate, evidence-based design decisions, professional development that centers on teacher agency, and governance frameworks that foster trust and transparency in AI-assisted learning. By sustaining an ongoing partnership among teachers, researchers, and AI developers, we can foster collective intelligence in human-AI collaboration that illuminates scientific reasoning, personalizes instruction, and supports students in developing robust scientific understandings for the twenty-first century.

This study explores the impact of human-AI collaborative teaching strategies on English teachers in secondary schools. Based on semi-structured interviews with five English teachers in Jiangxi Province, thematic analysis was conducted using the SAMR, UTAUT, and GHEX-IPACK theoretical frameworks. The findings indicate that AI technology is primarily applied in scenarios such as resource generation, assignment distribution, and learning analytics. By substituting traditional tools, enhancing teaching interactions, and reconstructing instructional processes, AI facilitates a shift in teaching strategies from “teacher-led” to “human-AI collaboration”. Teachers generally recognized the potential of this model for improving efficiency and supporting personalized learning, but also pointed out challenges, including data bias, hardware limitations, and a lack of emotional interaction. The study suggests that achieving deep human-AI collaboration requires balancing technological efficacy with humanistic care relying on blended instructional design and teacher training to optimize teachers’ knowledge structures. This research preliminary constructs a practical model of human-AI collaboration in secondary school English education, providing insights for teacher professional development. 

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.

This paper examines the role of digital technologies in enhancing personalized and collaborative learning in education. Drawing on theories of constructivism, personalized learning, and collaborative learning, it explores how adaptive platforms improve student outcomes. Case studies show that these technologies, along with collaborative tools in online courses, can foster greater engagement and deeper learning. However, their effectiveness depends on their integration with traditional teaching practices, where teachers remain central in guiding learning and providing emotional support. The paper concludes that while digital tools offer valuable benefits, their success relies on addressing challenges such as equitable access and teacher training.

This study investigates the key factors which influence design learners’ behavioral intention to collaborate with AI in the educational metaverse (EMH-AIc). Engaging design learners in EMH-AIc enhances learning efficiency, personalizes learning experiences, and supports equitable and sustainable design education. However, limited research has focused on these influencing factors, leading to a lack of theoretical grounding for user behavior in this context. Drawing on social cognitive theory (SCT), this study constructs a three-dimensional theoretical model comprising the external environment, individual cognition, and behavior, validated within an EMH-AIc setting. By using Spatial.io’s Apache Art Studio as the experimental platform and analyzing data from 533 design learners with SPSS 27.0, SmartPLS 4.0, and partial least squares structural equation modeling (PLS-SEM), this study identifies those rewards, teacher support, and facilitating conditions in the external environment, with self-efficacy, outcome expectation, and trust in cognition also significantly influencing behavioral intention. Additionally, individual cognition mediates the relationship between the external environment and behavioral intention. This study not only extends SCT application within the educational metaverse but also provides actionable insights for optimizing design learning experiences, contributing to the sustainable development of design education.

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The integration of artificial intelligence (AI) into education has generated increasing interest, particularly in its role in academic writing. While prior studies have examined students’ use of AI, limited attention has been given to teacher aspirants’ perceptions of AI collaboration with human writers across subject disciplines. Addressing this gap is crucial in preparing future educators for responsible AI integration in teaching and learning. This study aimed to determine the perceptions of English, science, and mathematics teacher aspirants toward AI collaboration with human writers in academic essay writing and to examine differences across subject disciplines. A descriptive‒quantitative design was employed, involving 90 undergraduate teacher aspirants equally distributed across the three disciplines. Stratified random sampling was used to ensure adequate representation, and data were collected through a structured questionnaire consisting of 10 items on a 5-point Likert scale with high internal reliability (α = 0,94). The data were analyzed via descriptive statistics and one-way ANOVA. The findings revealed generally positive perceptions of AI’s role in writing, particularly in generating outlines, assisting with citations, and supporting editing processes. Significant differences emerged among disciplines, with science majors expressing the most favorable perceptions (M = 4,13), followed by English (M = 3,94) and mathematics majors (M = 3,90). The study concludes that disciplinary orientation shapes openness to AI collaboration in academic writing. It is recommended that teacher education programs integrate structured training on the ethical and effective use of AI, ensuring a balance between technological assistance and the preservation of creativity and critical thinking.

The development of artificial intelligence has entered the era of cognitive intelligence, where machines are beginning to transcend their instrumental role and evolving into collaborative partners. This technological progress has triggered a fundamental shift in educational philosophy—moving from an emphasis on knowledge transmission to a focus on competency development. However, while implementing instructional design transformations through the “Problem-Based—Human-AI Collaboration—Ecological Evolution” framework, we have observed that students using AI can easily fall into the “prompt engineer trap,” passively accepting generated outcomes. In fact, the ultimate goal of AI integration is not to cultivate efficient users, but to nurture critical creators who can engage with AI reflectively and creatively. By analyzing pain points in students’ behavioral experiences within AIGC environments, this study is grounded in three interrelated theoretical foundations: critical thinking theory, the transformation and reshaping of design behavior paradigms in AIGC contexts, and the methodology of technology and behavioral design. With a student growth-oriented approach, we construct a critical behavioral design framework for Teacher-Student-AI collaboration, aimed at promoting deep learning and metacognitive development in student design courses.

This study investigates Shiksha copilot, an AI-assisted lesson planning tool deployed in government schools across Karnataka, India. The system combined LLMs and human expertise through a structured process in which English and Kannada lesson plans were co-created by curators and AI; teachers then further customized these curated plans for their classrooms using their own expertise alongside AI support. Drawing on a large-scale mixed-methods study involving 1,043 teachers and 23 curators, we examine how educators collaborate with AI to generate context-sensitive lesson plans, assess the quality of AI-generated content, and analyze shifts in teaching practices within multilingual, low-resource environments. Our findings show that teachers used Shiksha copilot both to meet administrative documentation needs and to support their teaching. The tool eased bureaucratic workload, reduced lesson planning time, and lowered teaching-related stress, while promoting a shift toward activity-based pedagogy. However, systemic challenges such as staffing shortages and administrative demands constrained broader pedagogical change. We frame these findings through the lenses of teacher-AI collaboration and communities of practice to examine the effective integration of AI tools in teaching. Finally, we propose design directions for future teacher-centered EdTech, particularly in multilingual and Global South contexts.

ABSTRACT Given the widespread use of generative artificial intelligence in different domains, the present study investigates Moroccan EFL teachers’ perceptions and practices of teacher artificial intelligence collaboration (TAC) for reflective practice and the impact it may have on their instructional practices. The study collects data from 56 Moroccan EFL teachers practicing in the Souss Massa region using a TAC for reflective practice questionnaire and semi-structured interviews. The findings show that participants generally view the integration of TAC into their reflective practice positively, but they express reservations about the irreplaceable nature of human interaction. The study also revealed that participants used TAC as a main or supplementary source of reflection. Further, the findings suggest that TAC for reflective practice can increase teacher confidence, identify areas for professional development, and potentially enhance instructional strategies. However, the findings highlight that many factors, such as teachers’ expertise and context of use, influence the effectiveness of TAC for reflective practice. Moreover, the study highlights the need for reflective practitioners to balance TAC for reflective practice and known forms of reflective practice. The study concludes with implications for different stakeholders.

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The objective of this research is to explore the collaborative role of AI and teachers in providing feedback on written assignments. Teacher feedback is key to improving students’ writing, but now there is AI that can perform the same role. The study uses a combination of classroom testing and questionnaires to collect information. Forty students studying BS English at Shaikh Ayaz University, Shikarpur, Pakistan participated, receiving feedback on their papers from a teacher, and the same assignments also received AI-generated feedback. The results were analyzed thematically and interpreted accordingly. The students’ perspective is that AI tools helped students improve grades by addressing grammar and sentence-level issues. Teachers benefited from less workload when AI was included; the feedback was faster, encouraging students to revise their work more readily. Human intervention is still required to ensure better quality and more intelligent AI suggestions. The findings suggest that teachers and AI work more effectively together to provide feedback on writing, including grammar and formal expression of opinions. The research implies that adopting AI into the curriculum carries responsibilities that need to be formally stated in policies and tested in classroom settings.

In the era of Artificial Intelligence, human-computer collaborative teaching has become a new picture of future development in the field of education, and how to utilize AI technology to collaborate on English lesson plan design has not yet been fully studied. Based on this, this paper explores the framework of context-aware English lesson plan collaborative design and improves the Bayesian knowledge tracking to propose the CS-BKT model to obtain students' English knowledge level and facilitate assisting English lesson plan design. The results show that the CS-BKT model possesses a better knowledge state tracking effect with optimal values of AUC, Accuracy, r2 and RMSE metrics, the first three of which are improved by 0.85% to 25.16%, 1.38% to 12.53% and 6.26% to 230.95%, while the latter is reduced by 3.42% to 13.80%. After applying the proposed model and framework, students in the experimental group showed significantly higher results in the latter five tests of their knowledge level than those in the control group (p < 0.05) and obtained higher teacher satisfaction. The context-aware English lesson plan co-design framework integrates context-awareness and artificial intelligence technologies and can promote the overall improvement of English teaching quality.

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.

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Teacher evaluation plays a critical role in ensuring educational quality. However, the traditional approaches, such as classroom observations and student surveys, remain limited by inherent subjectivity, high resource consumption, and delayed feedback. While artificial intelligence (AI) offers transformative potential through automated, real-time analysis of instructional data, purely algorithmic methods introduce new challenges related to contextual interpretation, ethical risks, and practical adoption. This paper proposes a novel framework for AI–human collaborative teacher evaluation, designed to synergize the computational efficiency of AI with the nuanced expertise and ethical judgment of human evaluators. The framework establishes dynamic task boundaries, implements explainable workflows across three modes of interaction (Embedding, Copilot, and Agent), and incorporates continuous bias-auditing mechanisms to enhance fairness and adaptability. Planned validation via a mixed-methods approach is expected to demonstrate improvements in evaluation accuracy, efficiency, and teacher receptiveness. By integrating technical innovation with human-centered design and ethical rigor, this study offers a comprehensive foundation for building scalable, culturally adaptive, and pedagogically meaningful evaluation systems.

This paper explores how to use ChatGPT-4 as an assistant for personalized teaching in the poetry writing class of Class 1 of the third-year university majoring in Chinese language and literature, to realize the human-computer collaboration between ChatGPT and the teacher, and conduct personalized teaching to provide students with personalized teaching. professional guidance and timely feedback. Traditional poetry writing teaching is difficult to meet the personalized needs of large-scale classes, and the introduction of artificial intelligence (AI) assistants can effectively solve this challenge. Therefore, this paper discusses how to use the advice of ChatGPT -4 and the professional guidance of teachers to improve the efficiency and quality of students' poetry homework correction, achieve personalized teaching guidance for students, and thus promote the improvement of poetry writing skills.

With the rapid development of artificial intelligence technology, the application of personalized learning systems in vocational education has gained increasing attention. However, existing systems often handle only single-modal data and lack real-time monitoring and adaptive adjustments based on emotional states. To address these issues, this study proposes a personalized learning system based on human-machine collaboration, integrating multimodal data fusion and emotion-driven mechanisms, specifically designed for vocational education. Experimental validation shows that the proposed system significantly improves student learning outcomes and teacher satisfaction, particularly in the areas of complex interdisciplinary knowledge integration and personalized learning path generation. This study provides new insights into the design of personalized learning systems in vocational education and lays the foundation for the application of human-machine collaboration and multimodal data fusion technologies in the educational field.

This study analyzes the effect of introducing artificial intelligence (AI) technology on the teacher-student relationship and educational human relationships and proposes an AI-teacher collaboration model to support the holistic development of learners. The importance of human interaction is explained based on Buber’s theory of the “I-Thou” and “I-It” relationships, and the qualitative improvement of interactions in an AI-based learning environment is discussed based on Dewey’s empirical educational theory. AI provides real-time analysis of learning data and customizes feedback, thus facilitating teachers in supporting emotional scaffolding and comprehensive learning to promote learning motivation and engagement. Through a virtual-scenario analysis, this study empirically examines whether the AI-teacher collaboration can maximize learners’ self-directed learning and educational performance. The results suggest that AI-teacher collaboration is key to the future paradigm shift in education.

The rapid development of Generative Artificial Intelligence (GAI) technologies is driving profound changes in education. This study investigates the construction of human–Artificial Intelligence (AI) collaborative teaching models supported by GAI, with the goals of improving teaching efficiency, enabling personalized learning, and optimizing the allocation of educational resources. The proposed framework integrated intelligent content generation, real-time tutoring, adaptive learning pathways, and a teacher–student–AI collaboration mechanism. The generative model employed was LLaMA-2-13B, which was domain-adapted through supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF). The experiment was conducted in a university course titled Data Structures and Algorithms, involving 60 students in the experimental group and 60 students in the control group. Multi-dimensional data were collected and analyzed, including academic performance, student engagement, interaction depth, technology acceptance, and long-term retention. The quality of AI-generated content was evaluated using Bilingual Evaluation Understudy (BLEU, 0.74) and Recall-Oriented Understudy for Gisting Evaluation (ROUGE, 0.79), with a Cohen’s κ value of 0.86 indicating high inter-rater consistency. The results showed that the GAI-driven human–AI collaborative model significantly improved final exam scores (85.6 vs. 78.3, p < 0.001), average assignment grades (91.2 vs. 84.7, p < 0.001), and learning satisfaction (p < 0.05), while reducing cognitive load and enhancing personalized and interactive learning. This study provides both a theoretical framework and practical guidance for innovating educational models in the era of intelligent technology, offering valuable insights for advancing the digital transformation of education.

With the rapid development and application of artificial intelligence technology, education is ushering in unprecedented changes. Through data analysis and intelligent algorithms, AI can achieve an efficient allocation of educational resources and optimization of the teaching process, while humanmachine collaboration, as a new educational model, emphasizes the collaboration between human intelligence and machine intelligence, opening up new paths for educational equity and innovation. In this study, we firstly sort out the current status quo related to human-machine collaboration and AI-assisted education; secondly, we analyze the connotation of human-machine collaboration and explore three types of human-machine collaborative teaching forms and related examples in virtual space, physical space, and hybrid space; thirdly, we illustrate the four types of application scenarios of human-machine collaboration in instructional design and teacher-student interaction, which are mainly the application and practice of AI in educational decision-making, intelligent tutoring, automatic assessment, and classroom evaluation scenarios; finally, it analyzes the future development trend of AI+education fusion, and at the same time points out the challenges of teachers' role and responsibility transformation, technology adaptability, and ethical risks, based on which it proposes the development path of future AI + education fusion.

The rapid development of artificial intelligence (AI) technology is reshaping the education ecosystem, driving the transformation of teachers' roles from traditional knowledge transmitters to learning facilitators and ethical supervisors. Through a systematic literature review, this study explores the driving factors of teachers' role transformation in the AI era and its impact on teaching strategies, while analyzing the practical challenges faced by teachers. The findings reveal that technological drivers, policy requirements, and societal demands synergistically reshape teachers' functions. However, this transformation faces multi-level challenges, including skill gaps, ethical dilemmas, and psychological conflicts. To address these issues, this paper proposes a "human-AI collaboration" teaching model, emphasizing the need to enhance teachers' decision-making authority in AI tool design. It further suggests optimizing policies, teacher training, and ethical frameworks to balance technological empowerment with educational values. The study provides theoretical and practical insights for educators to adapt to the AI era, advocating for the integration of technology, policy, and humanistic principles to restore education's core mission of nurturing holistic individuals.

This paper focuses on the transformation of the project-based learning (PBL) teaching model driven by artificial intelligence (AI), and explores the two-way reconstruction path of teacher roles and student abilities. Combining metacognitive theory to analyze the pain points of traditional PBL, this paper systematically sorts out the functional reconstruction path of AI in the dimensions of teaching design, process monitoring, and evaluation feedback. Then, starting from the social role theory, this paper deeply analyzes the transformation of teacher identity and the reconstruction of student abilities in the AI-PBL fusion scenario. AI not only reshapes the logic of cultivating students’ abilities but also prompts teachers to achieve deep changes in their roles at the cognitive, relational, and ethical levels. Human-machine collaboration should not replace teachers’ emotional values and educational judgments, but should become a key support for optimizing the educational ecology and realizing personalized education.

This research investigates the effectiveness of AI generative ChatGPT as a teacher assistant to reduce workload and prevent burnout in secondary schools. Lesson planning and content development were significant contributors to teacher burnout. In response, ChatGPT was tested with tailored queries for English, science, and math subjects, utilising an explanatory research approach to assess ChatGPT’s capabilities in personalised planning and content development, given that there is limited available information around this topic. The study highlights ChatGPT’s benefits in personalised planning through task-specific prompts and AI-human collaboration. Aligned with UAE’s AI integration objectives, the study emphasises balanced use and educational reform potential. Integrating AI tools optimises teacher planning, enhances instructional support, and refines resource allocation, contributing to AI’s academic potential while stressing burnout mitigation’s importance for educational advancement.

This work explored the area of human-machine collaboration in actual educational contexts based on the analysis of the ways of interaction between the teacher and the artificial intelligence (AI) devices and how the interactions affect teaching process, student experience, and the circumstances facilitating or inhibiting AI adoption. The quantitative descriptive-analytical type of research was used to gather data using a stratified sample of 312 teachers in primary, secondary, and tertiary institutions by administering a validated Likert-scale questionnaire. Descriptive statistics showed that the sample was diverse and experienced AI (56% of which were weekly users). The outcomes of Objective 1 revealed high consistency that AI facilitated instructional decision-making, lessened the work load, and increased accuracy and permitted instructors to maintain autonomy (mean = 3.85). The regression analysis revealed that human-machine collaboration was a significant predictor of the teaching and learning outcomes that explained half of the variance (50% 0.71 = 0.000). The results of ANOVA showed that there were significant differences in the perceived AI-based challenges between the groups of teaching experience (F = 6.27, p = 0.000), indicating that the adoption of AI depends on the background and exposure of the profession. All in all, the conclusions point to the fact that AI is an effective instructional companion but needs constant institutional assistance, professional growth, and access to technology equal opportunities to be successfully incorporated into the teaching process.

This study investigated the usage of conversational artificial intelligence (CAI) to support learners in foreign language classrooms. It employed Google Assistant and focused on the interactions between the teacher, learners, and CAI, as well as the teacher’s collaboration with CAI. Using social network and content analyses of two 50-minute language classes and group interviews, this study revealed that the teacher and CAI played a significant role during classroom interactions. The teacher employed various talk moves to facilitate interactions between the students and CAI. There were several instances of collaboration between the teacher and CAI during classroom facilitation. This study highlights the implications of the collaboration between human teachers and CAI in classrooms for teaching foreign languages and suggests avenues for future research.

Understanding the synergistic potential of human collaboration with artificial intelligence (AI) in creative endeavours, such as ideating Serious Game (SG) concepts, is of vital interest in our era of digital transformation. This study probes two pivotal questions: First, how does the incorporation of a GPT-4 transformer AI, assuming the role of a teacher, influence support for student teams during the ideation and balancing of SG concepts? Second, what are the students' perceptions of AI integration when co-designing these concepts with an AI in the educator role? In a between-group research design, two distinct groups engaged in a collaborative role-playing activity with digital role-specific cards and a visualised board to ideate and balance Serious Games addressing privacy decision-making. The first group, engaging in a local setting, collaborated with an AI that played the teacher role. In contrast, the second group played the co-ideation activity in a remote setting, with a human playing the role of the teacher. The findings indicate that generative AI can successfully be sourced to play the teacher role in a collaborative role-playing activity. Crucially, the timing of AI intervention thereby emerged as an essential factor that can impair creative support. Scheduled AI interventions can offer fresh insights but may not align with immediate team needs. The insights underscore the requirement to determine the most effective timing for AI intervention in human-AI co-playing ideation sessions to foster the full potential of an AI filling a role in a collaborative design process. Implications synthesised from the analytical findings and practical insights on AI-suggested design propositions/conflicts are discussed conclusively.

The AI-Teacher Teaching Tasks Spectrum (AITTTS) was conceived as a way to understand the relationship between human teachers and the ever-evolving AI technologies in education. This study demonstrates how the AITTTS framework can be operationalised into a tangible intervention, showcasing the design models and practical applications of the AITTTS in real-world educational settings. By categorising teaching tasks into a spectrum, the AITTTS delineates the roles that AI and human teachers can play, providing a structured and nuanced understanding of their collaboration. As a result of the practical application of the AITTTS, a design model was birthed in this study. It highlights various aspects of holistic student outcomes such as positive electronic nonverbal communication (eNVC) cues, adaptive learning paths, and interactive learning responses as elements by which AI should be designed. By providing a structured approach for educators to incorporate AI tools and interventions in their learning environments, this research lays the groundwork for further exploration of the synergistic relationship between AI and human teachers in modern education. This framework can serve as a guide for educators to develop and implement AI-enhanced teaching strategies, fostering a more dynamic and responsive educational landscape.

Currently powerful Large AI models (with trillions of parameters) have become a transformative mechanism for performing interactive language-based tasks between human and machine (Tan et al., 2023). Given that the majority of interactive applications involve engaging dialogue between teacher and student, there exists a natural penetration between this emerging technology and classroom teaching. However, despite this potential synergy, implementation and integration of these models into current teaching practices remains challenging. In the context of these challenges, research programmers are formulated by visualizing the effectiveness of classroom-based LLM interactive applications in a diversified manner. New datasets and standards with meaningful evaluation are provided to foster corresponding research communities. Also, a unified framework is given to facilitate the adoption of pre-trained LLMs for creative mainstream educational QA tasks. This comprehensive framework supports processing lengthy conversation text data and condensing messy informative text into well-organized tabular formats. Furthermore, from text-to-tabular processing, basic and extended tasks with diverse tangible applications are formulated, including improving inquiry efficiency, mining more informative dialogues, enhancing teaching compliance checking, and moderating student safety risks in large-scale synchronous interactions with AI (Guo et al., 2021).

The development of artificial intelligence technology has laid a solid foundation for the transformation of teaching methods and has also promoted the development of human-machine collaborative education. This paper, based on the existing human-machine collaborative "dual-teacher classroom" teaching model, introduces the use of knowledge graphs from the field of computer science and researches their application in the human-machine collaborative "dual-teacher classroom". The paper discusses the relationship between knowledge graphs and artificial intelligence education robots, presents the application of knowledge graphs in the human-machine collaborative "dual-teacher classroom" for different roles at different stages of education, and extracts a knowledge graph-driven human-machine collaborative teaching interaction model. Furthermore, based on the SAMR model, a knowledge graph-driven human-machine collaborative "dual-teacher classroom" teaching model is constructed, and several application scenarios of knowledge graphs in the human-machine collaborative "dual-teacher classroom" are described. AI education robots equipped with knowledge graphs can effectively improve teaching effectiveness, provide better personalized learning support, and help students develop comprehensively.

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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.

This study addresses critical challenges in the teaching of the “Decision Theory and Methods” course, including excessive reliance on teachers’ experience, lack of data-informed instructional decisions, and evaluation systems biased toward knowledge transmission. Drawing on the paradigm of data-informed decision-making, the research integrates human-machine collaboration and evidence-based practices to develop a dual-system instructional decision model combining machine intelligence with teacher cognition. A three-phase evidence-based instructional model—cov-ering planning, in-class interaction, and evaluation—is designed to support a transition from expe-rience-driven to evidence-supported teaching decisions. Empirical implementation demonstrates that the model enhances students’ decision-making cognition, research innovation, and reflective thinking, while reinforcing the course’s methodological and practical orientation. The study achieves a threefold integration of decision science and instructional practice, artificial intelligence and educator insight, and data evidence and educational values. It offers a novel approach for grad-uate-level curriculum reform and high-level talent cultivation in the context of intelligent education

In the intelligent age, the integration of artificial intelligence (AI) into education has sparked profound transformations, challenging traditional pedagogical paradigms. This study addresses the critical research question: How can human teachers and machines synergize their distinct strengths to optimize educational outcomes in the AI era? Through a mixed-methods approach combining literature analysis, case studies, and comparative frameworks, we systematically evaluate the complementary roles of humans and machines across cognitive, emotional, and skill-based dimensions. Our findings reveal that human-machine symbiosis — not substitution — is essential for fostering personalized, equitable, and innovative education. By redefining teacher roles (e.g., from “lecturer” to “facilitator”) and repositioning machines as collaborative partners, this research proposes a dynamic interaction model that enhances pedagogical efficiency and student development. The study contributes novel theoretical insights to human-AI collaboration in education and offers actionable strategies for policymakers and educators to navigate the evolving educational landscape.

This study aims at the current problems such as the disconnection and insufficient adaptability between digital teaching resources and the needs of teachers and students, and constructs a teaching resource optimization model from the perspective of human-machine collaboration, in order to improve resource allocation, enhance the accuracy of resources and personalized teaching. By analyzing the demands of teachers and students, a resource optimization model centered on the trinity collaboration of "teacher - GenAI - student" is proposed. Teaching resources are scientifically divided, and a four-step dynamic process of "data collection - demand transformation - resource generation - application iteration" is designed. Taking the "Obstacle Avoidance Design of Intelligent Logistics Robots" project of the Python course in higher vocational education as a case for practical verification, the results show that students' mastery rate of sensor principles, code debugging efficiency, and ability of cross-scenario knowledge transfer have all improved. This model has achieved the transformation of teaching resources from supply-driven to demand-led by precisely matching the needs of teachers and students and dynamically optimizing the supply of resources, providing theoretical support and practical paths for the construction of the human-machine collaborative education ecosystem.

With the latest progress and extensive application of AI in recent years, the educational sector has never experienced such a huge transformation. As a new teaching mode based on artificial intelligence technology and teacher’s subject knowledge that has been applied to College English classroom, human-machine collaboration teaching model has been applied to English classroom. This study is based on theory of symbiosis to establish and practice a “teacher-student-machine” human-machine teaching model applicable to college English teaching. The experiment continued for 12 weeks, pre-test and post-test were carried out to compare students’ academic performance and deep interviews of one teacher and nine students from experimental group were made to collect their opinions and comments about this teaching model. The above results are found to illustrate that this type of model can not only effectively enhance students’ English grade, but also stimulate teachers’ professional development and role mutation, and initially construct teachers, students and intelligent machine’s mutual-beneficial and symbiosis teaching ecosystem.

Feedback strategies in English oral teaching have long relied on teacher experience, and efficient personalized feedback has been a significant problem for large scale teaching. This study proposes an intelligent feedback method based on reinforcement learning to optimize the human-machine collaborative mechanism in English oral teaching. Specifically, the system designed multidimensional state variables including pronunciation accuracy, fluency, vocabulary breadth, grammatical correctness, confidence, participation, and fatigue level. The improved PPO (Proximal Policy Optimization) algorithm is used to optimize strategy selection, and a reward function is designed based on skill enhancement, emotional motivation, fatigue control, and personalized preferences to dynamically adjust feedback strategies. The experiment selected 120 vocational college students for a comparative study. The results showed that compared with traditional rule-based methods and DQN baseline, the improved PPO method significantly outperformed the comparative method in key indicators such as pronunciation accuracy, fluency, style matching accuracy, and average total reward, while maintaining real-time feedback response. This research also verified the feasibility and superiority of reinforcement learning in human-machine collaborative teaching.

In the 21st century, technology has become an integral part of education in revamping teaching and learning processes National Education Policy 2020 and the National Curriculum Framework 2023 emphasized the role of information technology as an enabler in education as well as teachers irreplaceable role. The integration of technology should align with the learning domains of Blooms taxonomy of Human development to enhance learning. At this juncture, descriptive survey research was conducted among 150 phase I Secondary school teachers in Kerala to study the awareness as well as their perception of integrating technology, including AI tools, in education using an awareness test and questionnaire, respectively. The first phase of the study focused on awareness, while the second phase gathered perceptions from those who had integrated technology into their classroom practices. The findings highlighted that while a blended approach of collaboration between technology and human teachers is widely preferred in the cognitive domain, teachers remain irreplaceable in domains requiring emotional understanding, ethical decision making, and motor skill development. The study results underscore insights for policymakers, educators, and institutions aspiring optimal utilisation of technology in Indian classrooms.

The rise of arti fi cial intelligence (AI) in language education has increased the collaboration between humans and machines (e.g., automated technologies, AI-powered digital tools, robots, etc.), leading to more effective, ef fi cient, inclusive, and sustainable language learning, teaching and assessment. In November 2022 when Open AI ’ s ChatGPT 3.5 was released, human – machine collaboration (HMC) gained more attention. HMC is a concept and practice where humans and machines are treated equally, complementing each other ’ s unique strengths to complete a task (Meniado, 2023a). It is a synergy between humans and intelligent machines, with the latter performing mundane and repetitive tasks allowing humans to handle the more complex tasks. In the current literature, HMC can be associated with collaborative intelligence, collective intelligence and augmented intelligence. Within the contexts of Digital Language Teaching 4.0 and 5.0, HMC places humans at the centre of innovation and creativity (Meniado, 2023a). It facilitates the development of human skills and machine operating abilities and allows humans and machines to increase their strengths and compensate for their fl aws. Moreover, it also lessens human errors and improves work effectiveness and ef fi ciency. Lastly, it allows humans and machines to perform jobs/tasks that they are unable to do on their own.

In the context of the digital intelligence era, artificial intelligence (AI) is driving a transformation in the education system from the traditional "teacher-student" binary classroom model to a "teacher-student-machine" triadic collaborative mode. Based on educational ecology and human-machine collaboration theories, this paper analyzes how AI reshapes the roles of teachers, students, and machines. Taking a public management course as an example, it demonstrates the practical pathways for reconstructing the teaching process in a "teacher-student-machine triadic collaborative educational ecosystem.

The essence of the human-machine collaborative "teacher-machine-student" educational paradigm lies in the collaborative interplay among all participants, aimed at elevating instructional quality and enhancing student performance. This study constructs a trilateral evolutionary game model aimed at maximizing educational effectiveness based on the beneficial relationships among teachers, machines, and students within the educational paradigm. It explores the strategic choices of each party during the game process, investigates the evolutionary paths of their behavioral strategies, and identifies the conditions for gradual stability. Matlab is utilized to analyze how changes in influencing factors within the evolutionary game model impact strategy selection. The research identifies that the optimal strategy equilibrium for enhancing educational efficacy involves teachers choosing proactive engagement, machines opting for proactive involvement, and students committing to active participation. For each entity, adjustments in teacher reputation gains, machine self-evolution benefits, and student learning gains influence their strategic decisions. These findings underscore the inherent principles of collaboration among teachers, machines, and students, offering concrete implementation strategies to promote human-machine cooperation and mutual educational advancement.

Analyzing human-machine collaboration behavior is crucial for integrating generative artificial intelligence(GAI) into the work of pre-service teachers, thereby enhancing teaching efficiency and quality. This study focuses on 55 pre-service teachers, employing lag sequential analysis and content analysis to examine their recorded collaboration processes and reflective texts. The aim is to explore the behavior patterns and characteristics of human-machine collaboration based on GAI. The findings indicate that the sequence C2→C1/C5→C3/C4→ C2 represents an efficient human-machine collaboration behavior pattern, in which pre-service teachers ask GAI questions, refine its responses based on their understanding, and iteratively collaborate to optimize lesson plans. The advantages of GAI in this collaboration include efficiency, comprehensive knowledge, clear logic, and lack of temporal and spatial constraints. However, its disadvantages are poor comprehension, limited professionalism, lack of personalization and innovation, reduced sense of communication, and technological limitations. The results provide significant insights for optimizing educational practices through collaboration between pre-service teachers and GAI.

The rapid development of Artificial Intelligence (AI) is transforming traditional educational models, gradually shifting from 'human-to-human' to 'human-machine collaboration' (HMC). Human-Machine Collaboration (HMC) is gradually becoming an important paradigm in the field of education, especially in foreign language teaching. This paper explores the application of human-machine collaboration in foreign language teaching, focusing on its theoretical foundations, practical cases, advantages, and challenges. Based on Cognitive Load Theory, Constructivist Learning Theory, and Personalized Learning Theory, this paper analyzes how human-machine collaboration can optimize teaching efficiency and enhance personalized learning experiences. Through case studies such as AI language platforms and real-time translation technologies, this paper demonstrates the potential of human-machine collaboration in providing immediate feedback, customizing learning paths, and fostering intercultural communication competence. However, the paper also points out the challenges posed by over-reliance on technology, the weakening of critical thinking, and issues related to data privacy. Finally, this paper looks forward to future development directions, emphasizing the transformation of teachers' roles and the necessity of a balanced combination of artificial intelligence and human wisdom to promote comprehensive innovation in foreign language teaching.

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.

Generative artificial intelligence(GenAI) like ChatGPT has been widely integrated into education, thereby empowering educators and gaining attention for human-machine collaborative education. The human-machine collaborative teaching ability has become one of the essential core competencies for teachers. There is an urgent need to explore the factors and mechanism to assist teachers in collaborating with GenAI more efficiently. This study carried out an empirical research, collecting multidimensional performances such as technological experience, technological beliefs, higher-order thinking, and prior knowledge from 44 preservice teachers. Based on the results of multiple linear regression analysis, we found that higher-order thinking and prior knowledge had a significant impact on the level of human-machine collaborative instructional design(H-M CID), while there was a negative impact relationship between technological beliefs and the level of higher-order thinking, as well as the technological experience. Those findings could lay the foundation for cultivating teachers’ H-M CID abilities.

In the era of generative artificial intelligence (GAI), the application of human-machine collaborative dialogue, grounded in GAI, holds significant promise within the educational and instructional domains. The integration of artificial intelligence with normal education has emerged as a pivotal topic in the cultivation of prospective teachers. This study has employed exploratory experiments with single pre-post measurements and epistemic network analysis to investigate the influence of human-machine collaborative dialogue on the dialectical reflection and instructional resource design capacities of normal students. The research reveals the following insights: Human-machine collaborative learning activities based on GAI can enhance the dialectical reflection ability of normal education students, and different human-machine collaborative behaviors have varying degrees of impact; The activities can effectively improve the instructional resource design capacity of normal education students, and different human-machine collaborative behaviors have differing effects; The human-machine collaborative dialogue based on GAI can indirectly affect students' instructional resource design capacity by changing their dialectical reflection ability. Drawing on these findings, the research offers recommendations on leveraging GAI to more effectively nurture the reflective and instructional resource design competencies of normal students.

In the field of French language learning, especially at the introductory stage, how to combine traditional teaching methods with artificial intelligence tools has become a research area that has attracted much attention. At present, artificial intelligence tools can already serve as qualified or even excellent teaching assistants, oral teachers, homework correction teachers, etc. So, what is the unique value of teachers? In what specific aspects is the irreplaceability of teachers’ learning in the introductory stage of French language reflected? After identifying these aspects, what teaching practices should be used to coordinate artificial intelligence tools in a more targeted manner to better complete teaching work? The research aims to explore the unique value and irreplaceability of teachers at the introductory stage of French. After finding out, explore how teachers should consolidate, emphasize and optimize their unique value; and study the application and application of artificial intelligence tools at this stage. The synergy between teachers' practices attempts to provide useful reference for teaching French at the introductory stage in the context of the great development of artificial intelligence tools.

The rapid development of artificial intelligence (AI) and digital transformation is driving higher education to shift from mass education to personalized education. However, current digital and intelligent technologies are primarily applied to teaching without fully exploring their potential. Thus, based on generative AI and knowledge graphs, a digital-intelligent collaborative teaching system is proposed to construct a novel intelligent teaching environment that supports active learning and collaborative teaching. The system focuses on the cultivation of talents in electronic information fields. Specifically, it leverages large models to develop a three-dimensional knowledge graph for professional courses, enabling the recommendation of personalized learning paths for students. Meanwhile, an intelligent formula derivation engine is designed to facilitate human-machine collaborative problem setting and solving, while establishing connections between knowledge and application based on the given problems. Moreover, a wireless communication agent incorporating teacher knowledge base is constructed to provide students with professional companion learning tools. The proposed system implemented over one semester in three classes with 144 students significantly enhances teaching quality and learning effectiveness, earning positive recognition from students. This provides a low-cost, high-efficiency digital-intelligent education model for new engineering education.

The advent of Generative Artificial Intelligence represents a watershed moment for educational paradigms across disciplines. Within the field of International Chinese Language Education, Generative Artificial Intelligence presents both profound opportunities and significant challenges, compelling a reconceptualization of traditional pedagogical models. This paper moves beyond the discourse of AI as a mere tool or replacement for educators, and instead, proposes a comprehensive "Teacher-AI" collaborative teaching paradigm. This paradigm is conceptualized as a synergistic ecosystem wherein the human teacher and AI systems engage in a dynamic partnership to foster a more personalized, efficient, and profound learning experience for students of Chinese as a second language. This inquiry is qualitative and theoretical, employing a conceptual analysis methodology. It begins by establishing a theoretical foundation grounded in Vygotsky's Sociocultural Theory, particularly the Zone of Proximal Development, and Siemens' Connectivism, arguing that these frameworks provide the necessary scaffolding for understanding a collaborative intelligence model. The core of the paper delineates the proposed paradigm, systematiComputer-Assisted Language Learningy redefining the roles of both the human teacher and the AI. The teacher's role evolves from a primary knowledge dispenser to that of a pedagogical architect, a humanistic mentor, a facilitator of complex interactions, and an assessor of deep competency. Concurrently, the AI is positioned as an indefatigable personalized tutor, a dynamic content co-creator, an immersive practice partner, and an intelligent assessment assistant. A three-stage implementation framework—encompassing pre-class preparation, in-class facilitation, and post-class consolidation—is detailed to provide a practical roadmap for integrating this model into International Chinese Language Education curricula. The paper further explores specific application scenarios, including personalized linguistic practice, simulated communicative contexts, enhancement of intercultural competence, and scaffolded academic writing. Finally, it discusses the pedagogical implications, potential challenges such as ethical considerations and the need for new teacher literacies, and charts directions for future research. This paper contributes a structured, theoretiComputer-Assisted Language Learningy-grounded framework for harnessing Generative Artificial Intelligence not as a disruptive force, but as a collaborative partner in advancing the mission of international Chinese language education.

This article theorizes Human-AI Collaborative Teaching as a co-teacher paradigm grounded in joint cognitive systems and reliability-first sociotechnical design, where instructional quality emerges from coupling, constraint and accountable orchestration rather than model fluency. The synthesis reframes teaching as real-time regulation under bounded rationality, linking distributed cognition and situated cognition to role-bearing AI participation across planning, enactment, assessment and reflection. It specifies a governance-ready architecture in which teacher authority is preserved through decision-rights partitioning, mixed-initiative interaction protocols, calibrated uncertainty signalling, and an abstain-escalate safety regime. Epistemic robustness is operationalized through provenance discipline, evidence-anchored feedback and contestability pathways that protect epistemic dignity, participation equity and multilingual-accessibility rights under high-stakes accountability. The article integrates constructs from learning sciences, human-computer interaction, resilience engineering, implementation science and public governance to produce five compact design instruments, a theoretical lens map, role-ecology contracts, interaction protocol patterns, a governance risk register, and an institutional maturity model for scalable adoption. The resulting framework offers concrete, globally portable implementation logic for policy makers, workforce development leaders, and educational technologists seeking audit-ready co-teaching infrastructures that enhance teacher noticing, strengthen formative inference and sustain assessment validity without privacy erosion, surveillance creep or de-skilling.

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Abstract Generative Artificial Intelligence (gen AI) is reshaping education by offering both opportunities and challenges for middle school teachers and students. This article examines the outcomes of a year-long professional learning community (PLC) focused on integrating gen AI in middle schools. Our interdisciplinary team of university faculty, doctoral students, and middle school teachers explores diverse applications of gen AI through teacher-led inquiry projects. We present a framework categorizing gen AI’s role in education from teaching AI concepts to using AI tools for pedagogy, ethical discussions, and teacher support. These projects illustrate gen AI’s potential to enrich teaching practices, foster critical thinking, and address equity in education while acknowledging implementation challenges. We conclude with lessons learned and recommendations for schools considering gen AI integrations.

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ABSTRACT This paper explores the pedagogical potential of Generative Artificial Intelligence (GenAI) in secondary education through a dialogic approach to teaching, learning and assessment. It presents an ongoing action research project in collaboration with a high school in Western Australia, involving four teachers to integrate GenAI in their classrooms. The study aims to develop and evaluate innovative pedagogies for leveraging GenAI to enhance educational practices and student learning outcomes across three action research teams focusing on critical questioning, assessment and differentiation. Drawing on Bakhtin’s concept of heteroglossia, the study conceptualizes GenAI not as a definitive knowledge provider but as a dialogic agent that facilitates collaborative dialogue and co-construction of knowledge among students. This perspective aims to encourage students to critically engage with AI-generated content and integrate multiple viewpoints into their learning, thus fostering key epistemic skills. Initial findings demonstrate active student engagement in dialogues with GenAI, highlighting the use of follow-up questions that indicate critical thinking and creativity. These findings underscore the significance of integrating multiple perspectives and fostering epistemic skills among students, promoting a comprehensive and ethical approach to AI use in education. The research calls for further exploration of GenAI’s pedagogic potential and its broader implications for educational practices, suggesting a promising avenue for pedagogical innovation and the development of critical thinking skills in the digital age.

The rise of generative artificial intelligence (GAI), especially with multimodal large language models like GPT‐4o, sparked transformative potential and challenges for learning and teaching. With potential as a cognitive offloading tool, GAI can enable learners to focus on higher‐order thinking and creativity. Yet, this also raises questions about integration into traditional education due to the limited research on learners' interactions with GAI. Some studies with GAI focus on text‐based human–AI interactions, while research on embodied GAI in immersive environments like mixed reality (MR) remains unexplored. To address this, this study investigates interaction dynamics between learners and embodied GAI agents in MR, examining cognitive and socio‐emotional interactions during collaborative learning. We investigated the paired interactive patterns between a student and an embodied GAI agent in MR, based on data from 26 higher education students with 1317 recorded activities. Data were analysed using a multi‐layered learning analytics approach, including quantitative content analysis, sequence analysis via hierarchical clustering and pattern analysis through ordered network analysis (ONA). Our findings identified two interaction patterns: type (1) AI‐led Supported Exploratory Questioning (AISQ) and type (2) Learner‐Initiated Inquiry (LII) group. Despite their distinction in characteristic, both types demonstrated comparable levels of socio‐emotional engagement and exhibited meaningful cognitive engagement, surpassing the superficial content reproduction that can be observed in interactions with GPT models. This study contributes to the human–AI collaboration and learning studies, extending understanding to learning in MR environments and highlighting implications for designing AI‐based educational tools. What is already known about this topic Socio‐emotional interactions are fundamental to cognitive processes and play a critical role in collaborative learning. Generative artificial intelligence (GAI) holds transformative potential for education but raises questions about how learners interact with such technology. Most existing research focuses on text‐based interactions with GAI; there is limited empirical evidence on how embodied GAI agents within immersive environments like Mixed Reality (MR) influence the cognitive and socio‐emotional interactions for learning and regulation. What this paper adds Provides first empirical insights into cognitive and socio‐emotional interaction patterns between learners and embodied GAI agents in MR environments. Identifies two distinct interaction patterns: AISQ type (structured, guided, supportive) and LII type (inquiry‐driven, exploratory, engaging), demonstrating how these patterns influence collaborative learning dynamics. Shows that both interaction types facilitate meaningful cognitive engagement, moving beyond superficial content reproduction commonly associated with GAI interactions. Implications for practice and/or policy Insights from the identified interaction patterns can inform the design of teaching strategies that effectively integrate embodied GAI agents to enhance both cognitive and socio‐emotional engagement. Findings can guide the development of AI‐based educational tools that capitalise on the capabilities of embodied GAI agents, supporting a balance between structured guidance and exploratory learning. Highlights the need for ethical considerations in adopting embodied GAI agents, particularly regarding the human‐like realism of these agents and potential impacts on learner dependency and interaction norms.

The development of new artificial intelligence-generated content (AIGC) technology creates new opportunities for the digital transformation of education. Teachers’ willingness to adopt AIGC technology for collaborative teaching is key to its successful implementation. This study employs the TAM and TPB to construct a model analyzing teachers’ acceptance of AIGC technology, focusing on the influencing factors and differences across various educational stages. The study finds that teachers’ behavioral intentions to use AIGC technology are primarily influenced by perceived usefulness, perceived ease of use, behavioral attitudes, and perceived behavioral control. Perceived ease of use affects teachers’ willingness both directly and indirectly across different groups. However, perceived behavioral control and behavioral attitudes only directly influence university teachers’ willingness to use AIGC technology, with the impact of behavioral attitudes being stronger than that of perceived behavioral control. The empirical findings of this study promote the rational use of AIGC technology by teachers, providing guidance for encouraging teachers to actively explore the use of information technology in building new forms of digital education.

The integration of disruptive technologies, including generative AI, into education science is profoundly transforming pedagogical practices and redefining the role of learners. The emergence of the connected learner marks an important breakthrough, where students become autonomous actors in their learning, thanks to constant access to online information and digital tools. This evolution is based on a reinvented pedagogy, which emphasizes learner-centered teaching, promotes active and collaborative learning, and allows for personalization of teaching through adaptive learning systems, often supported by artificial intelligence. Generative AI plays a crucial role in this transformation, making it possible to automatically create educational content tailored to students\' specific needs, such as quizzes, personalized summaries, or even interactive learning scenarios. This ability to generate content on demand offers new possibilities for more flexible, immersive and interactive learning, allowing students to have concrete and personalized experiences, such as simulations or virtual paths. However, this technological revolution raises several challenges, particularly in terms of unequal access to technology, teacher training, and the protection of student data. These ethical concerns must be addressed in order to ensure responsible and equitable adoption of these technologies. The main objective of this contribution is to highlight the challenges associated with the adoption of these technologies in the field of education, such as resistance to change, lack of resources, digital skills of teachers and learners, as well as ethical issues related to data protection. While generative AI and other technologies offer considerable potential to transform education, their implementation must be carefully thought out and balanced, in order to overcome inequalities in access and ethical issues.

As generative AI (GAI) tools become more advanced and adopted in professional user experience (UX) design fields, high school educators teaching UX and design-related courses have begun tinkering with GAI tools to use in their classrooms. Through interviews with high school UX educators, we examined their experiences incorporating GAI tools when teaching interdisciplinary UX courses. Key observations we identified include: 1) interactive student-to-student engagement and student-to-GAI collaborative learning; 2) modifications in brainstorming and visualization workflows with GAI; 3) concerns and barriers with the ongoing integration of GAI tools into high school curricula. We conclude with a discussion of implications and opportunities for critical and thoughtful use of GAI in high school design pedagogy.

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Primary mathematics education faces systemic challenges in translating curriculum reforms into classroom practice, exacerbated by teachers’ cognitive overload and limited support for pedagogical innovation. This study develops an Intelligent Teaching Design Assistant grounded in socio-constructivist and cognitive load theories to address these challenges. Thirty-four primary mathematics teachers participated in a quasi-experimental study. The Intelligent Teaching Design Assistant integrates Large Language Models with multi-dimensional knowledge bases (curriculum standards, teaching strategies, student profiles) and a multi-agent architecture (process planner, student simulator). The Intelligent Teaching Design Assistant significantly outperformed generic Large Language Models, improving overall lesson plan quality. This work pioneers a replicable pathway for AI to empower teacher agency and advance 21st-century educational transformation.

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To address the new challenges of translator training in the digital-intelligence era, the Advanced English course integrates constructivist and sociocultural theories to construct an “AI+HE” (Artificial Intelligence + Human Expertise) translation pedagogical model tailored for ethnic-minority institutions. Leveraging self-built MOOCs and knowledge graphs, the Jane intelligent agent dispatches authentic task packages—such as “ICH (Intangible Cultural Heritage) International Publicity” and “Eco-Inner Mongolia”—to support students in actively constructing translation competence through contextualized learning, thereby forming a “Teacher-Machine-Student” collaborative loop mediated by technology. Over the past three years, the course has cumulatively delivered 3.6 million words of ethnic-featured language services, covering English abstracts for the Journal of Mongolian Studies and Mongolian Language and Literature, video translation for the Community Education Pavilion, and interpreting practice for the China-Europe YES Project, thus forging a viable pathway oriented toward community discourse and serving regional international communication.

Despite the critical role of teachers in the educational process, few advanced learning technologies have been developed to support teacher-instruction or professional development. This lack of support is particularly acute for middle school math teachers, where only 37% felt well prepared to scaffold instruction to address the needs of diverse students in a national sample. To address this gap, the Advancing Middle School Teachers’ Understanding of Proportional Reasoning project is researching techniques to apply pedagogical virtual agents and dialog-based tutoring to enhance teachers' content knowledge and pedagogical content knowledge. This paper describes the design of a conversational, agent-based intelligent tutoring system to support teachers' professional development. Pedagogical strategies are presented that leverage a virtual human facilitator to tutor pedagogical content knowledge (how to teach proportions to students), as opposed to content knowledge (understanding proportions). The roles for different virtual facilitator capabilities are presented, including embedding actions into virtual agent dialog, open-response versus choice-based tutoring, ungraded pop-up sub-activities (e.g. whiteboard, calculator, note-taking). Usability feedback for a small cohort of instructors pursuing graduate studies was collected. In this feedback, teachers rated the system ease of use and perceived usefulness moderately well, but also reported confusion about what to expect from the system in terms of flow between lessons and support by the facilitator.

Immersive virtual learning environments can be used to teach a wide range of competences. Usually the learners are alone in such learning worlds. For help, they then necessarily turn to their class teacher. An alternative is the use of a smart agent as an aid. This approach is known as intelligent tutorship. The difference, however, lies in the immersive nature of the learning environment. This work is based on a literature review to identify requirements for smart agents in the context of a virtual classroom. In addition to multimodal conversation skills and pedagogical competences, the role as a central point of interaction and mentor/tutor is also part of this. The outer appearance (i.e., embodiment) should have a visible shape and be animated. The second part of the work deals with the practical conception of a smart agent. By means of a survey, the preferred visualization of a smart agent for a specific target group was identified. This showed that smart agents with human appearance were preferred.

We explore eXplainable AI (XAI) to enhance user experience and understand the value of explanations in AI-driven pedagogical decisions within an Intelligent Pedagogical Agent (IPA). Our real-time and personalized explanations cater to students' attitudes to promote learning. In our empirical study, we evaluate the effectiveness of personalized explanations by comparing three versions of the IPA: (1) personalized explanations and suggestions, (2) suggestions but no explanations, and (3) no suggestions. Our results show the IPA with personalized explanations significantly improves students' learning outcomes compared to the other versions.

Building upon previous research, this study aims to provide answers to the questions of how the presence of a humanoid artificial pedagogical agent as teacher and instructor and visual self-other representation of the learners through avatars influence the immediate cognitive performance and learning experience in online learning among adult learners.Several outcome measures were investigated to evaluate if effects are the same or different for the different experimental conditions and if learning with the pedagogical agent and visual self-other representation is modulated by the learner’s previous experiences with and preferences for online learning. Teacher presence and self-other presence of the learners were experimentally manipulated. A humanoid artificial agent, visible on all of the slides of the online course material and instructing the material represented the teacher. The avatars of the learners (self-avatar and peer avatars) were kept of minimal functionality but self-avatars were preselected or could be self-selected by the learners. The learner’s cognitive learning performance, the learner’s attention to the pedagogical agent, their sense of teacher presence and of self- and other-presence, their satisfaction with the course as well as the learner’s previous learning experiences were measured by cognitive testing, self-report, and linguistic analysis as major performance indicators and a positive learning experience. The analysis comprised 133 university students and results were additionally compared for two subsamples.Learning performance, learning satisfaction, and the attention paid to the teacher were positively related. In addition, positive evaluations of the cognitive presence elicited by the teacher were found. Self- or other-presence of avatars did not significantly influence the learner’s performance beyond teacher presence but the learner’s perception of it and their motivation to study online.The study and its results extend the previous literature that focused on the effects of pedagogical agents in online teaching or on virtual representations of the learner’s self and classmates in online learning. Despite limitations, the results of this study provide insights into combining teaching with artificial pedagogical agents and visual avatars for self-other representation during online teaching and the observations can serve as catalyst for future research.

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Introduction. Education quality management depends on the professional activity of teachers and requires a high level of their pedagogical skills. The key point in improving the quality of education and achieving educational goals is the teacher's mastery of pedagogical techniques. Aim. The aim of the work is to substantiate the importance of intelligent assistants in formation of a teacher's pedagogical technique. Methodology and research methods. The study is based on a systems approach; expert assessment methods, the "morphological box" method, a qualimetric approach and elements of statistical analysis were used. The article presents the results of an empirical study of the relationship between a teacher's pedagogical technique and educational outcomes of students, in which 1,653 teachers from 30 regions of Russia took part. The main method used was a survey containing 80 detailed positions on 15 risk factors for reducing the educational outcomes of students and a questionnaire for teachers to identify the relationship between the teacher's pedagogical technique and the educational outcomes of students. KEYWORDS Result and scientific novelty. 80 detailed items were identified for 15 risk factors for declining student educational outcomes across four groups of participants in educational relationships: teacher-student, teacher-teacher, teacher parent, and teacher-administration. The majority of respondents (744 people (45.0%)) believe that student educational outcomes depend on the teacher's pedagogical approach. The concept of an "intelligent system" in managing the development of a teacher's pedagogical approach in a general education organization is specified. This system is understood as a hardware and software system based on artificial intelligence methods and tools and focused on effective forms of student acquisition of knowledge about the subject area stored in the system's memory. The importance of intelligent systems in the educational process for shaping individual educational trajectories for the development of teachers' pedagogical approaches is substantiated. Practical significance. The developed intelligent assistants aimed at formation of a teacher’s pedagogical techniques "Govorun" and "KAMIRUPO" can be used both in a pedagogical university at the stage of training a future teacher, and by the administration of a general education organization to provide methodological assistance to working teachers.

Currently, poetry teaching in primary and secondary schools faces problems such as rigid teaching models, single teaching methods, and students' lack of emotional experience. To effectively address this dilemma, this study proposes a creative teaching model integrated with generative artificial intelligence (AI) technology. With "AI Poet" as an intelligent auxiliary tool, the core of this model lies in using natural language processing, virtual reality and other technologies to reconstruct the teaching process of "experience-interaction-creation" through approaches such as situational creation, virtual dialogue, intelligent analysis and poetic extension. Combined with the teaching practice case of Sending Yuan Er to Anxi, the study elaborates on the implementation methods and effects of this model. The results show that this model can effectively stimulate students' interest in poetry learning, promote the development of their innovative thinking and aesthetic ability, and drive the transformation of teaching objectives from knowledge transmission to core literacy cultivation. It provides a referential paradigm for the reform and innovation of poetry teaching in primary and secondary schools.

Human-AI collaboration in education represents a transformative shift in teaching and learning practices, offering potential to enhance educational outcomes through innovative teacher-AI partnerships. This review article provides a comprehensive analysis of the current literature on the integration of AI technologies in educational settings, focusing on the design and implementation of effective teacher-AI partnerships. The review synthesizes findings from recent studies, evaluates various AI tools and their applications, and identifies key factors for successful collaboration. The discussion highlights the impact of AI on teaching practices, student engagement, and learning outcomes, while also addressing challenges and ethical considerations. The article concludes with recommendations for designing effective teacher-AI partnerships to optimize educational experiences

Digital transformation in education has driven the integration of artificial intelligence (AI) as a key element in personalising learning, managing educational institutions, and supporting pedagogical decision-making processes. However, the application of AI also raises ethical challenges, access gaps, and fundamental changes in the role of teachers. This research aims to identify and classify the primary dimensions of human-AI collaboration in education through a qualitative approach, utilising a systematic literature review of 50 scientific articles published over the last five years. The articles were selected based on their thematic relevance from the Google Scholar and Scopus databases and analysed using NVivo software to cluster the dominant codes in the literature. The analysis resulted in four main components: Adaptive Learning with Artificial Intelligence (ALEAI), Artificial Intelligence in Education (AIED), Ethical Challenges in AI Education (EAIED), and Teacher Roles in AI-assisted learning (TRAIL). The findings indicate that AI has significant potential to enhance the efficiency and inclusivity of learning but also necessitates robust regulations in data protection, algorithm bias mitigation, and teacher training. This research contributes to the formulation of a conceptual framework for developing fair, ethical, and sustainable AI-based education policies.

The integration of artificial intelligence (AI) tools into professional development is reshaping the landscape for language teachers. Despite the growing presence of AI in language education, limited reviews have examined their specific impact on language teachers’ professional growth. This review addresses this gap by analysing empirical research, with a focus on ChatGPT. Using key search terms such as “ChatGPT language teaching” and “ChatGPT language teacher”, relevant studies were identified from the Scopus database and thematically analysed. Guided by Miao and Cukurova’s (2024) AI Competency Framework for Teachers, the review explores how language teachers engage with ChatGPT-assisted strategies for their professional development across three progression levels: Acquire, Deepen and Create. Findings reveal ChatGPT’s multifaceted role as a transformative tool for professional development. It enables language teachers to integrate technology into instructions, enhance teaching practices and engage in reflective knowledge reconstruction while fostering collaborative learning and co-creation. Furthermore, it supports professional adaptability by promoting AI literacy and equipping teachers to prepare students with the critical skills needed to navigate AI-enhanced educational environments. This research underscores the potential of human-AI collaboration to transform professional learning frameworks while fostering lifelong learning and adaptability in language education. It offers valuable insights into integrating AI into teacher development to encourage collective growth and advance professional practices.

Abstract The transformative potential of generative artificial intelligence (GenAI) in language education highlights the importance of AI literacy among teachers to ensure its ethical and effective integration into teaching practices. Although studies have examined the application of AI in language education, there is a lack of comprehensive reviews focusing on the interaction between language teachers and ChatGPT, a GenAI tool, particularly in fostering human–AI collaboration within educational contexts. This review addresses this gap by synthesising findings from empirical studies. The Scopus database was used as the primary source for this review. A total of 19 journal articles, published between 2023 and 2024, were identified. The review first analyses the research participants and research methods of the selected studies. Key themes are organised into five dimensions: AI foundations and applications, AI ethics, a human-centred mindset, AI pedagogy and AI for professional development, which are derived from the framework proposed by Miao and Cukurova (2024. AI competency Framework for teachers. Paris, France: UNESCO). This review adopts their framework as an analytical lens for evaluating both the opportunities and challenges associated with integrating ChatGPT into language education. The findings highlight the importance of a balanced approach to AI integration to safeguard educational integrity. By offering actionable insights for teachers, curriculum designers and policymakers, this review presents a roadmap for the responsible adoption of AI in language education, ensuring that teachers remain central to the learning process.

Abstract The global pandemic has brought about significant changes in education, which have led to concerns regarding fairness and accessibility in a technology-driven learning environment. This article focuses on the use of Artificial Intelligence (AI) in education and examines the potential for bias in AI-powered tools. By using the example of a first-year engineering student from India, it demonstrates how standardized tests and limited resources can generate skewed data that AI algorithms with bias could perpetuate. Strategies such as using diverse datasets, implementing explainable AI models, and including human oversight mechanisms can help mitigate this bias. While acknowledging challenges such as cost and technical limitations, the article highlights the opportunities that AI presents for personalized learning that benefits all students. Lastly, the article stresses the importance of collaboration between educators, policymakers, and AI developers in order to create ethical and equitable AI tools. It concludes by advocating for a future in which AI empowers learners and fosters a fair and just learning environment, prompting readers to consider the potential and responsibility associated with AI in education.

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With the development of Machine Learning, Visual Question Answering, Automatic speech recognition and other technologies, artificial intelligence technology is driving social change from all aspects. In the field of education, the development of AI has prompted the transformation of teacher roles and teaching forms. Since the 21st century, the research of educational technology has entered a period of explosion, and the academic circle has researched Human-machine collaboration empowering education which achieved rich results. To promote the further integration of Human-machine collaboration and education, this paper uses literature analysis and CiteSpace to summarize the development of Human-machine collaboration empowering education from three aspects: author distribution, talent highland, and Keyword network. Then the future trend is predicted by using the time graph of keywords.

Introduction The fact that the daily lives of billions of people were affected by the medical, social, and political aspects of the SARS-CoV-2 pandemic shows the need to anchor the understanding of One Health in society. Hence, promoting awareness and deepening the understanding of the interrelation between human health, animal health, and ecosystems must be accomplished through quality education, as advocated by UN Sustainable Development Goal 4. The often-questioned and discussed measures taken by governments to control the global pandemic between 2020 and 2023 can be seen as an opportunity to meet the educational needs of civil society solutions in multi-stakeholder settings between public, universities, and schools. Methods This paper focuses on the integration of One Health principles in educational frameworks, particularly within the context of the higher education teaching framework “Teaching Clinic.” This master-level course in the domain of pre-service teacher education serves as a potent vehicle for facilitating One Health Education, bridging the gap between research, higher education, and schools. Through the presentation of two case studies, this article demonstrates how the Teaching Clinic approach fosters interdisciplinary perspectives and provides a dynamic learning environment for pre-service teachers, as well as for pupils involved in the educational process. Results In both cases, the integration of educational One Health school teaching-learning settings effectively enhanced pupils’ understanding of complex topics and engaged them in active learning experiences. Pre-service teachers played a crucial role in developing, implementing, and evaluating these interventions. In Case I, pupils demonstrated proficiency in analyzing data and evaluating mathematical models, while in Case II, the chosen instructional approach facilitated One Health knowledge acquisition and enjoyment among pupils. These results underscore the potential of the One Health Teaching Clinic as a valuable educational framework for enhancing teaching and learning outcomes for pre-service teachers and fostering pupil engagement in socio-scientific One Health-related topics. Discussion The discussion delves into the significance of breaking down disciplinary silos and the crucial role of teacher education in promoting a holistic approach to education, emphasizing the intersectionality of One Health Education and Education for Sustainable Development. This article underpins the significance of collaborative efforts across multiple (scientific) disciplines and across secondary and tertiary education levels to reach a nexus. Moreover, it emphasizes the alignment of this approach with the 2030 Agenda, Education for Sustainable Development, and Sustainable Development Goals, highlighting the potential for collective action toward a more sustainable future.

Abstract Designing the teacher-student interaction model in online education is a significant research domain since it can assist teachers in preventing students from discontinuing their studies before final examinations and identifying students who need further support. Human computer interaction is all about solving issues and innovating, and students study how to find places of growth and then develop superior products and services. The observations extracted from the teacher-student interaction studies assisted The pupils' habits and mental states in studying and educational effectiveness are being improved. In this study, the Machine Learning-based Student Behaviour Data (ML-SBD) modeling algorithm is proposed to observe, analyze, and design the teacher-student interaction model. This research aims to forecast the challenges faced by students in a following digital design program. The information recorded by a Knowledge Boosted Learning (KBL) system based on Digital Electronics Study and Strategy Group (DESSG) has been analyzed using Machine Learning (ML) algorithms. The ML algorithms consist of Neural Networks (NNs), K-Nearest Neighbor (KNN), Binary Significance (BS), and support vector machines (SVMs) classifiers. The DESSG framework enables students, during recording input data, to solve digital design workouts with enormous complexity. Finally, the proposed ML algorithms significantly affect the student's success rate by effectively Designing the teacher-student interaction model combined with human computer interaction. The results observed from the developed ML system include more recommended solutions for improving students' success rate and academic efficiency. Dataset 8 has an 81.7% higher repeatability, 74.2% higher F1-measure repeatability, and 77.9% lower data loss than dataset 7 since it includes the case study method.

Generative artificial intelligence (GAI) has demonstrated remarkable potential in both educational practice and research, particularly in areas, such as personalized learning, adaptive assessment, innovative teaching methods, and cross-cultural communication. However, it faces several significant challenges, including the comprehension of complex domain knowledge, technological accessibility, and the delineation of AI's role in education. Addressing these challenges necessitates collaborative efforts from educators and researchers. This article summarizes the state-of-the-art large language models (LLMs) developed by various technology companies, exploring their diverse applications and unique contributions to primary, higher, and vocational education. Furthermore, it reviews recent research from the past three years, focusing on the challenges and solutions associated with GAI in educational practice and research. The aim of the review is to provide novel insights for enhancing human–computer interaction in educational settings through the utilization of GAI. Statistical analysis reveals that the current application of LLMs in the education sector is predominantly centered on the ChatGPT series. A key focus for future research lies in effectively integrating a broader range of LLMs into educational tasks, with particular emphasis on the interaction between multimodal LLMs and educational scenarios.