AI education

Artificial intelligence (AI) education is increasingly being recognized as essential at the K–12 level. For better understanding teachers’ preparedness for AI education and effectively developing relevant teacher training programs, teachers’ technological pedagogical content knowledge (TPACK) readiness and attitudes toward AI teaching must be determined. However, limited research has been conducted on this topic. To address this research gap, we recruited 1,664 K–12 teachers to obtain a comprehensive view of teachers’ readiness for and attitudes toward teaching AI in K–12 classrooms. These teachers differed in terms of their gender, teaching subject, teaching grade, teaching experience, and experience in teaching AI. The findings of this study indicated that a substantial gap exists in the AI-related content and technological knowledge of the recruited teachers. Moreover, intriguing relationships were found between the teachers’ pedagogical knowledge, content knowledge, and attitudes toward teaching AI. The effects of demographic factors on the teachers’ TPACK and attitudes were also examined. On the basis of the findings of this study, recommendations were formulated for developing effective teacher professional development programs in the field of AI education.

According to various international reports, Artificial Intelligence in Education (AIEd) is one of the currently emerging fields in educational technology. Whilst it has been around for about 30 years, it is still unclear for educators how to make pedagogical advantage of it on a broader scale, and how it can actually impact meaningfully on teaching and learning in higher education. This paper seeks to provide an overview of research on AI applications in higher education through a systematic review. Out of 2656 initially identified publications for the period between 2007 and 2018, 146 articles were included for final synthesis, according to explicit inclusion and exclusion criteria. The descriptive results show that most of the disciplines involved in AIEd papers come from Computer Science and STEM, and that quantitative methods were the most frequently used in empirical studies. The synthesis of results presents four areas of AIEd applications in academic support services, and institutional and administrative services: 1. profiling and prediction, 2. assessment and evaluation, 3. adaptive systems and personalisation, and 4. intelligent tutoring systems. The conclusions reflect on the almost lack of critical reflection of challenges and risks of AIEd, the weak connection to theoretical pedagogical perspectives, and the need for further exploration of ethical and educational approaches in the application of AIEd in higher education.

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The purpose of this study is multifold: First, to develop an educational program using artificial intelligence (AI) in middle school free semester system of South Korea. Second, to verify the program’s effectiveness, the study clarified the definition of AI and AI education and considered their meaning in technology education. This study used three steps: preparation, development, and improvement. In the preparation step, this study set the theme and purpose of the AI program and selected the free semester activity type “theme selection activity.” After analyzing the technology curriculum and extracting AI-related elements in the development step, this study laid out the program for 16 h of class time. In the improvement step, to augment the program’s validity, the researcher revised and supplemented it as a whole through expert consultation. This research differentiated the developed program from the AI education program of other subjects and specialized it, focusing on the specificity of technology education. The study emphasized the social impact of the latest technology, ethics of AI, physical computing using AI, and technological problem-solving activity using AI. The final developed program was applied to the students, and students participated in a pretest and posttest. The study used the PATT and AI competency test tools. The PATT results showed a significant increase in the mean of both constructs in “interest in technology” and “career aspirations in technology.” In AI competency, the mean of two constructs increased significantly in “social impact of AI” and “AI performance.” In particular, “AI performance” showed the largest increase. There was no statistically significant change in “interaction with AI.” The study results confirmed that the developed AI program was effective in technology education and career exploration, which is the primary purpose of the free semester. In addition, it was possible to confirm the technology educational value of the AI education program centered on technological problem-solving. These research results have implications for bringing AI into technology education.

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Summary Current and future healthcare professionals are generally not trained to cope with the proliferation of artificial intelligence (AI) technology in healthcare. To design a curriculum that caters to variable baseline knowledge and skills, clinicians may be conceptualized as “consumers”, “translators”, or “developers”. The changes required of medical education because of AI innovation are linked to those brought about by evidence-based medicine (EBM). We outline a core curriculum for AI education of future consumers, translators, and developers, emphasizing the links between AI and EBM, with suggestions for how teaching may be integrated into existing curricula. We consider the key barriers to implementation of AI in the medical curriculum: time, resources, variable interest, and knowledge retention. By improving AI literacy rates and fostering a translator- and developer-enriched workforce, innovation may be accelerated for the benefit of patients and practitioners.

Artificial Intelligence (AI) literacy is a crucial part of digital literacy that all individuals should possess in today's technologically advanced world. Despite the potential benefits that AI education offers, little research has been done on how to teach AI literacy to children.This study aimed to fill that gap by investigating how children were engaged in AI literacy activities that are supported by intelligent agents. These activities were implemented in a Hong Kong kindergarten with a class of six 5‐year‐olds (Mage = 62.83 months, SD = 2.91) over a 6‐week period.The study gathered data from multiple sources, including classroom observations, teacher interviews, and documents/artefacts.The results showed that children could learn about AI through interaction with intelligent agents in embodied learning contexts. The findings of this study have implications for the broader field of digital technology education, particularly in the context of early childhood education.

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This study comprehensively analyzed the current status and characteristics of China's artificial intelligence (AI) education policy. China has emerged as a global AI powerhouse, ranking first worldwide in generative AI patent applications from 2014 to 2023 and officially approving over 80 large language models. The government actively supports AI technological self-reliance and industrial development through its ‘AI+ Industry’ strategy and large-scale national investment funds, with approximately 4,500 AI companies currently operating. In the K-12 education sector, AI education will be mandatory for all elementary, middle, and high school students nationwide by 2025. The curriculum centers on four core competencies: computational thinking, digital learning and innovation, information awareness, and social responsibility. The curriculum progressively deepens from introductory AI technology experiences for lower elementary grades to AI algorithm design in high school. Beijing and Shanghai are leading innovative educational models, such as spiral curriculum design and AI-based educational governance. In higher education, efforts focus on cultivating world-class universities through the ‘Double First-Class Initiative’ and developing ‘AI+X’ convergence talent. Leading universities like Tsinghua University and Fudan University are spearheading AI convergence education models. China is enhancing global competitiveness across all AI domains by combining government support, private sector technological capabilities, and AI-centered innovation within the education system. This serves as a core strategy for securing national development momentum.

The exponential growth of artificial intelligence (AI) in the last two decades has been recognized by many as an opportunity to improve the quality of patient care. However, medical education systems have been slow to adapt to the age of AI, resulting in a paucity of AI-specific education in medical schools. The purpose of this systematic review is to evaluate the current evidence-based recommendations for the inclusion of an AI education curriculum in undergraduate medicine. Six databases were searched from inception to April 23, 2022 for cross sectional and cohort studies of fair quality or higher on the Newcastle-Ottawa scale, systematic, scoping, and integrative reviews, randomized controlled trials, and Delphi studies about AI education in undergraduate medical programs. The search yielded 991 results, of which 27 met all the criteria and seven more were included using reference mining. Despite the limitations of a high degree of heterogeneity among the study types and a lack of follow-up studies evaluating the impacts of current AI strategies, a thematic analysis of the key AI principles identified six themes needed for a successful implementation of AI in medical school curricula. These themes include ethics, theory and application, communication, collaboration, quality improvement, and perception and attitude. The themes of ethics, theory and application, and communication were further divided into subthemes, including patient-centric and data-centric ethics; knowledge for practice and knowledge for communication; and communication for clinical decision-making, communication for implementation, and communication for knowledge dissemination. Based on the survey studies, medical professionals and students, who generally have a low baseline knowledge of AI, have been strong supporters of adding formal AI education into medical curricula, suggesting more research needs to be done to push this agenda forward.

In response to the growing popularity of artificial intelligence (AI) usage in daily life, AI education is increasingly being provided at the K-12 level, with relevant initiatives being launched worldwide. Examining how these programs have been implemented and summarizing useful experiences is thus imperative. Although prior reviews have described the characteristics of AI education programs in publications, the papers reviewed were mostly nonempirical reports, and the analysis typically only involved a descriptive summary. The current review focuses on the most recent empirical studies on AI teaching programs in K-12 contexts through a systematic search of the Web of Science database from 2010 to 2022. To provide a comprehensive overview of the status of AI teaching and learning (T&L), 32 empirical studies were analyzed both descriptively and thematically. We analyzed (1) the research status, (2) the pedagogical design, and (3) the assessments and outcomes of the AI teaching programs. An increasing number of studies have focused on AI education at the K-12 stage, but most of them have a small sample size. Moreover, the data were mostly collected through interviews and self-reports. We reviewed the pedagogical design of AI teaching programs by using Gerlach and Ely’s pedagogical design model. The results comprehensively delineated current AI teaching programs through nine dimensions: learning theory, pedagogical approach, T&L activities, learning content, scale, teaching resources, prior knowledge prerequisite, aims and objectives, assessment, and learning outcome. The results highlighted the positive impact of current AI teaching programs on students’ motivation, engagement, and attitude. However, we observed a lack of sufficient research objectively measuring students’ knowledge acquisition as learning outcomes. Overall, in this paper, we discussed relevant findings in terms of research trends, learning content, teaching units, characteristics of the pedagogical design, and assessment and evaluation by providing illustrations of exemplary designs; we also discussed future directions for research and practice in AI education in the K-12 context.

Learning about Artificial Intelligence (AI) from a young age can help students become competent citizens able to move through our increasingly digital world with confidence and responsibility. This contribution presents a preliminary investigation in Bulgaria, Greece, Italy, and Romania to understand middle school teachers’ perspective on how to best teach digital competencies for AI. It uses the Will, Skill, Tool model as a theoretical lens, and it aims to inform the design of educational content and online platforms to enable teachers to integrate AI education into their classroom. Through a human-centred design process – including focus groups and a survey – needs and requirements were identified for a supportive online educational platform that aids teachers in AI education. The research results showed a positive attitude towards AI education and high motivation to introduce AI-related content at school, which translates to a positive Will factor. Regarding the Skill factor, teachers seem to have a basic level of digital skills but low AI-related skills. No significant problems emerged regarding the availability of resources, but further research investigating whether the Tool factor is accounted for would be desirable. Based on the results, six design implications for a web-based educational platform on AI have been formulated: (i) provide the required basics; (ii) make it relevant; (iii) make it interactive and collaborative; (iv) keep everyone in the loop; (v) make it accessible; (vi) motivate the user. These implications are further discussed to extend computational thinking frameworks to incorporate AI-related concepts and perspectives.

Background As the adoption of artificial intelligence (AI) in health care increases, it will become increasingly crucial to involve health care professionals (HCPs) in developing, validating, and implementing AI-enabled technologies. However, because of a lack of AI literacy, most HCPs are not adequately prepared for this revolution. This is a significant barrier to adopting and implementing AI that will affect patients. In addition, the limited existing AI education programs face barriers to development and implementation at various levels of medical education. Objective With a view to informing future AI education programs for HCPs, this scoping review aims to provide an overview of the types of current or past AI education programs that pertains to the programs’ curricular content, modes of delivery, critical implementation factors for education delivery, and outcomes used to assess the programs’ effectiveness. Methods After the creation of a search strategy and keyword searches, a 2-stage screening process was conducted by 2 independent reviewers to determine study eligibility. When consensus was not reached, the conflict was resolved by consulting a third reviewer. This process consisted of a title and abstract scan and a full-text review. The articles were included if they discussed an actual training program or educational intervention, or a potential training program or educational intervention and the desired content to be covered, focused on AI, and were designed or intended for HCPs (at any stage of their career). Results Of the 10,094 unique citations scanned, 41 (0.41%) studies relevant to our eligibility criteria were identified. Among the 41 included studies, 10 (24%) described 13 unique programs and 31 (76%) discussed recommended curricular content. The curricular content of the unique programs ranged from AI use, AI interpretation, and cultivating skills to explain results derived from AI algorithms. The curricular topics were categorized into three main domains: cognitive, psychomotor, and affective. Conclusions This review provides an overview of the current landscape of AI in medical education and highlights the skills and competencies required by HCPs to effectively use AI in enhancing the quality of care and optimizing patient outcomes. Future education efforts should focus on the development of regulatory strategies, a multidisciplinary approach to curriculum redesign, a competency-based curriculum, and patient-clinician interaction.

The teaching of artificial intelligence (AI) topics in school curricula is an important global strategic initiative in educating the next generation. As AI technologies are new to K-12 schools, there is a lack of studies that inform schools’ teachers about AI curriculum design. How to prepare and engage teachers, and which approaches are suitable for planning the curriculum for sustainable development, are unclear. Therefore, this case study aimed to explore the views of teachers with and without AI teaching experience on key considerations for the preparation, implementation and continuous refinement of a formal AI curriculum for K-12 schools. It drew on the self-determination theory (SDT) and four basic curriculum planning approaches—content, product, process and praxis—as theoretical frameworks to explain the research problems and findings. We conducted semi-structured interviews with 24 teachers—twelve with and twelve without experience in teaching AI—and used thematic analysis to analyze the interview data. Our findings revealed that genuine curriculum creation should encompass all four forms of curriculum design approach that are coordinated by teachers’ self-determination to be orchestrators of student learning experiences. This study also proposed a curriculum development cycle for teachers and curriculum officers.

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Over the past decades, numerous practical applications of machine learning techniques have shown the potential of AI-driven and data-driven approaches in a large number of computing fields. Machine learning is increasingly included in computing curricula in higher education, and a quickly growing number of initiatives are expanding it in K–12 computing education, too. As machine learning enters K–12 computing education, understanding how intuition and agency in the context of such systems is developed becomes a key research area. But as schools and teachers are already struggling with integrating traditional computational thinking and traditional artificial intelligence into school curricula, understanding the challenges behind teaching machine learning in K–12 is an even more daunting challenge for computing education research. Despite the central position of machine learning and AI in the field of modern computing, the computing education research body of literature contains remarkably few studies of how people learn to train, test, improve, and deploy machine learning systems. This is especially true of the K–12 curriculum space. This article charts the emerging trajectories in educational practice, theory, and technology related to teaching machine learning in K–12 education. The article situates the existing work in the context of computing education in general, and describes some differences that K–12 computing educators should take into account when facing this challenge. The article focuses on key aspects of the paradigm shift that will be required in order to successfully integrate machine learning into the broader K–12 computing curricula. A crucial step is abandoning the belief that rule-based “traditional” programming is a central aspect and building block in developing next generation computational thinking.

The article analyzes the role of artificial intelligence (AI) in the transformation of the educational sphere in the context of achieving the Sustainable Development Goals (SDGs). The purpose of the study is a comprehensive assessment of the potential benefits and risks of introducing AI into education in terms of their impact on sustainable development. Tasks include: identification of key AI application opportunities to improve the quality, accessibility, and inclusivity of education (SDG 4); analysis of issues related to ethics, algorithm bias, digital inequality, and the need for retraining; assessment of the impact of AI on the formation of competencies necessary for a sustainable future. The results show the significant potential of AI in personalizing learning, automating routine tasks, and providing access to knowledge, but also reveal serious risks of exacerbating social inequality (SDG 10) and ethical dilemmas. The scientific novelty lies in the systematic consideration of the relationship between the introduction of AI in education and the achievement of the SDGs, offering a balanced view of the challenges and opportunities. The practical significance lies in the formulation of recommendations for policy makers, educators, and developers on responsible and sustainability-oriented implementation of AI in educational systems.

The present discussion examines the transformative impact of Artificial Intelligence (AI) in educational settings, focusing on the necessity for AI literacy, prompt engineering proficiency, and enhanced critical thinking skills. The introduction of AI into education marks a significant departure from conventional teaching methods, offering personalized learning and support for diverse educational requirements, including students with special needs. However, this integration presents challenges, including the need for comprehensive educator training and curriculum adaptation to align with societal structures. AI literacy is identified as crucial, encompassing an understanding of AI technologies and their broader societal impacts. Prompt engineering is highlighted as a key skill for eliciting specific responses from AI systems, thereby enriching educational experiences and promoting critical thinking. There is detailed analysis of strategies for embedding these skills within educational curricula and pedagogical practices. This is discussed through a case-study based on a Swiss university and a narrative literature review, followed by practical suggestions of how to implement AI in the classroom.

This systematic review provides unique findings with an up-to-date examination of artificial intelligence (AI) in higher education (HE) from 2016 to 2022. Using PRISMA principles and protocol, 138 articles were identified for a full examination. Using a priori, and grounded coding, the data from the 138 articles were extracted, analyzed, and coded. The findings of this study show that in 2021 and 2022, publications rose nearly two to three times the number of previous years. With this rapid rise in the number of AIEd HE publications, new trends have emerged. The findings show that research was conducted in six of the seven continents of the world. The trend has shifted from the US to China leading in the number of publications. Another new trend is in the researcher affiliation as prior studies showed a lack of researchers from departments of education. This has now changed to be the most dominant department. Undergraduate students were the most studied students at 72%. Similar to the findings of other studies, language learning was the most common subject domain. This included writing, reading, and vocabulary acquisition. In examination of who the AIEd was intended for 72% of the studies focused on students, 17% instructors, and 11% managers. In answering the overarching question of how AIEd was used in HE, grounded coding was used. Five usage codes emerged from the data: (1) Assessment/Evaluation, (2) Predicting, (3) AI Assistant, (4) Intelligent Tutoring System (ITS), and (5) Managing Student Learning. This systematic review revealed gaps in the literature to be used as a springboard for future researchers, including new tools, such as Chat GPT. A systematic review examining AIEd in higher education (HE) up to the end of 2022. Unique findings in the switch from US to China in the most studies published. A two to threefold increase in studies published in 2021 and 2022 to prior years. AIEd was used for: Assessment/Evaluation, Predicting, AI Assistant, Intelligent Tutoring System, and Managing Student Learning.

Abstract The increase of generative artificial intelligence (GenAI) in education has emphasized prompt literacy and design, influencing the experience of GenAI-assisted learning. However, there is limited understanding regarding the various prompt patterns that emerge during student-GenAI interaction (SAI) within the context of navigating learning tasks at different levels of AI Literacy or how these patterns are associated with task performance. This study analyzed 19 university students’ prompt patterns when interacting with GenAI on academic writing tasks, grouping students by AI literacy level and examining how prompt patterns relate to performance. This study performed content analysis of student-GenAI chat histories and categorical data analysis of student interviews. Pattern differences were then visualized using Gephi 0.10.1. Distinctive prompt patterns were identified: high AI literacy students exhibited descriptive, context-based prompts in collaborative interactions, while low AI literacy students demonstrated general prompts in a student-directed approach. Additionally, student essays were evaluated by five experts to inform how prompt patterns relate to task performance and the Wilcoxon signed-rank test was applied. The two groups demonstrated significant differences across categories of writing performance (content, structure, and expression). This study provides implications for designing and implementing GenAI and student-centered AI-assisted instruction.

Artificial intelligence (AI) technologies are advancing swiftly, both in terms of quantity and quality. Students must possess a solid understanding, abilities, and skills to successfully employ them. Therefore, the purpose of this quantitative study is to examine students’ AI literacy, including AI technological understanding, its practical application, critical appraisal, and its effects on students’ AI output quality, self-efficacy, and academic performance. Moreover, this research examines the effects of students’ AI self-efficacy and output quality on their academic achievement. An online survey was used to gather responses from 286 Austrian university students. Structural equation modeling was performed to test the research model. The findings indicate that AI technical understanding and the practical application significantly and positively influenced AI self-efficacy, while AI practical application had a significant and positive influence on both, AI self-efficacy and AI output quality. AI critical appraisal significantly and negatively impacted AI self-efficacy and AI output quality, whereas AI technical understanding, critical appraisal, practical application, self-efficacy, and output quality had an insignificant effect on students’ academic performance. The findings of this study may deepen the understanding of students’ AI literacy, contribute to the literature in this relatively new academic field, and help clarify the contradictory findings on the effects of AI-powered technology use on students’ learning outcomes. This research can also help integrate AI-powered tools into education effectively and improve students’ AI literacy so they can use them ethically and effectively. The results may also help revise and create new AI policies, curricula, and educational initiatives.

With the rapid development of Artificial Intelligence (AI) technology, the application of AI in the field of education has gradually become one of the key factors in improving teaching quality and student abilities. Based on the conservation of resources theory, this study explores how the usage of AI in teaching impacts students’ creativity, exploring the mediating role of learning engagement and the moderating role of AI literacy. The research finds that the usage of AI in teaching significantly enhances students’ creativity, with learning engagement playing a mediating role in this process, thereby promoting creativity improvement. In addition, AI literacy moderates the relationship between the usage of AI in teaching and learning engagement. The results of this study not only expand the application of the conservation of resources theory in the field of education but also emphasize the important role of AI literacy in AI teaching, providing valuable policy suggestions for educational practices.

Introduction Artificial Intelligence (AI) has made its way into every dimension of human life, and its impact is significant and multifaceted. Specifically, the effect of AI in nursing education has reshaped the healthcare system. However, this technological shift in nursing and the healthcare system still needs to be evaluated in multiple aspects to ensure the effective use of AI and to prepare future professionals. Methods This PROSPERO-registered systematic literature review and meta-analysis explored the integration of AI literacy and competency within nursing curricula and the profession globally from January 2020 to June 2025. The study specifically aimed to: (1) examine the extent of AI integration within nursing curricula; (2) assess the awareness, attitudes, and readiness of nursing students, faculty, and practitioners toward AI; (3) evaluate the effectiveness of educational interventions designed to enhance AI literacy and competency; (4) identify ethical, institutional, and pedagogical challenges associated with AI adoption in nursing education; and (5) provide evidence-based recommendations for standardized and equitable AI education frameworks in nursing. Results The review synthesizes evidence from 111 peer-reviewed articles, including 18 distinct quantitative studies, which have been further analyzed through meta-analytic techniques. PRISMA guidelines were followed to search for relevant articles and extract the required information. Meta-analysis reveals high levels of AI-related awareness (pooled estimate = 73%, 95% CI: 64–80%) and positive attitudes (71%, 95% CI: 63–78%) among various nursing groups. The implementation of AI-related skills remains highly variable (67%, 95% CI: 55–78%), especially in low-resource settings, which needs careful interpretation. Overall, meta-analysis findings highlight significant variations and reflect non-uniformity and disparities across regions, institutions, and nursing groups (students, staff, faculty). Conclusion Thematic synthesis underscores the need for standardized AI education, tailored faculty development, and equitable access to digital tools. Although individual-level awareness and attitudes toward AI are promising, this review reveals a lack of institutional readiness, with many nursing programs lacking standardized curricula, faculty training, and infrastructural support. Moreover, findings emphasize the critical need for broader institutional and policy efforts to match individual enthusiasm with institutional capacity in preparing nurses for an AI-enabled healthcare landscape. Further, this review offers evidence-based recommendations for various stakeholders to ensure inclusive and future-ready nursing education. Systematic review registration https://www.crd.york.ac.uk/prospero/, identifier CRD420251090108.

As digital entrepreneurship accelerates, aspiring founders increasingly rely on artificial intelligence (AI) to drive innovation. This study examines how AI literacy—defined as the ability to identify, use, and evaluate AI tools—influences digital entrepreneurial intention. Based on Social Cognitive Career Theory (SCCT), we propose a moderated mediation model, where digital entrepreneurial self-efficacy mediates the relationship between AI literacy and entrepreneurial intention, and AI self-efficacy moderates this pathway. Survey data from 1,061 Vietnamese university students were analyzed using the PROCESS macro. Results reveal that AI literacy positively predicts entrepreneurial intention both directly and indirectly via increased self-efficacy. Furthermore, AI self-efficacy strengthens the link between AI literacy and entrepreneurial self-efficacy, amplifying its mediated impact on intention. These findings extend SCCT to the AI-enabled entrepreneurship domain, emphasizing the joint role of competence and confidence. Practical implications highlight the need for entrepreneurship education to integrate AI skill development and foster technological self-belief among students.

In the rapidly evolving landscape of artificial intelligence (AI), understanding the factors that influence individuals’ intentions to adopt AI technologies is crucial, particularly within educational contexts. This study addresses a critical gap in the literature by examining how AI literacy interacts with the constructs of the Unified Theory of Acceptance and Use of Technology (UTAUT)—Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions—to shape university students’ behavioral intentions to adopt AI technologies. Data from 359 Chinese university students were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings reveal that AI Literacy is a significant predictor of Behavioral Intention to adopt AI, with Social Influence, Performance Expectancy, and Effort Expectancy serving as important mediators. Notably, Facilitating Conditions did not have a significant effect on Behavioral Intention in this context. The results underscore the importance of enhancing AI Literacy among university students to foster positive adoption intentions, particularly through social and expectancy-related factors, providing practical implications for educators and policymakers aiming to promote AI integration in higher education.

This study examines Chinese university students’ adoption of generative artificial intelligence (GenAI) tools by integrating the Theory of Planned Behavior (TPB), the Technology Acceptance Model (TAM), and AI literacy dimensions into a hybrid framework. Survey data from 1006 students across various majors and regions are analyzed using partial least squares structural equation modeling. Notably, AI literacy (i.e., students’ AI ethics, evaluation, and awareness) positively affect their attitudes, subjective norms, and perceived behavioral control, although the influence patterns vary according to the literacy dimension. Perceived privacy risks reduce AI trust, which mediates adoption behavior. Overall, core TPB pathways are validated, with behavioral intentions significantly predicting students’ actual use. Gender and regional differences moderate the key relationships. The results of this study suggest that enhancing students’ ethical and evaluative competencies, building user trust, and addressing privacy concerns could promote generative AI integration in education.

As artificial intelligence (AI) increasingly influences education, understanding learners' experiences, engagement and literacy of these tools is critical. This study explores AI literacy among Turkish EFL (English as a Foreign Language) students regarding their familiarity, knowledge, and ethical perceptions of AI technologies in academic writing. Using a descriptive exploratory approach, the study surveyed 427 students from two Turkish universities. Findings reveal a moderate level of AI familiarity and usage among participants, with a significant reliance on AI tools for translation and grammar proofreading. Despite recognizing AI's potential to enhance academic writing, students exhibited limited technical proficiency and understanding of AI's underlying mechanisms, highlighting a need for targeted and structured AI education for EFL writing. The findings contribute to the ongoing discourse on AI integration in EFL education, offering insights for policymakers, educators, and researchers to better prepare students for an AI-driven academic environment.

As Artificial Intelligence (AI) continues to influence various aspects of society, the need for AI literacy education for K-12 students has grown. An increasing number of AI literacy studies aim to enhance students’ competencies in understanding, using, and critically evaluating AI systems. However, despite the vulnerabilities faced by students from underserved communities—due to factors such as socioeconomic status, gender, and race—these students remain underrepresented in existing research. To address this gap, this study focuses on leveraging the cultural capital that students acquire from their communities’ unique history and culture for AI literacy education. Education researchers have demonstrated that identifying and mobilizing cultural capital is an effective strategy for educating these populations. Through collaboration with 26 students from underserved communities—including those who are socioeconomically disadvantaged, female, or people of color—this paper identifies three types of cultural capital relevant to AI literacy education: 1) resistant capital, 2) communal capital, and 3) creative capital. The study also emphasizes that collaborative relationships between researchers and students are crucial for mobilizing cultural capital in AI literacy education research.

Amid accelerating digital transformation, students in vocational colleges face increasing uncertainty about their future career prospects. Grounded in the Cognitive Appraisal Theory (CAT) and Social Cognitive Career Theory (SCCT), this study proposed and tested a dual-path mediation model in which transformational leadership (TL) in educational settings influenced students’ future work readiness (FWR) through two mechanisms: AI literacy and career self-regulation (career control and decision-making difficulties) factors. The structural equation modeling and bootstrapped mediation analyses using data from 697 Chinese vocational college students revealed that TL indirectly enhanced career optimism and reduced future work anxiety through these mediators. Taken together, the findings are consistent with a full-mediation account: indirect effects were significant, and the direct links from TL to CO and to FWA attenuated to non-significance once the mediators were included. The findings positioned AI literacy as a critical cognitive bridge linking educational leadership to vocational psychological outcomes, and extended theoretical models of future work readiness and psychological adaptation to digitally evolving educational contexts.

Abstract Generative artificial intelligence (GAI) advancements have ignited new expectations for artificial intelligence (AI)-enabled educational transformations. Based on the theory of planned behavior (TPB), this study combines structural equation modeling and interviews to analyze the influencing factors of Chinese university students’ GAI technology usage intention. Regarding AI literacy, students’ cognitive literacy in AI ethics scored the highest (M = 5.740), while AI awareness literacy scored the lowest (M = 4.578). Students’ attitudes toward GAI significantly and positively influenced their usage intention, with the combined TPB framework and AI literacy explaining 59.3% of the variance. AI literacy and subjective norms positively influenced students’ attitudes toward GAI technology and perceived behavioral control, and attitude mediated the impact of AI literacy and subjective norms on GAI usage intention. Further, the interviews provide new insights for university management and educational leadership regarding the construction of an educational ecosystem under the application of GAI technology.

This study adopted an exploratory approach to investigate the nuances of AI literacy among Library and Information Science (LIS) students in South Asia namely Bangladesh, India and Pakistan. A total of 632 respondents from these countries participated in an online survey that explored their level of AI literacy and familiarity with AI tools and technologies, purposes of using AI tools, ethical perceptions and how AI-related contents were covered in LIS courses and programmes. The study results indicate that students are moderately familiar with AI tools, but the degree of their self-rated AI literacy ranges from basic to advanced. Students reported using AI tools for academic purposes, including information searching, summarising articles, generating ideas and writing academic papers. However, participant LIS students expressed concerns about the ethical usage of AI and Generative AI tools, particularly academic integrity and plagiarism in academic writing. The results underscore the need for more robust AI literacy education in South Asian LIS education programmes – and potentially globally – to deepen students’ understanding and critical engagement with AI tools and technologies. This would better equip them for emerging roles in AI-integrated library services, highlighting a key direction for curriculum development, training methodologies and policy initiatives within LIS education and library and information management profession.

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AIM This study investigates the mediating role of artificial intelligence self-efficacy (AISE) in the association between AI literacy and learning engagemeg nursing students in higher education. BACKGROUND The fourth technological revolution driven by AI has profound implications for healthcare education. Insufficient AI literacy and AISE among nursing students may hinder their learning engagement, yet empirical evidence on these relationships remains limited. DESIGN A cross-sectional study. METHOD A convenience sampling method was used to select 2029 nursing students from 29 colleges in China from November to December 2024. A general information questionnaire, AI literacy scale, AISE scale and learning engagement scale were used to conduct the online questionnaire survey. Data were analyzed using descriptive analysis, Pearson correlation test and Bootstrap method. RESULTS Results show that AI literacy was positively correlated with AISE and learning engagement (r = 0.462-0.435, P＜0.01) and AISE was positively correlated with learning engagement (r = 0.537, P＜0.01). The indirect effect of AI literacy on learning engagement through AISE was 0.130, accounting for 20.44 % of the total effect. CONCLUSION The AI literacy of nursing students is suboptimal; thus, enhancing foundational AI education is essential. AI literacy directly predicts learning engagement, with AISE serving as a partial mediator. Educators should integrate AI training into curricula, provide practical scenarios and foster interdisciplinary collaboration to improve AI competency and engagement.

Abstract Although introducing AI education into K-12 schools has attracted significant interest from educators and researchers, research into effective strategies for enhancing elementary students’ AI literacy remains in its infancy. Grounded in design-based learning, we propose a pedagogical framework, “Students as AI literate designers” (SAILD), to facilitate young learners’ acquisition of AI literacy by designing solutions for real-world problem-solving. This paper reports on our mixed-methods study evaluating the effectiveness of SAILD in Grade-5 students’ AI literacy learning and teaching in Hong Kong, regarding AI knowledge, skills, ethics, and attitudes. The quantitative findings show significant differences in students’ AI knowledge, ethics, and attitudes between pre- and post-test scores; the qualitative findings reveal students’ problem-solving processes using AI skills. While the present work provides empirical evidence supporting the adoption of SAILD in elementary AI education, it underlines the challenges of SAILD and suggests areas for further research.

As artificial intelligence (AI) becomes increasingly integrated into healthcare, AI literacy has emerged as an essential competency for future healthcare professionals. However, research exploring healthcare students’ AI literacy remains limited. This study aimed to assess AI literacy among healthcare students and examine its associations with attitudes toward AI, intention to use AI in clinical contexts, and students’ characteristics (interest in AI and previous AI training) within a Korean context. A cross-sectional survey was conducted at a Korean medical school. Of 690 invited students, 91 (67 medical, 24 nursing) participated. Variables were measured using the Scale for Non-Expert AI Literacy (SNAIL), the General Attitudes toward Artificial Intelligence Scale, and intention to use AI items adapted from the Technology Acceptance Model. Analyses included confirmatory factor analysis, descriptive statistics, t-tests, and correlation analyses. After removing seven items, the validated 24-item SNAIL-KR was used for analysis. Students demonstrated slightly below-average AI literacy, with technical understanding scoring the lowest. AI literacy was positively correlated with positive attitudes toward AI and intention to use AI. Interest in AI was more strongly correlated with AI literacy than prior AI training. No significant differences were found by gender, year of study, or major. The findings highlight the urgent need to incorporate AI literacy into healthcare curricula in a consistent and systematic manner. Fostering healthcare students’ positive attitudes toward and interest in AI is also crucial for enhancing AI literacy.

Fostering Artificial Intelligence (AI) literacy and equipping college students with 21st-century skills in the generative AI era have become a global educational priority. In this context, generative AI offers opportunities for development in higher education institutions. Thus, this study investigates the influence of AI literacy and 21st-century skills on generative AI acceptance.For data collection, the study employed a quantitative design with three scales, and the study sample included 260 college students selected randomly.Results revealed that AI literacy and 21st-century skills are present at a moderate level among college students. AI literacy and 21st-century skills influence the generative AI Acceptance level.Based on the results, the study recommends enriching the curriculum with AI literacy and equipping students with 21st-century skills while using generative AI applications.

AIMS To examine the interrelationships among artificial intelligence (AI) literacy, AI self-efficacy and AI self-competency and to investigate the mediating role of AI self-efficacy. BACKGROUND AI literacy is increasingly explored in primary and higher education, yet remains underexamined in nursing. In particular, the role of AI self-efficacy as a mediator between AI literacy and AI self-competency has not been fully established. DESIGN A cross-sectional, correlational study. METHODS A total of 434 nursing students were consecutively recruited. Data were collected from November 2024 to February 2025 using three standardized self-report scales. Structural equation modeling (SEM) was used to analyze the data. RESULTS Nursing students' AI literacy positively influenced on AI self-efficacy (β=0.54, p = 0.001) and AI self-competency (β=0.13, p = 0.003). AI self-efficacy directly associated with AI self-competency (β=0.64, p = 0.001). AI literacy had indirect positive effect on AI self-competency (β=0.34, p = 0.001) through the mediating effect of AI self-efficacy. AI literacy explained 28.85 % of the variance of AI self-efficacy, while both AI literacy and AI self-efficacy measured 50.98 % of the explained variance of AI self-competency. CONCLUSION This study found that nursing students' AI literacy significantly correlated with both AI self-efficacy and AI self-competency. Moreover, AI self-efficacy functioned as a mediator, amplifying the effect of AI literacy on self-competency. Nursing colleges and educators may strengthen students' AI literacy and competence by fostering AI self-efficacy through targeted, integrated educational strategies.

No abstract available

This systematic literature review rigorously evaluates the impact of Generative AI (GenAI) on academic integrity within higher education settings. The primary objective is to synthesize how GenAI technologies influence student behavior and academic honesty, assessing the benefits and risks associated with their integration. We defined clear inclusion and exclusion criteria, focusing on studies explicitly discussing GenAI’s role in higher education from January 2021 to December 2024. Databases included ABI/INFORM, ACM Digital Library, IEEE Xplore, and JSTOR, with the last search conducted in May 2024. A total of 41 studies met our precise inclusion criteria. Our synthesis methods involved qualitative analysis to identify common themes and quantify trends where applicable. The results indicate that while GenAI can enhance educational engagement and efficiency, it also poses significant risks of academic dishonesty. We critically assessed the risk of bias in included studies and noted a limitation in the diversity of databases, which might have restricted the breadth of perspectives. Key implications suggest enhancing digital literacy and developing robust detection tools to effectively manage GenAI’s dual impacts. No external funding was received for this review. Future research should expand database sources and include more diverse study designs to overcome current limitations and refine policy recommendations.

The release of ChatGPT in late 2022 marked the beginning of a rapid transformation in higher education, soon followed by the development of multimodal generative AI programs. As this technology becomes increasingly integrated into teaching and learning, it is crucial to evaluate its current use and impact. This systematic literature review captures the initial academic response to generative AI, providing insights into how higher education has adopted this transformative technology in its first two years. The findings indicate that while some themes from the pre-ChatGPT era persist, new and emerging trends—particularly in fostering creativity, critical thinking, learning autonomy, and prompt literacy—are now taking shape. This shift underscores a growing emphasis on the pedagogical integration of generative AI. However, the review also highlights a key tension: while generative AI enhances efficiency, it raises concerns about overreliance, potentially leading to the outsourcing of critical cognitive and metacognitive skills. To address these challenges and fully harness the potential of generative AI, future research should focus on exploring multimodal generative AI tools and fostering student–teacher-AI collaboration.

Generative AI (hereinafter GenAI) technology, such as ChatGPT, is already influencing the higher education sector. In this work, we focused on the impact of GenAI on the academic integrity of assessments within higher education institutions, as GenAI can be used to circumvent assessment approaches within the sector, compromising their quality. The purpose of our research was threefold: first, to determine the extent to which the use of GenAI can be detected via the marking and moderation process; second, to understand whether the presence of GenAI affects the marking process; and finally, to establish whether authentic assessments can safeguard academic integrity. We used a series of experiments in the context of two UK‐based universities to examine these issues. Our findings indicate that markers, in general, are not able to distinguish assessments that have had GenAI input from assessments that did not, even though the presence of GenAI affects the way markers approach the marking process. Our findings also suggest that the level of authenticity in an assessment has no impact on the ability to safeguard against or detect GenAI usage in assessment creation. In conclusion, we suggest that current approaches to assessments in higher education are susceptible to GenAI manipulation and that the higher education sector cannot rely on authentic assessments alone to control the impact of GenAI on academic integrity. Thus, we recommend giving more critical attention to assessment design and placing more emphasis on assessments that rely on social experiential learning and are performative rather than output‐based and asynchronously written. What is already known about this topic GenAI has enabled students to complete higher education assessments quickly and with good quality, leading to challenges in academic integrity. GenAI has transformed the requirements and considerations in assessment design in higher education. Authentic assessments are seen as a prominent way to tackle the GenAI challenge. What this paper adds We provide quantitative and qualitative experimental evidence suggesting that GenAI can generate authentic assessments that pass the scrutiny of experienced academics. We demonstrate how the use of authentic assessments alone does not protect the academic integrity of students in higher education. Our qualitative analysis indicates that markers may generate false positive and false negative results if they suspect GenAI tampering in an assessment. Thus, students' learning is not assessed correctly. Implications for practice and/or policy When universities and national organisations design policies regarding GenAI, authentic assessments are not the panacea; the focus must remain on assessment design. Assessments of learning need to shift from assessing output to focusing on process and relevance to the workplace. That would mean a paradigmatic shift from written assessments to synchronous interpersonal assessments. The move away from written assessments has implications that are far reaching for the academy if written assessments cannot be trusted as a reliable indicator for and of learning.

No abstract available

This research aims to explore the use, perceptions, and challenges associated with generative AI (GenAI) among higher education students. As GenAI technologies, such as language models, image generators, and code assistants, become increasingly prevalent in academic settings, it is essential to understand how students engage with these tools and their impact on their learning process. The study investigates students’ awareness, adoption patterns, and perceptions of generative AI’s role in academic tasks, alongside the benefits they identify and the challenges they face, including ethical concerns, reliability, and accessibility. Through quantitative methods, the research provides a comprehensive analysis of student experiences with generative AI in higher education. The findings aim to inform educators, technologists, and institutions about the opportunities and barriers of integrating these technologies into educational practices and guide the development of strategies that support effective and responsible AI use in academia.

The utilization of Generative AI (GenAI) in higher education classrooms has significantly increased in recent years. Studies show that GenAI holds promise in impacting the learning experiences of both students and teachers, offering personalized learning and assessment opportunities. This study conducts a systematic review of the responses, attitudes, and behaviors related to the application of GenAI within higher education classrooms. To this end, we synthesized 99 papers published between 2020 and August 2024, focusing on the utilization of GenAI in higher education settings. The analysis addresses three key inquiries: responses, attitudes, and behaviors. This systematic review provides an updated understanding from psychological perspectives of GenAI’s role in the teaching and learning processes of higher education, with a particular emphasis on GenAI technologies.

Artificial intelligence (AI) is increasingly shaping diverse sectors, including education, sparking debates about its potential to transform pedagogical practices and redefine the role of educators. This study explores the perceptions and applications of generative AI in Moroccan higher education to better understand its implications for teaching and learning. A mixed-methods approach was adopted, combining quantitative data from 130 teachers and 156 students with qualitative insights. Quantitative findings reveal significant differences: students demonstrate greater openness to adopting AI, appreciating its capacity to provide instant feedback, enhance creativity, and improve academic performance. In contrast, teachers express reservations, particularly regarding AI’s potential to undermine critical soft skills such as collaboration, problem-solving, and critical thinking. Qualitative analyses confirm these trends, highlighting that, while AI is perceived as a valuable complementary tool, it cannot replace the essential human role of educators in providing personalized guidance and addressing students’ emotional and cognitive needs. Both groups agree on the necessity of enhanced digital literacy to ensure ethical and effective AI integration. These findings underscore the opportunities of generative AI, such as personalized learning and efficiency, while addressing limitations like ethical concerns and over-reliance, offering actionable insights for policymakers, educators, and technologists aiming to integrate AI responsibly in education.

This study examined the guidelines issued by the top 50 U.S. universities regarding the use of Generative AI (GenAI) in academic and administrative activities. Employing a mixed methods approach, the research combined topic modeling, sentiment analysis, and qualitative thematic analysis to provide a comprehensive understanding of institutional responses to GenAI. Topic modeling identified four core topics: Integration of GenAI in Learning and Assessment, GenAI in Visual and Multimodal Media, Security and Ethical Considerations in GenAI, and GenAI in Academic Integrity. These themes were further explored through sentiment analysis, which revealed highly positive attitudes towards GenAI across all institution types, with significant differences between faculty and student-targeted guidelines. Qualitative thematic analysis corroborated these findings and provided deeper insights, revealing that 94% of universities had faculty guidelines emphasizing the importance of establishing and communicating course-specific GenAI policies. This analysis also highlighted recurring themes such as academic integrity and privacy concerns, which aligned with the security and ethical considerations identified in the topic modeling. The study highlights the rapid evolution of GenAI guidelines in higher education and the need for flexible, stakeholder-specific policies that address both the opportunities and challenges presented by this technology.

The rapid integration of generative artificial intelligence (AI) into educational environments raises both opportunities and concerns regarding assessment design, academic integrity, and quality assurance. While new generation AI tools offer new modes of interactivity, feedback, and content generation, their use in assessment remains insufficiently pedagogically framed and regulated. In this study, we propose a new framework for generative AI-supported assessment in higher education, structured around the needs and responsibilities of three key stakeholders (branches): instructors, students, and control authorities. The framework outlines how teaching staff can design adaptive and AI-informed tasks and provide feedback, how learners can engage with these tools transparently, and how institutional bodies can ensure accountability through compliance standards, policies, and audits. This three-branch multi-level model contributes to the emerging discourse on responsible AI adoption in higher education by offering a holistic approach for integrating AI-based systems into assessment practices while safeguarding academic values and quality.

Generative AI (GenAI) tools, including ChatGPT, Microsoft Copilot, and Google Gemini, are rapidly reshaping higher education by transforming how students, educators, and researchers engage with learning, teaching, and academic work. Despite their growing presence, the adoption of GenAI remains inconsistent, largely due to the absence of universal guidelines and trust-related concerns. This study examines how trust, defined across three key dimensions (accuracy and relevance, privacy protection, and nonmaliciousness), influences the adoption and use of GenAI tools in academic environments. Using survey data from 823 participants across different academic roles, this study employs multiple regression analysis to explore the relationship between trust, user characteristics, and behavioral intention. The results reveal that trust is primarily experience-driven. Frequency of use, duration of use, and self-assessed proficiency significantly predict trust, whereas demographic factors, such as gender and academic role, have no significant influence. Furthermore, trust emerges as a strong predictor of behavioral intention to adopt GenAI tools. These findings reinforce trust calibration theory and extend the UTAUT2 framework to the context of GenAI in education. This study highlights that fostering appropriate trust through transparent policies, privacy safeguards, and practical training is critical for enabling responsible, ethical, and effective integration of GenAI into higher education.

Generative Artificial Intelligence (GenAI) is rapidly transforming the landscape of higher education, offering novel opportunities for personalised learning and innovative assessment methods. This paper explores the dual-edged nature of GenAI’s integration into educational practices, focusing on both its potential to enhance student engagement and learning outcomes and the significant challenges it poses to academic integrity and equity. Through a comprehensive review of current literature, we examine the implications of GenAI on assessment practices, highlighting the need for robust ethical frameworks to guide its use. Our analysis is framed within pedagogical theories, including social constructivism and competency-based learning, highlighting the importance of balancing human expertise and AI capabilities. We also address broader ethical concerns associated with GenAI, such as the risks of bias, the digital divide, and the environmental impact of AI technologies. This paper argues that while GenAI can provide substantial benefits in terms of automation and efficiency, its integration must be managed with care to avoid undermining the authenticity of student work and exacerbating existing inequalities. Finally, we propose a set of recommendations for educational institutions, including developing GenAI literacy programmes, revising assessment designs to incorporate critical thinking and creativity, and establishing transparent policies that ensure fairness and accountability in GenAI use. By fostering a responsible approach to GenAI, higher education can harness its potential while safeguarding the core values of academic integrity and inclusive education.

Generative AI is weaving into the fabric of many human aspects through its transformative power to mimic human-generated content. It is not a mere technology; it functions as a generative virtual assistant, raising concerns about its impact on cognition and critical thinking. This mixed-methods study investigates how GenAI ChatGPT affects critical thinking across cognitive presence (CP) phases. Forty students from a four-year university in the southwestern United States completed a survey; six provided their ChatGPT scripts, and two engaged in semi-structured interviews. Students’ self-reported survey responses suggested that GenAI ChatGPT improved triggering events (M = 3.60), exploration (M = 3.70), and integration (M = 3.60); however, responses remained neutral during the resolution stage. Two modes of interaction were revealed in the analysis of students’ ChatGPT scripts: passive, AI-directed use and collaborative, AI-supported interaction. A resolution gap was identified; nonetheless, the interview results revealed that when GenAI ChatGPT was utilized with guidance, all four stages of cognitive presence were completed, leading to enhanced critical thinking and a reconceptualization of ChatGPT as a more knowledgeable other. This research suggests that the effective use of GenAI in education depends on the quality of human–AI interaction. Future directions must orient toward an integration of GenAI in education that positions human and machine intelligence not as a substitution but as co-participation, opening new epistemic horizons while reconfiguring assessment practices to ensure that human oversight, critical inquiry, and reflective thinking remain at the center of learning.

This systematic review examines how machine learning (ML) and generative AI (GenAI) have been integrated into learning analytics (LA) in higher education (2018–2025). Following PRISMA 2020, we screened 9590 records and included 101 English-language, peer-reviewed empirical studies that applied ML or GenAI within LA contexts. Records came from 12 databases (last search 15 March 2025), and the results were synthesized via thematic clustering. ML approaches dominate LA tasks, such as engagement prediction, dropout-risk modelling, and academic-performance forecasting, whereas GenAI—mainly transformer models like GPT-4 and BERT—is emerging in real-time feedback, adaptive learning, and sentiment analysis. Studies spanned world regions. Most ML papers (n = 75) examined engagement or dropout, while GenAI papers (n = 26) focused on adaptive feedback and sentiment analysis. No formal risk-of-bias assessment was conducted due to heterogeneity. While ML methods are well-established, GenAI applications remain experimental and face challenges related to transparency, pedagogical grounding, and implementation feasibility. This review offers a comparative synthesis of paradigms and outlines future directions for responsible, inclusive, theory-informed AI use in education.

The rise of generative AI in higher education has disrupted our traditional understandings of academic integrity, moving our focus from clear-cut infractions to evolving ethical judgment. In this study, a survey of 401 students from major U.S. universities provides insight into how beliefs, behaviors, and policy awareness intersect in shaping how students interact with AI-assisted writing. The findings indicate that students’ ethical beliefs—not institutional policies—are the strongest predictors of perceived misconduct and actual AI use in writing. Policy awareness was found to have no significant effect on ethical judgments or behavior. Instead, students who believe AI writing is cheating were found to be substantially less likely to view it as ethical or engage with it. These findings suggest that many students do not treat AI use in learning activities as an extension of conventional cheating (e.g., plagiarism), but rather as a distinct category of academic conduct/misconduct. Rather than using punitive models to attempt to punish students for using AI, this study suggests that education about AI ethics and the risk of AI overreliance may prove more successful for curbing unethical AI use in higher education.

Generative artificial intelligence (GAI) technologies are gaining traction in higher education, offering potential benefits such as personalized learning support and enhanced productivity. However, successful integration requires understanding the factors influencing students’ adoption of these emerging tools. This study investigates the determinants shaping higher education students’ adoption of GAI through the lens of the unified theory of acceptance and use of technology 2 framework. Data was collected from Pyatigorsk State University students and analyzed using structural equation modeling. The findings reveal habit (HB) as the most influential predictor of GAI adoption among students, followed by performance expectancy. Hedonic motivation, social influence (SI), and price value positively influenced behavioral intention (BI) to use these technologies. Surprisingly, facilitating conditions (FCs) exhibited a negative effect on BI, suggesting potential gaps in support systems. The study identifies no significant gender differences in the underlying factors driving adoption. Based on the results, recommendations are provided to foster HB formation, communicate benefits, enhance hedonic appeal, leverage SI, address price concerns, and strengthen FCs. Potential limitations include the cross-sectional nature of the data, geographic constraints, reliance on self-reported measures, and the lack of consideration for individual differences as moderators. This research contributes to the growing body of knowledge on GAI adoption in educational contexts, offering insights to guide higher education institutions in responsibly integrating these innovative tools while addressing student needs and promoting improved learning outcomes.

The integration of generative artificial intelligence (GAI) into higher education is transforming students’ learning processes, academic performance, and psychological well-being. Despite the increasing adoption of GAI tools, the mechanisms through which students’ AI literacy and self-regulated learning (SRL) relate to their academic and emotional experiences remain underexplored. This study investigates how AI literacy and SRL are associated with writing performance and digital well-being among university students in GAI-supported higher learning contexts. A survey was administered to 257 students from universities in China, and structural equation modeling was used to examine the hypothesized relationships. Results show that both AI literacy and SRL significantly and positively predict students’ writing performance, with SRL having a stronger effect. Moreover, AI literacy shows a positive association with GAI-driven well-being, with writing performance serving as a partial mediator in this relationship. These findings suggest that fostering both technological competencies and effective learning strategies may support students’ academic outcomes while supporting their psychological well-being in AI-enriched educational environments. By integrating AI literacy and SRL into a unified model, this study contributes to the growing body of research on GAI-driven well-being in higher education and offers practical implications for cultivating balanced and sustainable learning experiences in the age of GAI.

The proliferation of generative artificial intelligence (GenAI) has disrupted academic institutions across the world, presenting transformative challenges for decision makers, and leading to questions around existing methods and practices within higher education (HE). The widespread adoption of GenAI tools and processes highlights an ongoing change to existing perceptions of the role of humans and machines. Academics have expressed concerns relating to: academic integrity, undermining critical thinking, lowering of academic standards and the threat to existing academic models. This study presents a mixed methods approach to developing valuable insight to the key underlying challenges impacting GenAI adoption within HE. The results highlight many of the key challenges impacting decision makers in the formation of policy and strategic direction. The findings identify significant interdependencies between the key underlying challenges associated with GenAI adoption in HE. We further discuss the implications in the findings of the high levels of driving power of the factors: (i) perceived risks from Large Language Model training and learning; (ii) the reliability of GenAI outputs in the context of impact on creativity and decision making; (iii) the impact from poor levels of GenAI platform regulation. We posit this research as offering new insight and perspective on the changing landscape of HE through the widespread adoption of GenAI.

This systematic review investigates the impact of generative artificial intelligence (GenAI) tools on developing academic skills in higher education. Analysing 158 studies published between 2021 and 2024, it focuses on the impact of GenAI tools on the development of cognitive, technical and interpersonal skills. The results reveal that 94% of the sampled studies reported significant improvements in cognitive skills, like critical thinking, problem‐solving, analytical and metacognitive abilities, facilitated by personalised learning and feedback. Indeed, the development of technical skills was reported in research (24%), writing (26%), data analysis (33%) and technical literacy (18%). Additionally, GenAI tools were found to promote interpersonal skills by fostering interactive and engaging learning environments, with notable skills development in communication (24%), organisation (26%), empathy (5%) and teamwork (45%). Hence, this review underscores the importance of ethical and responsible use of GenAI tools, ongoing monitoring and active stakeholder engagement to maximise their benefits in developing cognitive, technical and interpersonal skills in higher education. They offer a promising avenue for academic advancement by fostering critical thinking, enhancing technical proficiency and promoting effective communication and teamwork. Therefore, GenAI tools significantly enhance academic skills; however, their integration requires a robust ethical framework and sustained examination of their long‐term impacts.

Abstract The integration of Generative Artificial Intelligence (GenAI) in education has introduced innovative approaches to assessment. One such approach is AI chatbot-based assessment, which utilizes large language models to provide students with timely and consistent feedback. However, the effectiveness of AI chatbots in generating assessments comparable to human evaluators in educational contexts remains underexplored. This study compared the grades and feedback provided by AI chatbots, peers, and the course instructor for student projects in a higher education course. The participants were 76 undergraduate students who engaged in a group project involving three phases: questionnaire development, peer assessment, and chatbot-based assessment. Employing a mixed-methods approach, this study quantitatively compared project grades and qualitatively analyzed feedback quality. Results indicated that AI chatbots consistently assigned higher grades than human assessors, while peer and instructor grades were notably lower and closely aligned. Content analysis revealed that chatbots generally provided higher-quality feedback compared to peers, offering detailed insights and specific guidance for improvement, though they occasionally included irrelevant or contradictory information requiring student intervention. Conversely, peer feedback was more personalized and context-sensitive. These findings highlight the importance of human judgment, suggesting that integrating chatbot-based assessments with traditional methods can leverage their complementary strengths to enrich student learning.

With the rapid evolution and widespread adoption of Generative AI (GenAI), there has been a recent surge in research on students’ perceptions and usage of these tools for learning. However, a critical need remains to understand how students from various demographic groups perceive and utilize GenAI for educational purposes. This research aims to fill this gap by evaluating students’ use of GenAI, comfort level, perception of readiness, benefits and challenges, and demographic differences in their perceptions. Through an online survey of 482 students across diverse institutions of higher education primarily within the United States, the findings indicate a significant majority of students feel comfortable using GenAI tools and recognize their potential to enhance productivity and academic success. However, students also reported concerns related to GenAI use, including concerns about academic integrity, overreliance on AI, and data privacy and security. The analysis highlights differences in the subscales of perceived readiness (GenAI Comprehension; GenAI Utilization and Proficiency), benefits (Impact of GenAI; GenAI Empowerment), and challenges (Negative Impact of GenAI; GenAI Limitation) across demographic groups, including student classification, enrollment status, and institution types. Findings suggest a critical need for the integration of AI literacy into curricula to address opportunities and challenges posed by GenAI in higher education, and this study calls for additional research to explore evolving perceptions of GenAI use over time and its long-term impact on higher education.

Development of large language models (LLMs) has transformed the landscape of personalized education through intelligent tutoring systems (ITS) which responds to diverse learning requirements. This article proposed a model named LLM based Personalized Intelligent Tutoring System (LPITutor) that is based on LLM for personalized ITS that leverages retrieval-augmented generation (RAG) and advanced prompt engineering techniques to generate customized responses aligned with students’ requirements. The aim of LPITutor is to provide customized learning content that adapts to different levels of learners skills and question complexity. The performance of proposed model was evaluated on accuracy, completeness, clarity, difficulty alignment, coherence, and relevance. The finding of LPITutor indicates that it effectively balances the response accuracy and clarity with significant alignment to the difficulty level of student queries. The proposed work also emphasises the broader implications of artificial intelligence (AI)-driven ITS in education and presents future directions for improving the adaptation and optimization of LPITutor.

Previous studies have demonstrated the effectiveness of intelligent tutoring systems (ITS) in facilitating English learning. However, no empirical research has been conducted on secondary students’ intention to use ITSs in the language domain. This study proposes an extended technology acceptance model (TAM) to predict secondary students’ continuance intention to use and actual use of ITSs for English learning. The model included fifteen hypotheses that were tested with 528 senior secondary students in China. The results of structural equation modeling showed that (1) perceived usefulness and price value had direct positive impacts on continuance intention; (2) perceived ease of use was not directly associated with students’ intention but indirectly influenced intention via perceived usefulness; (3) through the mediation of perceptions, learning goal orientation and facilitating conditions were positively associated with continuance intention; (4) perceived enjoyment positively predicted and anxiety negatively predicted students’ intention to use ITSs; and (5) students’ continuance intention to use ITSs was significantly positively associated with their actual use of ITSs for English learning. The model showed strong explanatory power and might be implemented in future research. This study contributes to the theory and practice of ITSs in K-12 education.

Self-regulated learning (SRL) is critical for learning across tasks, domains, and contexts. Despite its importance, research shows that not all learners are equally skilled at accurately and dynamically monitoring and regulating their self-regulatory processes. Therefore, learning technologies, such as intelligent tutoring systems (ITSs), have been designed to measure and foster SRL. This paper presents an overview of over 10 years of research on SRL with MetaTutor, a hypermedia-based ITS designed to scaffold college students’ SRL while they learn about the human circulatory system. MetaTutor’s architecture and instructional features are designed based on models of SRL, empirical evidence on human and computerized tutoring principles of multimedia learning, Artificial Intelligence (AI) in educational systems for metacognition and SRL, and research on SRL from our team and that of other researchers. We present MetaTutor followed by a synthesis of key research findings on the effectiveness of various versions of the system (e.g., adaptive scaffolding vs. no scaffolding of self-regulatory behavior) on learning outcomes. First, we focus on findings from self-reports, learning outcomes, and multimodal data (e.g., log files, eye tracking, facial expressions of emotion, screen recordings) and their contributions to our understanding of SRL with an ITS. Second, we elaborate on the role of embedded pedagogical agents (PAs) as external regulators designed to scaffold learners’ cognitive and metacognitive SRL strategy use. Third, we highlight and elaborate on the contributions of multimodal data in measuring and understanding the role of cognitive, affective, metacognitive, and motivational (CAMM) processes. Additionally, we unpack some of the challenges these data pose for designing real-time instructional interventions that scaffold SRL. Fourth, we present existing theoretical, methodological, and analytical challenges and briefly discuss lessons learned and open challenges.

Background In 2020, school closures during the COVID-19 pandemic forced students all over the world to promptly alter their learning routines from in-person to distance learning. However, so far, only a limited number of studies from a few countries investigated whether school closures affected students’ performance within intelligent tutoring system—such as intelligent tutoring systems. Method In this study, we investigated the effect of school closures in Austria by evaluating data (n = 168 students) derived from an intelligent tutoring system for learning mathematics, which students used before and during the first period of school closures. Results We found that students’ performance increased in mathematics in the intelligent tutoring system during the period of school closures compared to the same period in previous years. Conclusion Our results indicate that intelligent tutoring systems were a valuable tool for continuing education and maintaining student learning during school closures in Austria.

Teaching Science, Technology, Engineering, and Mathematics (STEM) disciplines faces significant challenges due to the increasing complexity of content and the diversity in students’ knowledge levels. Traditional teaching methods, characterized by their standardized approach, fail to adapt to individual needs, limiting learning potential. This problem is particularly evident in mathematics, physics, and programming, where mastering critical concepts requires a logical and sequential progression. This study proposes an artificial intelligence-based intelligent tutoring system to provide a real-time personalized and adaptive learning experience. The system integrates advanced deep learning and natural language processing models, allowing for targeted feedback and dynamic adjustment of learning trajectories. The results show significant improvements in several key metrics. The experimental group achieved an average precision of 85% in programming and 78% in mathematics, significantly outperforming the control group. Furthermore, a linear regression model identified a positive correlation between the time of interaction with the system and the rate of progress in fundamental concepts. Student perceptions were also highly positive, with 80% appreciating the usefulness of adaptive feedback. These findings identify the system’s potential to transform STEM teaching, address the lack of personalization, and improve learning in various educational settings.

Abstract Learning through an Intelligent Tutoring System (ITS) lies in performing well in academics and improving the learner’s learning outcomes. The recent developments within an Intelligent Tutoring System are not focused on improving human–computer interactions. The best way to overcome this constraint is to develop ITS interfaces to provide learners with better learning experiences. In this study, Augmented Reality (AR) potential along with AI methodologies is utilized within the developed ITS interface to improve the learning experience of the learning-disabled learners. Augmented Reality is where virtual images overlay the physical world, and Mixed Reality is an emerging technology that presents an altered reality, which engages users to interact with an environment developed using virtual objects. With several tools and applications available, creating and providing immersive learning experiences for learners is rising. AR in educating the specially-abled is widespread, and its benefits are sufficiently explored. However, limited AR applications are designed specifically for supporting the education of individuals with learning disabilities. The available application, and their impact on learning-disabled learners, needs detailed investigation. This work presents an Intelligent Tutoring System (ITS) to educate learners with Augmented Reality (AR) based content. The ITS learner module implemented in this study was developed for learning disabilities identification and we have assessed total 105 participants (with or without Learning Disabilities) for the experiment. The ITS performance is compared (with or without) AR content-based learning and based on the findings, AR-based learning through ITS is effective. The benefits are manifold, increase in motivation, ease of interaction, development in cognitive skills, enhancement in short-term memory, and making lessons more enjoyable, turning the overall experience stimulating and engaging.

Education is the cornerstone of improving people’s lives and achieving global sustainability. Intelligent systems assist sustainable education with various benefits, including recommending a personalized learning environment to learners. The classroom learning environment facilitates human tutors to interact with every learner and obtain the opportunity to understand the learner’s psychology and then provide learning material (access learner previous knowledge and well-align the learning material as per learner requirement) to them accordingly. Implementing this cognitive intelligence in Intelligent Tutoring System is quite tricky. This research focused on mimicking human tutor cognitive intelligence in the computer-aided system of offering an exclusive curriculum or quality education for sustainable learners. The prime focus of this research article was to evaluate the proposed SeisTutor using Kirkpatrick four-phase evaluation model. The experimental results depict the enhanced learning gained through intelligence incorporated SeisTutor against the intelligence absence, as demonstrated.

Nowadays, the improvement of digital learning with Artificial Intelligence has attracted a lot of research, as it provides solutions for individualized education styles which are independent of place and time. This is particularly the case for computer science, as a tutoring domain, which is rapidly growing and changing and as such, learners need frequent update courses. In this paper, we present a thorough evaluation of a fuzzy-based intelligent tutoring system (ITS), that teaches computer programming. The evaluation concerns multiple aspects of the ITS. The evaluation criteria are: (i) context, (ii) effectiveness, (iii) efficiency, (iv) accuracy, (v) usability and satisfaction, and (vi) engagement and motivation. In the evaluation process students of an undergraduate program in Informatics of the University of Piraeus in Greece participated. The evaluation method that was used included questionnaires, analysis of log files and experiments. Also, t-tests were conducted to certify the validity of the evaluation results. Indeed, the evaluation results are very positive and show that the incorporated fuzzy mechanism to the presented ITS enhances the system with Artificial Intelligence and through this, it increases the learners’ satisfaction and new knowledge learning and mastering, improves the recommendation accuracy of the system, the efficacy of interactions, and contributes positively to the learners’ engagement in the learning process.

Intelligent Tutoring Systems (ITSs) are educational systems that reflect knowledge using artificial intelligence implements. In this paper, we give an outline of the Programming-Tutor architectural design with the core implements on user interaction. This pilot proposal is for designing a model domain of a subset in the computer programming language. The completed project would be adequate to show the idea of a completely developed computing Intelligent Tutoring System in online programming courses to offer benefits to students in the Pacific. This proposed concept would also provide students with an immersive learning experience in an online course to assist in a formative assessment to enhance student learning. A smart tutoring system can provide prompt input of high quality which not only conveys to students about the consistency of the solution but also provides them with information on the precision of the key concerning their existing solutions expertise. This Intelligent Tutoring System (ITS) is proposed to be designed using intelligent algorithms such as optimized ant colony to be able to support the online tutoring system that can initiate the complex learning principles in computing science courses. It is also hypothesized that, based on the performance of other Intelligent Tutoring Systems, students would be able to learn to program more easily in regional campuses and acquire experiences more rapidly and efficiently than students who are taught using conventional methods in an online mode.

Abstract The present study aims to examine the pedagogical effectiveness of a Chinese mathematical dialogue-based intelligent tutoring system used for teaching mathematics. The mathematical unit ‘multiplication and division of time expressions’ was taught to 134 fifth-grade students in three types of instruction conditions: the intelligent tutoring system (ITS), conventional teacher instruction and material reading. The results show that student performance was comparable between the proposed mathematical ITS and conventional instruction conditions but was significantly poorer when no teaching method was used. The questionnaire survey showed that the ITS method not only improved maths learning, but also increased motivation among the fifth graders. The effectiveness of ITS remedial instruction in mathematics is discussed.

We propose and implement a novel intelligent tutoring system, called RadarMath, to support intelligent and personalized learning for math education. The system provides the services including automatic grading and personalized learning guidance. Specifically, two automatic grading models are designed to accomplish the tasks for scoring the text-answer and formula-answer questions respectively. An education-oriented knowledge graph with the individual learner’s knowledge state is used as the key tool for guiding the personalized learning process. The system demonstrates how the relevant AI techniques could be applied in today's intelligent tutoring systems.

The way in which people learn and institutions teach is changing due to the ever-increasing impact of technology. People access the Internet anywhere, anytime and request online training. This has brought about the creation of numerous online learning platforms which offer comprehensive and effective educational solutions which are 100% online. These platforms benefit from intelligent tutoring systems that help and guide students through the learning process, emulating the behavior of a human tutor. However, these systems give the student little freedom to experiment with the knowledge of the subject, that is, they do not allow him/her to propose and carry out tasks on his/her own initiative. They are very restricted systems in term of what the student can do, as the tasks are defined in advance. An intelligent tutoring system is proposed in this paper to encourage students to learn through experimentation, proposing tasks on their own initiative, which involves putting into use all the skills, abilities tools and knowledge needed to successfully solve them. This system has been designed developed and applied for learning predictive parsing techniques and has been used by Computer Science students during four academic courses to evaluate its suitability for improving the student’s learning process.

Abstract The goal of this study was to investigate 65 students' evidence scores of emotions while they engaged in cognitive and metacognitive self-regulated learning processes as they learned about the circulatory system with MetaTutor, a hypermedia-based intelligent tutoring system. We coded for the accuracy of detecting students’ cognitive and metacognitive processes, and examined how the computed scores related to mean evidence scores of emotions and overall learning. Results indicated that mean evidence score of surprise negatively predicted the accuracy of making a metacognitive judgment, and mean evidence score of frustration positively predicted the accuracy of taking notes, a cognitive learning strategy. These results have implications for understanding the beneficial role of negative emotions during learning with advanced learning technologies. Future directions include providing students with feedback about the benefits of both positive and negative emotions during learning and how to regulate specific emotions to ensure the most effective learning experience with advanced learning technologies.

In this experimental study, an intelligent tutoring system called the fuzzy Bayesian intelligent tutoring system (FB-ITS), is developed by using artificial intelligence methods based on fuzzy logic and the Bayesian network technique to adaptively support students in learning environments. The effectiveness of the FB-ITS was evaluated by comparing it with two other versions of an Intelligent Tutoring System (ITS), fuzzy ITS and Bayesian ITS, separately. Moreover, it was evaluated by comparing it with an existing traditional e-learning system. In order to evaluate whether the academic performance of the students in different learning groups differs or not, analysis of covariance (ANCOVA) was used based on the students’ pre-test and post-test scores. The study was conducted with 120 undergraduate university students. Results showed that students who studied using FB-ITS had significantly higher academic performance on average compared to other students who studied with the other systems. Regarding the time taken to perform the post-test, the results indicated that students who used the FB-ITS needed less time on average compared to students who used the traditional e-learning system. From the results, it could be concluded that the new system contributed in terms of the speed of performing the final exam and high academic success.

The aim of this research was to develop a learning recommendation component in an intelligent tutoring system (ITS) that dynamically predicts and adapts to a learner's style. In order to develop a proper ITS, we present an improved knowledge base supporting adaptive learning, which can be achieved by a suitable knowledge construction. This process is illustrated by implementing a web-based online tutor system. In addition, our knowledge structure provides adaptive presentation and personalized learning with the proposed adaptive algorithm, to retrieve content according to individual learner characteristics. To demonstrate the proposed adaptive algorithm, pre-test and post-test were used to evaluate suggestion accuracy of the course in a class for adapting to a learner's style. In addition, pre- and post-testing were also used with students in a real teaching/learning environment to evaluate the performance of the proposed model. The results show that the proposed system can be used to help students or learners achieve improved learning.

Abstract Students nowadays are hard to be motivated to solve logical problems with traditional teaching methods. Computers, Smartphone's, tablets and other smart devices disturb their attention. But those smart devices can be used as auxiliary tools of modern teaching methods. The flipped classroom is one such innovative method that moves the solving problems outside the classroom via technology and reinforces solving problems inside the classroom via learning activities. In this paper, the authors implement flipped classroom as an element of Internet of Things (IOT) into learning process of mathematical logic course. In the flipped classroom, an Intelligent Tutoring System (ITS) was used to help students work with the problems in the course outside the classroom. This study showed that perceived usefulness, self-efficacy, compatibility, and perceived support for enhancing social ties are important antecedents to continuance intention to use flipped classroom.

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The goal of this study was to use eye-tracking and log-file data to investigate the impact of prior knowledge on college students’ (N = 194, with a subset of n = 30 for eye tracking and sequence mining analyses) fixations on (i.e., looking at) self-regulated learning-related areas of interest (i.e., specific locations on the interface) and on the sequences of engaging in cognitive and metacognitive self-regulated learning processes during learning with MetaTutor, an Intelligent Tutoring System that teaches students about the human circulatory system. Results revealed that there were no significant differences in fixations on single areas of interest by the prior knowledge group students were assigned to; however there were significant differences in fixations on pairs of areas of interest, as evidenced by eye-tracking data. Furthermore, there were significant differences in sequential patterns of engaging in cognitive and metacognitive self-regulated learning processes by students’ prior knowledge group, as evidenced from log-file data. Specifically, students with high prior knowledge engaged in processes containing cognitive strategies and metacognitive strategies whereas students with low prior knowledge did not. These results have implications for designing adaptive intelligent tutoring systems that provide individualized scaffolding and feedback based on individual differences, such as levels of prior knowledge.

Students experience mathematics in their day-to-day lives as they pursue their individual interests in areas like sports or video games. The present study explores how connecting to students’ individual interests can be used to personalize learning using an Intelligent Tutoring System (ITS) for algebra. We examine the idea that the effects of personalization may be moderated by students’ depth of quantitative engagement with their out-of-school interests. We also examine whether math problems designed to draw upon students’ knowledge of their individual interests at a deep level (i.e., actual quantitative experiences) or surface level (i.e., superficial changes to problem topic) have differential effects. Results suggest that connecting math instruction to students’ out-of-school interests can be beneficial for learning in an ITS and reduces gaming the system. However, benefits may only be realized when students’ degree of quantitative engagement with their out-of-school interests matches the depth at which the personalized problems are written. Students whose quantitative engagement with their interests is minimal may benefit most when problems draw upon superficial aspects of their interest areas. Students who report significant quantitative engagement with their interests may benefit most when individual interests are deeply incorporated into the quantitative structure of math problems. We also find that problems with deeper personalization may spur positive affective states and ward off negative ones for all students. Findings suggest depth is a critical feature of personalized learning with implications for theory and AI instructional design.

Distance Education Courses in Virtual Learning Environments (VLE) employ tutors for pedagogical support, monitoring of students, and detecting possible dropouts. However, when courses have a higher number of students, there can be a work overload on human tutors, impacting their work quality. To mitigate this problem, artificial smart tutors can be used not only to increase the capacity to meet students' needs but also to improve the monitoring of their performance. Increasing the scalability of online courses can be achieved using an intelligent artificial tutor. Goal: Analyze the viability of an intelligent tutor in meeting the main tutoring demands made by students on Dell Accessible Learning platform. Method: We developed an artifact called STUART that monitors DAL and interacts with students, providing automation, intelligence, and support to the teaching and learning process. We programmed STUART to meet reactively and proactively students' main demands, based on the corpus of interactions scenarios on previous courses. Fourteen participants attended two classes of a Distance Education Course, with and without STUART, where interaction data were collected. Results: For 76% of the participants, STUART helped solve the more frequent problems in the pedagogical, technical, and content levels. There was an average reduction of 87% in the requests for a human tutor and a reduction of 27% in the time needed to finish tasks. That resulted in the preference of STUART compared to the human tutor for 85% of the students. SUS (usability assessment) scored 86.

Contribution: This study incorporates competition-based learning (CBL) into machine learning courses. By engaging students in innovative problem-solving challenges within information competitions, revealing that students’ participation in online problem-solving competitions can improve their information technology, and showcase competitions can enhance their competition ability. Background: The CBL model seamlessly integrates project-based learning and competition, placing a strong emphasis on both collective learning and outcomes. This approach cultivates motivation among team members, driving them to enhance their learning and translate knowledge into practical experience. Research Questions: The objective is to examine the disparities in the development of theoretical knowledge, information technology, AI practical ability, and competition ability among students participating in online problem-solving competitions and showcase competitions, and discusses the potential moderating effect of competition type on the relationships between variables in the hypothetical model. Methodology: The study involved 74 students enrolled in machine learning course at a university. The students were given theoretical knowledge and information technology pretests and posttests in the 2nd and 17th weeks, respectively. In the 18th week, the students presented their projects using slideshows and were graded by judges while also submitting their final competition proposal and slides. Findings: Students in online problem-solving competitions can enhance their information technology, while those participating in showcase competitions can improve their competitive ability. Moreover, the competition type was found to moderate the relationships among theoretical knowledge, information technology, and AI model accuracy. The findings suggest that incorporating CBL into machine learning courses effectively cultivates students’ AI practical and competitive abilities.

Abstract The goal of this study was to investigate 65 students' evidence scores of emotions while they engaged in cognitive and metacognitive self-regulated learning processes as they learned about the circulatory system with MetaTutor, a hypermedia-based intelligent tutoring system. We coded for the accuracy of detecting students’ cognitive and metacognitive processes, and examined how the computed scores related to mean evidence scores of emotions and overall learning. Results indicated that mean evidence score of surprise negatively predicted the accuracy of making a metacognitive judgment, and mean evidence score of frustration positively predicted the accuracy of taking notes, a cognitive learning strategy. These results have implications for understanding the beneficial role of negative emotions during learning with advanced learning technologies. Future directions include providing students with feedback about the benefits of both positive and negative emotions during learning and how to regulate specific emotions to ensure the most effective learning experience with advanced learning technologies.

The aim of this research was to develop a learning recommendation component in an intelligent tutoring system (ITS) that dynamically predicts and adapts to a learner's style. In order to develop a proper ITS, we present an improved knowledge base supporting adaptive learning, which can be achieved by a suitable knowledge construction. This process is illustrated by implementing a web-based online tutor system. In addition, our knowledge structure provides adaptive presentation and personalized learning with the proposed adaptive algorithm, to retrieve content according to individual learner characteristics. To demonstrate the proposed adaptive algorithm, pre-test and post-test were used to evaluate suggestion accuracy of the course in a class for adapting to a learner's style. In addition, pre- and post-testing were also used with students in a real teaching/learning environment to evaluate the performance of the proposed model. The results show that the proposed system can be used to help students or learners achieve improved learning.

Machine learning courses play an important role in big data talent cultivation. To address problems in machine learning teaching, a big data talent cultivation oriented machine learning curriculum construction scheme is proposed. We build a theory and practice deeply integrated curriculum system, establish a set of teaching methods featured with “student centered, output based” educational philosophy, and improve the “hierarchical and course-competition integrated” assessment mechanism, which help students build their practical and innovative ability in big data analysis. Finally, the effectiveness of machine learning curriculum construction is verified by the

Artificial Intelligence (AI) and, more specifically, Machine Learning (ML) methodologies have successfully tailored commercial applications for decades. However, the recent profound success of large language models like ChatGPT and the enormous subsequent funding from governments and investors have positioned ML to emerge as a paradigm-shifting technology across numerous domains in the coming years. To cultivate a competent workforce and prepare students for success in this new AI-focused evolving world, the integration of ML is proposed to begin in compulsory education rather than in college courses or expensive boot camps. Unfortunately, ML is a complex and intimidating topic for high school teachers to engage with, let alone high school students. Based on our experiences hosting Machine Learning for High School Teachers (ML4HST) workshops for teachers teaching ML topics at our institution, we present in this paper various considerations for educating educators on the topic of ML. In particular, we discuss (a) overarching pedagogic strategies, (b) accessibility of resources such as computational hardware and datasets, (c) balancing theory and implementation, (d) appropriate selection of topics and activities for fostering understanding and engagement, and perhaps most importantly, (e) a compilation of pitfalls to avoid. Synthesizing these insights, we propose a framework for successfully empowering educators to introduce ML in the classroom.

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AI, and, more specifically, deep learning, is profoundly impacting our industries and societies [1]. In recent years, machine learning (ML) 's surging impact has sparked discourse about the importance of AI education for young people, and in recent years, several initiatives and projects pursuing the mission of K-12 AI education have emerged. In 2020 Israel's Ministry of Education (MoE) approved a new comprehensive and rigorous ML curriculum targeting 11 and 12th-grade pupils majoring in computer science (CS). The curriculum is meant to be taught by the existing CS teacher workforce. However, since ML theory and practice are fundamentally different from traditional CS [2], implementing this thorough ML curriculum poses substantial challenges in developing an effective teaching workforce to deliver it. In this research, we suggest a solution for this challenge in the form of a blended-learning (BL) program. The online component of this program is based mainly on Coursera's Deep Learning Specialization MOOCs series [3]. The BL program, enhanced with pedagogical training, is also used for the professional development (PD) of the teachers who deliver the program. Out of fourteen CS teachers who participated in the PD in the summer of 2021, ten teach the BL program this year to 273 high-school pupils. Initial results demonstrate the achievement of the curriculum learning goals and provide compelling preliminary evidence that this program enables CS teachers who are new to machine learning to teach this thorough curriculum effectively

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This study evaluates the effectiveness of a machine learning (ML) integrated science curriculum implemented within the Science Research Mentorship Program (SRMP) for high school youth at the American Museum of Natural History (AMNH) over 2 years. The 4-week curriculum focused on ML knowledge gain, skill development, and self-efficacy, particularly for under-represented youth in STEM. ML is increasingly prevalent in STEM fields, making early exposure to ML methods and artificial intelligence (AI) literacy crucial for youth pursuing STEM careers. However, STEM fields, particularly those focused on AI research and development, suffer from a lack of diversity. Learning experiences that support the participation of under-represented groups in STEM and ML are essential to addressing this gap. Participant learning was assessed through pre- and post-surveys measuring ML knowledge, skills, and self-efficacy. Results from the implementation of the curriculum show that participants gained understanding of ML knowledge and skills (p < 0.001, d = 1.083) and self-efficacy in learning ML concepts (p = 0.004, d = 0.676). On average, participants who identified as female and non-white showed greater learning gains than their white male peers (ML knowledge: p < 0.001, d = 1.191; self-efficacy: p = 0.006, d = 0.631), decreasing gaps in ML knowledge, skills, and self-efficacy identified in pre-survey scores. The ML-integrated curriculum effectively enhances students’ understanding and confidence in ML concepts, especially for under-represented groups in STEM, and provides a model for future ML education initiatives in informal science settings. We suggest that policy makers and school leaders take into account that high school age youth can learn ML concepts through integrated curricula while maintaining an awareness that curriculum effectiveness varies across demographic groups.

Abstract This paper discusses how artificial intelligence and machine learning can be incorporated into the new curricula of chemical engineering education as part of the transformation facing the challenges of Industry 4.0. It describes the ways in which AI tools, adaptive learning platforms, virtual laboratories, intelligent tutoring systems, and automatic assessments can significantly improve teaching effectiveness, resulting in corresponding increases in student learning outcomes. A set of indicative case studies from engineering programs is also analyzed to assess impacts based on a broad literature review. Results demonstrate that AI/ML applications provide opportunities for personalized instruction, leading to heightened conceptual understanding and accessible feedback support. Such benefits make AI/ML more preferable than traditional pedagogy, which often lacks scalability and adaptability to diverse circumstances. It also highlighted challenges related to data privacy, faculty preparedness, infrastructure limitations, and institutional resistance. The paper finally presents a set of practical recommendations for implementation, along with emerging trends, such as generative AI and learning analytics, that are shaping the future landscape of engineering education.

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PROBLEM Despite the rapidly expanding role of artificial intelligence (AI) and machine learning (ML) in health care, a significant knowledge gap remains among clinicians in their ability to evaluate and use AI and ML tools. APPROACH The Data-Augmented, Technology-Assisted Medical Decision Making (DATA-MD) curriculum was developed to teach fundamental AI and ML concepts to clinical trainees. The curriculum contains 4 modules: (1) Introduction to AI/ML in Healthcare, (2) Epidemiology and Biostatistics in AI/ML, (3) Use of AI/ML to Augment Diagnostic Decisions, and (4) Ethical and Legal Considerations of AI/ML in Healthcare. The curriculum was piloted in May and June 2023 among University of Michigan internal medicine residents and delivered to 2 cohorts of 11 and 12 residents. All learners completed presession and postsession assessments on AI and ML knowledge before and after the curriculum and a retrospective pre-post survey to evaluate familiarity with AI and ML concepts, comfort with AI and ML literature appraisal, and attitudes toward AI and ML use. OUTCOMES Twenty of 23 learners (87%) completed the presession knowledge assessment before participating in the DATA-MD sessions, and all 23 learners completed the postsession knowledge assessment and retrospective pre-post survey. Median knowledge scores significantly improved for modules 1 to 3 (module 1: 2.5 to 3.0, P = .008; module 2: 1.0 to 2.0, P = .049; module 3: 2.0 to 3.0, P < .001) but not module 4 (2.0 before and after testing; P = .80). Learners reported increased confidence in their abilities to appraise the AI and ML literature and use AI and ML tools in future practice. NEXT STEPS The DATA-MD curriculum pilot demonstrates that a standardized AI and ML curriculum can improve trainees' knowledge and attitudes about clinical AI and ML. Next steps include expansion to learners from different medical specialties, health professions, and academic institutions.

The field of Nursing and Midwifery Informatics (NMI) aims to equip healthcare professionals with the skills to efficiently use emerging technologies in their practice. This research assessed NMI educational programs in Ghana using machine learning techniques to analyze key factors influencing student performance, engagement, and satisfaction. Data was gathered from 1,500 students across C.K. Tedam University of Technology and Applied Sciences, Bolgatanga Nursing and Midwifery Training College, Regentropfen University College, Tamale Nursing and Midwifery Training College, and University for Development Studies. The study employed Random Forest, Gradient Boosting, Support Vector Machine, K-Nearest Neighbor, and Logistic Regression algorithms, evaluated using standard performance metrics, including accuracy, precision, and recall. The Gradient Boosting model achieved the highest predictive accuracy at 95%, identifying student engagement and curriculum satisfaction as the most influential predictors of academic success. Additionally, multiple regression analysis revealed that institutional differences significantly influenced academic outcomes, with students at Tamale Nursing and Midwifery Training College outperforming their counterparts at C.K. Tedam University of Technology and Applied Sciences (β = 3.85, p = 0.021), likely due to better alignment between their curriculum and instructional methods. These findings offer actionable insights for curriculum development and healthcare policy planning in resource-constrained settings, advocating for the integration of machine learning tools into academic evaluations. The study presents a scalable predictive model that can be adapted to enhance digital health education in similar low-resource settings worldwide, offering a pathway to more effective and inclusive healthcare education systems.

Abstract With the continuous development of social Internet technology, machine learning plays an important role in different industries, and the current education field is also experiencing the transformation from traditional teaching mode to data-driven intelligent teaching mode. In this paper, we provide an overview of the current status of the application of machine learning and educational big data analytics in education, discuss the commonly used machine learning algorithms and key technologies for educational data analytics, and at the same time, analyse the problems and challenges of curriculum reform, and elucidate the potentials of machine learning and educational big data in the process of solving these problems. In addition, this paper uses machine learning models to analyse students' learning behaviours, and also uses the models for personalised learning path recommendation, intelligent recommendation of teaching resources and other aspects. The application effect of the model in actual teaching activities is partially demonstrated through experimental research, and the effectiveness of the model is verified through data analysis. Finally, this paper summarises the research results and highlights the key role of machine learning-based big data analytics in education in promoting personalisation of education and improving teaching quality. The research in this paper not only provides educators with a new perspective on curriculum reform, but also provides educational decision makers with a reference for data-driven decision making, which is expected to promote the development of education in the direction of more intelligent and personalised education.

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As initiatives on AI education in K-12 learning contexts continues to evolve, researchers have developed curricula among other resources to promote AI across grade levels. Yet, there is a need for more effort regarding curriculum, tools, and pedagogy, as well as assessment techniques to popularize AI at the middle school level. Drawing on prior work, we created original curriculum activities with innovative use of existing technology, a new computational teaching tool, and a series of approaches and assessments to evaluate students’ engagement with the learning resources. Our curriculum called AI MyData comprises elements of ML and data science infused with ethical orientation. In this article, we describe the novel AI curriculum and further discuss how we engaged students in learning and critiquing AI ethical dilemmas. We gathered data from two pilot studies conducted in the Northeast United States, one Artificial Intelligence Afterschool (AIA) program, and one virtual AI summer camp. The AIA program was carried out in a local public school with four middle school students aged 12 to 13; the program consisted of eleven 2-hour sessions. The summer camp consisted of 2-hour sessions over 4 consecutive days, with 18 students aged 12 to 15. We facilitated both pilot programs with hands-on plugged and unplugged activities. The method of capturing data included artifact collection, structured interviews, written assessments, and a pre- to post-questionnaire tapping participants’ dispositions about AI and its societal implication. Participant artifacts, written assessments, survey, observation, and analysis of tasks completed revealed that the children improved in their knowledge of AI. In addition, the AI curriculum units and accompanying approaches developed for this study successfully engaged the participants, even without prior knowledge of related concepts. We also found an indication that introducing ethics of AI to adolescents will help their development as ethically responsive citizens. Our study results also indicate that lessons establishing links with students’ personal lives (e.g., letting students choose personally meaningful datasets) and societal implications using unplugged activities and interactive tools were particularly valuable for promoting AI and the integration of AI in middle school education across the subject domains and settings. Based on these results, we discuss our findings, identify their limitations, and propose future work.

Machine learning has become an increasingly popular area of research in the field of education, with potential applications in various aspects of higher education curriculum design. This study aims to review the current applications of AI in the curriculum design of higher education. We conducted an initial search for articles on the application of machine learning in curriculum design in higher education. This involved searching three core educational databases, including the Educational Research Resources Information Centre (ERIC), the British Education Index (BEI), and Education Research Complete, to identify relevant literature. Subsequently, this study performed network analysis on the included literature to gain a deeper understanding of the common themes and topics within the field. The results showed a growing trend in publishing research on the application of machine learning within the educational domain. Our review pinpointed merely 11 publications specifically targeting the application of machine learning in higher education course design, with only three being peer-reviewed articles. Through the word cloud visualization, we discerned the most prominent keywords to be AI, foreign countries, pedagogy, online courses, e-learning, and course design. Collectively, these keywords underscore the significance of AI in molding the educational landscape, as well as the expanding tendency to incorporate AI technologies into online and technology-enhanced learning experiences. Although there is a significant amount of research on the application of machine learning in education, the literature on its specific use in higher education course design still needs to be expanded. Our review identified only a small number of studies that directly focused on this topic, and among them. The network analysis generated from the included literature highlights important themes related to student learning and performance and the use of models and algorithms. However, there is still a need for further research in this area to fully understand the potential of machine learning in higher education course design. This study would contribute literature in this specific field. The review can update teacher’s awareness of using machine learning in teaching practice. Additionally, it implies more and more researchers conduct related research in this area. Future studies should consider the limitations of the existing literature and explore new approaches to incorporate machine learning into curriculum design to improve student learning outcomes.

BACKGROUND Over the years, approaches of the pharmaceutical industry to discover and develop drugs have changed rapidly due to new scientific trends. Among others, they have started to explore Machine Learning (ML), a subset of Artificial Intelligence (AI), as a promising tool to generate new hypotheses regarding drug candidate selections for clinical trials and to predict adverse side effects. Despite these recent developments, the possibilities of ML in pharmaceutical sciences have so far hardly penetrated the training of pharmaceutical science students. 1, 2 Therefore, as part of an elective course, an introductory module on ML was developed at Utrecht University, Department of Pharmaceutical Sciences. OBJECTIVE The aim of this study was to assess student' views on the module set-up, and their perspectives on ML within pharmaceutical science curricula. METHODS Semi-structured interviews over three years were conducted with 15 students participating in the module. RESULTS The students valued the well-designed and effective delivered module. They were personally motivated to learn more about ML in a future master or research internship. The students now perceive a lack of possibilities for ML training in pharmaceutical sciences education and indicate the value of incorporating ML opportunities for their future career. CONCLUSION Integrating ML training into pharmaceutical sciences curricula is needed to keep future drug researchers up to date with drug research advancements, enhancing their skills, academic development, and career prospects.

ABSTRACT Most educational systems use either an integrated or a separated science curriculum. However, it is unclear which of these science curricula benefits students more author and existing research provides insufficient information about the implementation details of the curriculum employed. Therefore, this study compares the effects of two science curricula on students’ science literacy, drawing on socio-ecological theory and employing educational data mining techniques. Results from Grade 8 Science students in 44 countries sampled in the Trends in International Mathematics and Science Study (TIMSS) 2019 showed that (1) the integrated curricula benefitted students marginally more than the separated curricula; (2) curriculum type was not essential in directly predicting students’ academic performance; and (3) random forest outperformed linear regression, lasso regression, decision trees, and neural networks in predicting student science achievement. This study advances our understanding of the predictors of student science performance, demonstrates that machine learning techniques can be applied successfully to examine curriculum effects, and provides directions for implementing integrated science curricula.

ABSTRACT The use of artificial intelligence in academia is a hot topic in the education field. ChatGPT is an AI tool that offers a range of benefits, including increased student engagement, collaboration, and accessibility. However, is also raises concerns regarding academic honesty and plagiarism. This paper examines the opportunities and challenges of using ChatGPT in higher education, and discusses the potential risks and rewards of these tools. The paper also considers the difficulties of detecting and preventing academic dishonesty, and suggests strategies that universities can adopt to ensure ethical and responsible use of these tools. These strategies include developing policies and procedures, providing training and support, and using various methods to detect and prevent cheating. The paper concludes that while the use of AI in higher education presents both opportunities and challenges, universities can effectively address these concerns by taking a proactive and ethical approach to the use of these tools.

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The rapid advancement of artificial intelligence (AI) technologies, particularly OpenAI’s ChatGPT, has significantly impacted higher education institutions (HEIs), offering opportunities and challenges. While these tools enhance personalized learning and content generation, they threaten academic integrity, especially in assessment environments. This study systematically examines the impact of ChatGPT on academic integrity in HEIs, focusing on exam design, assessment strategies, AI detection tools, and policy frameworks. This research draws from current literature and expert recommendations to identify practical approaches for developing assessments that foster critical thinking and deep cognitive engagement, making them less susceptible to AI-generated content. Key areas explored include the creation of complex, analytical exam formats, deploying advanced AI detection software to counter AI-assisted cheating, and formulating institutional policies that promote the ethical use of AI. This comprehensive framework aims to equip educators and administrators with practical strategies to preserve academic standards while harnessing the potential benefits of AI, ensuring the continued validity of assessments in the AI-driven educational landscape.

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The rapid adoption of AI-powered conversational agents such as ChatGPT is transforming the landscape of higher education in Hong Kong, offering both unprecedented opportunities for personalszed learning and complex challenges to academic integrity. This study investigates the perceptions and experiences of students across eight Hong Kong universities, employing a quantitative design that analyses questionnaire data from 200 students. Results reveal a pronounced polarisation: while students—particularly those with lower GPAs—appreciate ChatGPT’s capacity to clarify complex concepts and facilitate research, their counterparts with higher GPAs express deep concerns about dependency, plagiarism, and the erosion of critical thinking skills. The findings underscore the urgent need for Hong Kong universities to implement comprehensive policies, advanced AI-detection tools, and targeted educational initiatives that foster a culture of integrity and responsible AI use. This research contributes to ongoing debates about the integration of generative AI in higher education, advocating for localised, policy-driven solutions that balance innovation with ethical stewardship.

The release of ChatGPT has sparked significant academic integrity concerns in higher education. However, some commentators have pointed out that generative artificial intelligence (AI) tools such as ChatGPT can enhance student learning, and consequently, academics should adapt their teaching and assessment practices to embrace the new reality of living, working, and studying in a world where AI is freely available. Despite this important debate, there has been very little academic literature published on ChatGPT and other generative AI tools. This article uses content analysis to examine news articles (N=100) about how ChatGPT is disrupting higher education, concentrating specifically on Australia, New Zealand, the United States

This study examines the impact of AI, particularly ChatGPT, on academic integrity and assessment practices in higher education. As AI integration grows, concerns about its potential to undermine academic rigour and increase inequalities have surfaced. Through interviews with students and a lecturer, the research explores the benefits and challenges of using ChatGPT in academic work. The innovative approach of having students use ChatGPT to write assignments highlights both efficiency gains and the need for responsible use. Findings reveal the importance of using AI-generated content as a supplement rather than a replacement for traditional learning, with concerns about its potential misuse. The study advocates for updated integrity policies and clear guidelines to ensure AI enhances, rather than compromises, education. Emphasising ethical AI use and process-oriented assessments, the study offers strategies to promote fairness, integrity, and critical thinking in the digital age.

ABSTRACT More than a year has passed since reports of ChatGPT-3.5’s capability to pass exams sent shockwaves through education circles. These initial concerns led to a multi-institutional and multi-disciplinary study to assess the performance of Generative Artificial Intelligence (GenAI) against assessment tasks used across 10 engineering subjects, showcasing the capability of GenAI. Assessment types included online quiz, numerical, oral, visual, programming and writing (experimentation, project, reflection and critical thinking, and research). Twelve months later, the study was repeated using new and updated tools ChatGPT-4, Copilot, Gemini, SciSpace and Wolfram. The updated study investigated the performance and capability differences, identifying the best tool for each assessment type. The findings show that increased performance and features can only heighten academic integrity concerns. While cheating concerns are central, opportunities to integrate GenAI to enhance teaching and learning are possible. While each GenAI tool had specific strengths and weaknesses, ChatGPT-4 was well-rounded. A GenAI Assessment Security and Opportunity Matrix is presented to provide the community practical guidance on managing assessment integrity risks and integration opportunities to enhance learning.

This study aims to investigate academic integrity considerations of Generative Pre-Trained Transformer (ChatGPT) usage in assessment activities among the finalist student teachers at the University of Dar es Salaam. The study builds upon other previous studies on the topic of artificial intelligence (AI) tools. However, it is unique in terms of its context and the focus on integrity. A questionnaire was used to collect data from 383 finalist student teachers, and other 30 students were involved in focus group discussions (FGDs). Data for this study were analysed using IBM-SPSS Statistics Versions 25 to obtain descriptive statistics for the quantitative part and thematic analysis was used along with QDA Miner Lite for the qualitative part to generate themes. Student teachers consider ChatGPT as a transformative AI technology to ease the accomplishment of their assessment tasks. However, a large percentage of them did not pay much attention to academic integrity while using the AI tool. About 199 (52%) out of 383 respondents said ChatGPT is a useful tool in generating answers for their assessment tasks compared to 192 (50.1%) of respondents who had negative opinions. Given the sensitivity of the topic on academic integrity some participants were hesitant to share information until when the researchers clarified the purpose of the study. Participants were told that the information they would provide was purely for academic purpose. Also, this study was conducted when students’ regular classes were in progress, such that it was challenging to set universal time to conduct FGDs where all intended participants could attend. The researcher was flexible enough to find hours which were conducive for participants to participate in FGDs. For practitioners, given the fact that ChatGPT is a relatively new AI tool, training for raising awareness of its use should be conducted. Besides, specific academic integrity guidelines for its ethical use should be formulated and implemented. Likewise, instructors should set questions that prompt students original thinking which the AI tool cannot easily give contextualized solutions. The university where the AI tool is used is an open system that is within the society, and the impacts of AI technologies are inevitable in social domains. The society, therefore, should be informed about the emerging AI tools with their associated potentials and shortcomings to safeguard the norms and interests of the society. The study provides a comprehensive overview of student teachers’ ChatGPT use and its implication on academic integrity. Given the novelty nature of ChatGPT use in education, course instructors and students may benefit from insights into responsible utilization of this AI technology without compromising integrity and quality of education.

Abstract This mixed-methods study explored the perspectives of university instructors and learners of English as a foreign language (EFL) regarding ChatGPT’s effectiveness for EFL learning and its implications for academic integrity. Twenty-four instructors and 135 learners engaged with ChatGPT for one trimester. After a training session, instructors incorporated ChatGPT into their teaching. At the trimester’s end, questionnaires were distributed to gather participants’ opinions on their experience with ChatGPT. Instructors also provided open-ended responses and participated in semistructured interviews. Instructors and learners both had positive attitudes about artificial intelligence (AI) use in EFL learning, especially for written work. Learners acknowledged, however, that they might conceal their ChatGPT use from instructors; many said they would have ChatGPT complete their homework, if possible. Customizing responses within a word limit and generating multiple answers to similar prompts were identified as ChatGPT capabilities that could lead to misuse. This study’s practical implications, along with solutions for reducing the risk of AI-facilitated plagiarism, are discussed.

The advent of AI-powered writing tools such as ChatGPT has revolutionized academic writing, enhancing its efficiency and accessibility. This shift has raised concerns about academic integrity, particularly about plagiarism and the insufficiency of current detection techniques. This study examines the correlation between ChatGPT utilization and plagiarism in student writing, taking into account the impact of usage frequency, awareness of academic integrity, and the intended purpose of application on plagiarism rates. This research diverges from prior studies that concentrate just on plagiarism detection or AI ethics, examining how usage patterns may influence academic integrity by integrating behavioral and technological perspectives. The theoretical framework employs a quantitative methodology utilizing ANOVA tests to assess the influence of three independent variables—frequency of ChatGPT usage, awareness of academic integrity, and aim of application—on the dependent variable, plagiarism in student writing. The analysis indicates that students who often utilize ChatGPT exhibit elevated levels of plagiarism (F = 8.25, p = 0.003). Students utilizing this tool for content development exhibit significantly elevated plagiarism rates (F = 10.68, p = 0.002). Students who are cognizant of academic integrity exhibit markedly lower rates of plagiarism, (F = 12.47, p = 0.001). Stronger ethical rules, more academic integrity training, and advancements in AI detection technologies are required. Subsequent research will examine the long-term effects on critical thinking skills and develop strategies to both foster innovation and uphold academic integrity.

The purpose of this study was to examine ChatGPT-3’s capabilities to generate code solutions for assessment problems commonly assessed by automatic correction tools in the TEFL academic setting, focusing on the Kattis platform. The researcher explored potential implications for academic integrity and the challenges associated with AI-generated solutions. The investigation involved testing ChatGPT on a subset of 124 English language assessment tasks from Kattis, a widely used automatic software grading tool. The results revealed that ChatGPT independently solved 16 tasks successfully. Data analysis demonstrated that while ChatGPT performed well on simpler problems, it faced challenges with more complex assessment tasks. To supplement quantitative findings, a qualitative follow-up investigation was conducted, including interviews with two EFL assessment instructors. The discussion encompassed methodological considerations, the effectiveness of Kattis in preventing cheating, and the limitations in detecting AI-generated code. ChatGPT independently solved 16 out of 124 assessment tasks assessed by Kattis. Performance varied based on task complexity, with better accuracy on simpler problems. Qualitative insights revealed both the strengths and limitations of Kattis in preventing cheating. While ChatGPT demonstrates competence in solving certain assessment problems, challenges persist with more complex tasks. The study emphasizes the need for continuous adaptation in EFL assessment methodologies to maintain academic integrity in the face of evolving AI capabilities. As students gain access to sophisticated AI-generated solutions, the need for vigilant strategies to uphold originality and critical thinking in academic work becomes increasingly crucial. The study's findings have implications for multiple stakeholders, including (1) awareness of AI capabilities in generating code solutions, necessitating vigilant assessment strategies. (2) Understanding the importance of academic integrity and the limitations of AI in mastering complex assessment tasks. (3) Insights into the interplay between AI, automated assessment systems, and academic integrity, guiding future investigations. This performance illustrates the need for careful assessment design to mitigate the risk of AI-assisted academic dishonesty while maintaining rigorous academic standards.

The purpose of this research is to gain a complete understanding of how students and faculty in higher education perceive the role of AI tools, their impact on academic integrity, and their potential benefits and threats in the educational milieu, while taking into account ways to help curb its disadvantages. Drawing upon a qualitative approach, this study conducted in-depth interviews with a diverse sample of faculty members and students in higher education, in universities across Lebanon. These interviews were analyzed and coded using NVivo software, allowing for the identification of recurring themes and the extraction of rich qualitative data. The findings of this study illuminated a spectrum of perceptions. While ChatGPT and AI tools are recognized for their potential in enhancing productivity, promoting interactive learning experiences, and providing tailored support, they also raise significant concerns regarding academic integrity. This research underscores the need for higher education institutions to carefully navigate the integration of AI tools like ChatGPT. It calls for the formulation of clear policies and guidelines for their ethical and responsible use, along with comprehensive support and training. This study contributes to the existing literature by presenting a comprehensive exploration of the perceptions of both students and faculty regarding AI tools in higher education, through a qualitative rich approach. By delving into the intricate dynamics of ChatGPT and academic integrity, this study offers fresh insights into the evolving educational landscape and the ongoing dialogue between technology and ethics.

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Purpose The research aims to unravel the dynamics of academic integrity in the ChatGPT era by analyzing critical predictors such as personal best goals (PBG), academic competence and workplace stress. Furthermore, it examines how ChatGPT adoption acts as a moderating factor, potentially influencing the relationship between these predictors and academic integrity, offering a nuanced understanding of its impact in academic settings. Design/methodology/approach Relying on the social cognitive theory, the authors adopt a quantitative approach through an online survey to explore the impact of key variables – PBG, academic competence and workplace stress, as well as ChatGPT adoption on integrity – by analyzing data from responses collected through Academic Social Networking Sites. Findings The study found that PBG related positively to academic integrity among academic staff. However, workplace stress had a negative impact on academic integrity, while academic competence failed to report any effect. With the integration of ChatGPT adoption as a moderator into the model, the authors found the association between PBG and academic integrity altered to be negative. The ChatGPT adoption-moderated interactions of academic competence and workplace stress on academic integrity were significant. Practical implications The findings suggest that institutions should provide training on the ethical and effective use of artificial intelligence (AI) tools like ChatGPT to ensure they support rather than hinder academic integrity. Additionally, organizations should focus on stress management initiatives and fostering a balanced approach to personal goal setting to mitigate the potential negative impacts of ChatGPT adoption on ethical behavior. Originality/value This attempt is both timely and pioneering, addressing a clear gap in the current body of literature. As limited studies have examined the role of ChatGPT in academic settings, this work stands out as one of the earliest to investigate how educators and researchers incorporate OpenAI tools into their professional practices.

The rapid adoption of ChatGPT—a Generative AI chatbot—in higher education has raised concerns about academic integrity, ethical responsibility, and the development of critical-thinking skills. This cross-sectional study investigates the usage patterns and ethical perceptions of ChatGPT among 381 students (technical, undergraduate, and postgraduate) at a university in Colombia during the first semester of 2024. An anonymous online survey—distributed through snowball sampling—collected data on demographics, ChatGPT usage, perceptions, and its impact on academic assignments. Descriptive statistics and bivariate analyses were conducted using crude prevalence ratios (PR) and 95% confidence intervals to explore associations between sex, training, and perceptions. Results indicate that 46.7% of students used ChatGPT for evaluative assignments, 34.6% for essays, and 25.2% for research projects. Notably, 60.4% allowed ChatGPT to complete parts of their assignments, and 17.1% used it to fully complete at least one of the last four assignments. Only 7.6% received training on its optimal use. Male students were significantly more likely to participate in training courses (PR = 5.57; 95% CI: 2.54–12.22; p < 0.001), and this participation was associated with an increased perception of incoherent responses generated by ChatGPT (PR = 1.31; 95% CI: 1.09–1.58; p = 0.033). This study highlights the widespread use of ChatGPT and underscores the urgent need for enhanced training and the integration of ethical considerations into academic curricula. Such measures are essential for maximizing the benefits of generative AI while mitigating risks to academic integrity, ethical responsibility, and critical thinking.

The rapid development of Generative Artificial Intelligence (GenAI), particularly ChatGPT, has transformed educational practices and created both opportunities and challenges in teacher education. This study explores how pre-service EFL teachers use ChatGPT to develop their Technological Pedagogical Content Knowledge (TPACK) while maintaining academic integrity during teaching practice. Employing a sequential explanatory mixed-methods design, the research involved 100 survey respondents and 10 interview participants from Universitas Pendidikan Ganesha, Bali. Data were collected through questionnaires and semi-structured interviews and analyzed using descriptive statistics and thematic analysis. The results revealed that pre-service teachers frequently used ChatGPT to explore digital tools, design lesson plans, and create interactive learning activities, indicating that ChatGPT functions as a pedagogical partner in supporting TPACK integration. Moreover, the findings showed that pre-service teachers demonstrated strong awareness of ethical principles in AI use by paraphrasing, modifying, and verifying AI-generated content to ensure originality and avoid plagiarism. These practices reflect their ability to balance innovation with responsibility in digital learning environments. The study concludes that integrating AI literacy and ethical reflection into teacher education curricula is essential to prepare pre-service teachers as adaptive, reflective, and integrity-driven educators in the era of artificial intelligence.

The rapid advancements in large language models (LLMs) such as ChatGPT have raised concerns about their potential impact on academic integrity. While initial concerns focused on ChatGPT’s writing capabilities, recent updates have integrated DALL-E 3’s image generation features, extending the risks to visual evidence in biomedical research. Our tests revealed ChatGPT’s nearly barrier-free image generation feature can be used to generate experimental result images, such as blood smears, Western Blot, immunofluorescence and so on. Although the current ability of ChatGPT to generate experimental images is limited, the risk of misuse is evident. This development underscores the need for immediate action. We suggest that AI providers restrict the generation of experimental image, develop tools to detect AI-generated images, and consider adding “invisible watermarks” to the generated images. By implementing these measures, we can better ensure the responsible use of AI technology in academic research and maintain the integrity of scientific evidence.

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

An activity-based learning program with 285 hours of EKADA-based professional development was evaluated in this study. In Thailand's New Generation Teacher Training Initiative for Non-Degree Program (Project Code: FN68/0052), sponsored by the Office of the Permanent Secretary, Ministry of Higher Education, Science, Research, and Innovation, the EKADA model was applied. The program involved 42 teachers and educational personnel from Songkhla Province and nearby in four hands-on modules: AI and IoT for Modern Learning, Digital Learning Media Creation, AI and IoT Learning Innovation Design, Impact Assessment, and Professional Presentation, as well as a study tour to Huawei Technologies, NT (National Telecom), and Apple ICONSIAM. The study collected quantitative and qualitative data from July to November 2025 using a convergent parallel mixedmethods design. Results show significant learning: mean scores increased from 48.5% to 90.8%, a growth rate of 42.3 percentage points, 1.4 times the program's target of at least a score above mastery (30%). Out of 42 participants, 38 (90.48%) were credentialed, exceeding the norm of 80%. Participants created 38 learning innovation projects that reached 3,500 to 4,000 students and received a mean quality score of 4.42/5.00 from external expert evaluators. Overall satisfaction averaged 4.63/5.00 (“highest”). Qualitative analysis identified four implementation themes: structured hands-on practice, experiential sequencing, peer collaboration, and post-training support. The evidence suggests that the EKADA framework can develop teacher competencies for complex emerging technologies at scale with adequate resourcing and institutional commitment.

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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.

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

Artificial intelligence (AI) is emerging as a transformative force in teachers' professional development within contemporary educational systems. This study employs a mixed-methods approach, analyzing 217 Chinese and 500 English literature sources through CiteSpace-aided visualizations (keyword co-occurrence, clustering, and timeline mapping) to map global research trends. Key findings reveal AI's multidimensional contributions: supporting intelligent teaching practices and pedagogical research, facilitating teacher training through knowledge-sharing platforms, enhancing professional identity development, and providing adaptive learning resources for skill enhancement. However, critical challenges emerge in technology adoption barriers, data privacy risks, and unresolved ethical dilemmas in AI implementation. Cross-cultural analysis identifies context-specific variations in AI integration between Chinese and international educational ecosystems, prompting tailored strategies for technological adaptation. Future directions highlight AI's potential to revolutionize teacher mentorship programs, optimize educational administration, and enable personalized learning pathways, ultimately contributing to sustainable educational development. This comprehensive analysis bridges theoretical understanding with practical implications for AI-empowered educator development.

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

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

Public interest has surged around artificial intelligence (AI) due to new capabilities demonstrated by new AI chatbot technologies, such as ChatGPT. This study investigates teachers’ immediate perceptions, concerns, and professional development needs with respect to AI in a large urban school district. Using a mixed methods approach, we analyzed survey responses from 1,454 teachers to understand how teacher attitudes toward AI chatbots are associated with characteristics such as grade level taught, subject area, and teaching experience. Findings identify key concerns and benefits teachers report for themselves and for their students regarding the adaptation of AI chatbot usage in educational settings. Teachers in secondary school settings were less willing to allow AI chatbot usage, relative to those in elementary settings. While elementary and middle school teachers prioritized basic AI literacy as a professional development need, high school teachers emphasized strategies for guiding students to use AI and detecting AI-generated work. These findings offer insights for developing targeted professional development programs and policies for effective AI integration across K-12 settings.

Generative Artificial Intelligence (GenAI) is increasingly influencing teacher professional development, yet systematic understanding of its pedagogical affordances and implementation strategies remains limited. This critical review synthesizes 20 peer-reviewed studies (16 empirical and 4 conceptual) published between 2023 and 2025, examining GenAI framework development, implementation strategies, and affordance analysis in teacher professional development contexts. Our analysis identifies four affordance categories: functional (content co-creation, instructional design), cognitive (higher-order thinking, knowledge construction), social (human–AI partnership models), and reflective (self-efficacy building). While constructivist principles appear in approximately two-thirds of studies, fewer than one-third explicitly engage with the TPACK framework, highlighting theoretical gaps. Implementation challenges include limited prompt engineering training and variable ethical framework development. We propose the GenAI-Enhanced Teacher Professional Development (GenAI-TPD) framework comprising five core competency domains and four progressive developmental stages, operationalized through testable propositions and observable indicators that provide actionable guidance for researchers and practitioners.

Teacher Professional Development has historically been a challenging field, with several requirements such as the personalisation of training to meet teachers’ specific needs. With the rapid advancement of AI, there is now potential that points toward innovative approaches to these challenges. This paper introduces the term AI-TPD to describe the use of AI technologies to support and enhance Teacher Professional Development. A Scoping review was conducted following the PRISMA-ScR checklist and guidelines to identify the state of research of AI-TPD, to detect what are its current applications, to address the main challenges and risks it faces, and to highlight the emerging trends and future directions. The review found that AI applications primarily focus on personalized learning paths, real-time feedback, instructional design support, and fostering teacher reflection and practice. Reported benefits include increased adaptability and responsiveness of Professional Development, while challenges centre on ethical concerns, data privacy, AI distrust, and readiness. Evidence also highlights that AI-TPD is in an exploratory phase with limited theoretical frameworks and long-term evidence. The results map current applications of how AI can be used to make Professional Development more responsive and relevant in diverse teaching contexts. Recommendations for further research to continue exploring AI-TPD are also provided.

Artificial intelligence (AI) has moved from a peripheral innovation to an everyday presence in science education, where generative models, adaptive tutors, virtual laboratories, and data-rich simulations increasingly shape how learners encounter scientific knowledge. However, science teachers remain unevenly prepared to make pedagogically, ethically, and scientifically defensible decisions about these tools. Existing professional development often focuses on technical familiarity with AI applications, while giving less attention to the disciplinary practices of science teaching, such as inquiry, modelling, experimentation, data interpretation, evidence-based argumentation, and assessment. This article proposes a human-centred framework for teacher professional development in AI-supported science teaching. The framework is organised around five interlocking dimensions: foundational AI and data literacy contextualised in science; pedagogical content knowledge for AI-mediated inquiry; ethics, equity, and human agency; design competence for adaptive use and AI-supported assessment; and sustained, collaborative professional learning. Developed as a conceptual synthesis, the framework draws on the UNESCO AI Competency Framework for Teachers, recent extensions of TPACK into intelligent-TPACK and AI-TPACK, Human-Centric Artificial Intelligence Pedagogy, and emerging empirical studies on AI-supported classroom practice. It contributes by translating broad AI competency models into a science education-oriented professional development structure that foregrounds inquiry, modelling, experimentation, evidence-based reasoning, ethical judgement, and teacher agency. The article contributes to the literature by translating broad AI competency models into a science education-oriented professional development structure. It illustrates the framework through science-specific classroom scenarios and proposes a phased implementation roadmap from acquisition to deepening and creation. The article argues that effective AI-supported science teaching depends less on access to tools alone than on cultivating teachers who can critically evaluate, adapt, and humanize AI in the service of authentic scientific inquiry.

The integration of Artificial Intelligence (AI) in education has underscored the urgent need to equip educators with essential AI literacy and related competencies. This paper highlights the critical importance of advancing research on the development of teachers' AI literacy, particularly through targeted professional development programs. To address this need, the study piloted a training program involving 19 mid-career teachers in Singapore. Over the course of six weeks, participants engaged in an intensive 18-hour program designed to enhance their ability to integrate AI into educational practices, with a strong emphasis on ethical considerations. Mixed methods were employed. Data collection included pre-and post- intelligent Technological Pedagogical Content Knowledge (TPACK) surveys, teachers’ perceptions on AI, in-class group discussions, and post-individual written assignments. Data analyses included content analysis and quantitative data analysis. The results showed a significant enhancement in i-TPACK, accompanied by a shift in their overall perceptions of AI. The teachers not only acquired a good understanding of ethical frameworks but also demonstrated adept application in envisioning innovative AI in teaching, schools, and assessment. They formulated tailored action plans for implementing AI in their respective schools. Furthermore, the study employed a novel analytical matrix based on the Aristotelian tripartite division of knowledge—episteme, techne, and phronesis—to compare action plans between teachers with higher and lower perceived i-TPACK levels, focusing on AI’s application in teaching, schools, and assessment. This study contributes valuable insights into teacher professional development concerning AI in education and informs the implementation of AI in teaching practices.

This study explores the integration of technology and artificial intelligence (AI) as catalysts for professional teacher development within the context of Mexico’s educational challenges. Adopting a qualitative and exploratory approach, a four-phase workshop was conducted with 40 high school and university-level teachers from the National Polytechnic Institute (IPN). The methodology included scientific modeling activities using traditional methods, software (Tracker, ver. 6.2.0), and AI tools (ChatGPT-3.5), while analyzing participants’ perceptions and experiences. The findings reveal a clear disconnect between teachers’ theoretical competencies and their practical skills, with persistent gaps in scientific literacy at both educational levels. However, this study documents that the workshop functioned as a genuine professional learning community, where inter-academic collaboration and peer-learning proved to be an effective strategy for addressing these deficiencies. Technology, specifically the Tracker software, served as a catalyst for conceptual understanding. Despite AI’s potential for research, its limitations in the precision of responses reinforced this study’s central conclusion: technology does not replace the teacher’s work but transforms the teacher’s role into a critical mediator, responsible for guiding students to develop analytical and critical thinking in a complex digital environment.

ABSTRACT This study aims to provide a systematic review using the PRISMA protocol that overviews artificial intelligence (AI) professional development (PD) initiatives for teachers, emphasising the fusion of technological and pedagogical knowledge. Framed within the Technological Pedagogical Content Knowledge (TPACK) framework, the research explores the incorporation of TPACK domains, the specific AI tools taught, the structure and format of AI PD programs, the features contributing to effectiveness, and the tangible outcomes experienced by teachers’ post-PD participation. Through a systematic review, this study addresses critical research questions, contributing valuable insights to guide the strategic planning and assessment of AI initiatives in teacher learning, ultimately benefiting stakeholders interested in AI in education.

Generative AI holds transformative potential for education but also introduces ethical and pedagogical complexities, thereby necessitating comprehensive teacher preparation. To investigate whether mindset-oriented professional development (PD), beyond tool-focused training alone, enhances teachers’ responsible AI use, we conducted a mixed-method study comparing two PD approaches with 57 preservice teachers. Participants were randomly assigned to either a tools-only training group or a combined tools-and-mindset training group, including modules on AI ethics, human-centered education, and pedagogical reflection. Results revealed that both groups experienced decreased AI anxiety; yet only the tools-only group reported statistically significant gains in self-efficacy across multiple AI competence domains. Conversely, the mindset group exhibited a targeted increase in human-centered AI competence, reflecting deeper ethical awareness and critical reflection. Qualitative analysis confirmed that mindset-trained teachers articulated more subtle concerns about AI’s risks and pedagogical implications, adopting a more cautious stance and expressing a desire for further ethics-focused training. This study provides empirical evidence that embedding mindset components into AI-focused PD fosters essential reflective capacities and ethical judgment. Effective teacher PD should thus integrate technical proficiency with ethics, human-centered design, and critical pedagogy, cultivating educators capable of responsibly integrating AI in classrooms.

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

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

ABSTRACT While generative artificial intelligence (GenAI) chatbots show transformative potentials in English as a foreign language (EFL) education, critical gaps persist in understanding how chatbot-assisted language learning affect multidimensional learner engagement. Grounded in self-determination theory (SDT), this sequential explanatory mixed-methods study examines the predictive effect of perceived supports on learners’ engagement in chatbot-assisted language learning, and the mediating role of basic psychological needs (BPNs) in the relationship. Data from 506 Chinese EFL learners (quantitative phase: structural equation modeling of survey responses) and 18 follow-up interviewees (qualitative phase: thematic analysis) indicated that: (1) perceived supports had a significant and positive impact on cognitive and emotional engagement, as opposed to behavioral and agentic engagement; (2) perceived supports significantly and positively predicted BPNs (autonomy, competence, and relatedness); (3) BPNs significantly and positively influenced behavioral, cognitive, emotional, and agentic engagement; and (4) perceived supports indirectly affected emotional and agentic engagement through autonomy, and behavioral engagement through relatedness, while competence consistently mediated all four engagement dimensions. Furthermore, qualitative insights from the semi-structured interviews triangulated the quantitative results, offering a comprehensive understanding of EFL learners’ engagement. The findings shed light on the future research avenues and the practical deployment of GenAI chatbots in EFL education.

In an era where digital tools are becoming central to education, chatbots offer a unique opportunity to facilitate language learning through interactive dialogue. This study investigates the integration of chatbot technology into English language learning and its impact on enhancing student engagement and interactive communication skills. The research involved a random selection of participants from a university English course, aiming to assess how chatbot interactions can bolster communication skills. Data was collected through pre- and post-implementation assessments, focusing on students’ speaking proficiency across various tasks. The analysis employed advanced analytical models such as t-tests to determine improvements in fluency, pronunciation, intonation, and stress patterns. A mixed-methods approach was utilized, incorporating surveys to gather students’ perceptions of chatbot usage in their learning process. This procedure involved implementing chatbot interactions during the course, allowing students to practice speaking and receive immediate feedback. Results indicated significant improvements in students’ overall speaking abilities, particularly in fluency and intonation. While no notable differences were observed in pronunciation between proficiency levels, substantial advancements were found in interactive speaking tasks. The findings highlight the potential of chatbot technology to improve student engagement and communication skills in English language learning. This research underscores the necessity for further exploration into innovative educational tools to support language acquisition in diverse learning environments.

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Given the importance of conversation practice in language learning, chatbots, especially ChatGPT, have attracted considerable attention for their ability to converse with learners using natural language. This review contributes to the literature by examining the currently unclear overall effect of using chatbots on language learning performance and comprehensively identifying important study characteristics that affect the overall effectiveness. We meta-analyzed 70 effect sizes from 28 studies, using robust variance estimation. The effects were assessed based on 18 study characteristics about learners, chatbots, learning objectives, context, communication/interaction, and methodological and pedagogical designs. Results indicated that using chatbots produced a positive overall effect on language learning performance (g = 0.484), compared to nonchatbot conditions. Moreover, four characteristics (i.e., educational level, language level, interface design, and interaction capability) affected the overall effectiveness. In an in-depth discussion on how the 18 characteristics are related to the effectiveness, future implications for practice and research are presented.

Language learners increasingly rely on intelligent digital tools to supplement their learning experiences, yet existing chatbots often provide limited support, lacking adaptability, personalization, or domain-specific intelligence. This study introduces a novel AI-powered multi-agent chatbot architecture designed to support English–Arabic translation and language learning. Developed through a three-phase methodology, offline preparation, real-time deployment, and evaluation, the system employs both retrieval-based and generative AI models, with specialized agents managing tasks such as translation, example retrieval, user translation review, and learning feedback. The chatbot was developed using a hybrid architecture incorporating fine-tuned Generative Pre-trained Transformer (GPT) model, sentence embedding techniques, and similarity evaluation metrics. A user study involving 40 undergraduate students and 4 faculty members evaluated the system across usability, effectiveness, and pedagogical value. Results show that the multi-agent chatbot significantly enhanced learner engagement, provided accurate and contextually appropriate language support, and was positively received by both students and instructors. These findings demonstrate the value of multi-agent design in language learning applications and highlight the potential of AI-driven chatbots as intelligent educational assistants.

The growing integration of artificial intelligence (AI) chatbots in English language learning has sparked increasing interest in their potential to enhance learners’ willingness to communicate (WTC) in English. Nonetheless, comprehensive syntheses of their impact remain limited. This study presents a narrative meta-synthesis of empirical evidence on AI chatbot-assisted English learning in the Asian English as a Foreign Language (EFL) context, including Iran, South Korea, Taiwan, China, Saudi Arabia, Egypt, Thailand, and Vietnam. A systematic search of prominent databases (Scopus, Web of Science, EBSCO, and Google Scholar) yielded pertinent studies, which were thematically analyzed to synthesize quantitative and qualitative results. The synthesis reveals that regular chatbot interaction enhances WTC, communicative confidence, and motivation by reducing speaking anxiety and providing adaptive, nonjudgmental feedback. Learners reported increased fluency and engagement when using AI chatbots such as Google Assistant, Alexa, and ChatGPT, although technological limitations and limited conversational depth occasionally hindered sustained interaction. Although most studies show moderate to strong short-term effects, evidence on long-term transfer to real-world communication remains scarce. Overall, the findings emphasize AI chatbots as valuable mediators of communicative readiness and confidence among Asian EFL learners, while emphasizing the necessity of longitudinal and context-sensitive research to refine their pedagogical application.

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Growth mindset, the belief in enhancing abilities through effort and dedication, is commonly applied in classroom learning. The Mindset Theory Scale suggests it can be measured by "effort" and "belief in improvement", while fixed mindset is indicated by "procrastination" and "immutability of belief". In today’s interconnected world, the need for proficiency in multiple languages has grown. However, mastering different languages poses several challenges, including motivational challenges and fear of failure. To address this issue, our study explores the use of an adaptable Large Language Model (LLM) based chatbot to foster growth mindset and aid in language learning. In a user study with this novel chatbot system, we evaluated the impact of a growth mindset teaching style on new language learning and user perception. Our initial findings show that users are more comfortable, confident, and interested in interacting with the growth mindset chatbot compared to the fixed mindset chatbot.

The rapid advancement of technology has revolutionized language learning, introducing innovative methods that depart from traditional instructional approaches. This study employs a mixed-methods research design to examine the impact of chatbot-based language learning support, adaptive learning algorithms, and virtual reality language immersion on the language learning proficiency and self-regulated learning skills of English as a Foreign Language (EFL) learners. The research design includes quantitative analysis of language proficiency scores and qualitative exploration of learner experiences with the technological interventions. The theoretical implications of this study are rooted in constructivist and socio-cultural learning theories, which underpin the design and implementation of technological interventions. The triangulation of qualitative and quantitative data revealed that the participants' positive perceptions of the effectiveness of chatbot-based language learning support were supported by the quantitative results, with the variable "chat-bot based support" demonstrating a substantial mean difference compared to other groups. This convergence of findings reinforces the positive influence of chatbot-based support on language learning outcomes and highlights the importance of integrating theoretical perspectives with empirical evidence to gain a comprehensive understanding of the impact of technological interventions on language learning outcomes for EFL learners. The study's findings provide insights into the potential of these technological interventions to optimize language learning outcomes for EFL learners and promote autonomous learning behaviors.

Today, many students are having a hard time learning to speak the English language and the problem is even worse for those in rural areas, where both teachers and resources are rare. We are trying to break this disparity and provide better resources for those in need utilizing Artificial Intelligence and Natural Language Processing (NLP) to contribute to the SDG4 Quality and Inclusive Education. In this paper, we present the idea of how we developed Chatbot named BuddyBot Utilizing OpenAI Generative Pre-Trained Transformer 2 (GPT-2) and Google Text-to-Text Transfer Transformer (FLAN-T5) models. This system incorporates diverse datasets for fine-tuning and specialized tasks in English language learning. BuddyBot follows a five-phase architecture - User interface, Pre-processing, Natural Language Processing (NLP) stack, language generation engine and adaptive learning personalization. The E-Learn web-based interface offers interactive courses with BuddyBot assistance, emphasizing adaptive learning personalization. Dataset preprocessing includes tokenization, sentiment analysis, and specialized formatting. The NLP stack integrates GPT-2 and FLAN-T5 models into the framework, maintaining dialogue structure and personalization. Validation tests reveal model effectiveness, with reduced training loss (0.778 to 0.414 by epoch 8) and increased accuracy (0.810 training, 0.792 validation). Overall, BuddyBot's robust methodology and validation results showcase its efficacy in providing personalized English language learning experiences.

There are important areas that have not been fully investigated. The use of chatbots to improve students' language learning abilities presents various advantages and obstacles. This study explores the integration of Chatbots in English language instruction for EFL learners, focusing on their impact on learning implementation experiences, advantages, and limitations. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) standards, the research reviewed articles from 2019 to 2024 to ensure a comprehensive analysis. A total of 998 publications were identified through Google Scholar, with search terms such as "chatbot," "AI chatbot," and "Chatbot implementation among EFL learners in English language learning." After an extensive screening process, including duplicate removal, inclusion, and exclusion criteria, 10 empirical, open-access studies were selected for review. The findings reveal that ChatGPT significantly enhances writing, grammar, and vocabulary skills by providing immediate and personalized feedback. It also fosters engagement and motivation while supporting cultural exploration and intercultural competence. However, limitations include reduced efficacy in developing speaking and listening skills, technical challenges, and ethical concerns. The study concludes that while ChatGPT is a valuable tool, it should complement, not replace, traditional teaching methods to ensure a balanced and effective learning experience.

This study explores the potential of chatbot applications to enhance English language learning in Nigerian secondary schools, motivated by the ongoing challenges in achieving high English proficiency using traditional methods. The purpose of the study was to assess the current level of English proficiency among students, evaluate the effectiveness of chatbot applications, understand user perceptions, and identify implementation challenges. A quantitative research design was employed, involving 150 students from three international schools, selected through stratified random sampling. Data were collected using a structured questionnaire and analyzed using descriptive and inferential statistics, along with thematic analysis for qualitative insights. The study revealed varied levels of English proficiency among students’ general positive perceptions of chatbot applications as effective tools for learning English; significant technical and accessibility challenges, such as poor internet connectivity and limited device availability; and the necessity of comprehensive training for both students and teachers to maximize the benefits of chatbot applications. The study concludes that while chatbot applications have considerable potential to improve English language skills, addressing the identified challenges is crucial. Recommendations included enhancing infrastructure, providing comprehensive training, and improving the software and user interface of chatbot applications to maximize their educational benefits. These insights aim to inform educators, policymakers, and technology developers on optimizing the use of chatbot applications in language education.

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Artificial Intelligence (AI) chatbots, with their ability to engage in conversations that resemble human interactions, have been increasingly applied to language teaching. Most recent review studies overlook student learning outcomes and the methods to achieve these outcomes in chatbot-supported language learning. Activity Theory (AT) offers a framework of elements and functions inside an activity system to accomplish desired objectives. This systematic study intends to specify student learning outcomes in a chatbot-supported setting and explain how various factors such as rules, tools, and division of labor work together to enhance learning outcomes in this environment. This review included 37 papers published from January 2014 to January 2025. The findings provide two empirical contributions: the four types of outcomes and the use of AT-based approaches to achieve these outcomes. Additionally, two practical suggestions are made: creating instructional design models for teacher-AI collaboration in chatbot-assisted language learning and developing professional AI chatbots for language education. Furthermore, five research directions are proposed: teacher-AI chatbot interactions, agentic outcomes, out-of-school context, chatbot and human-chatbot collaborations, and K-12 education setting. The findings indicate how to use factors from AT to assist students leaning language effectively with AI chatbots.

—Lack of opportunities is a significant hurdle for English as a Foreign Language (EFL) for students during their learning journey. Previous studies have explored the use of chatbots as learning partners to address this issue. However, the success of chatbot implementation depends on the quality of the reference dialogue content, yet research focusing on this subject is still limited. Typically, human experts are involved in creating suitable dialogue materials for students to ensure the quality of such content. Research attempting to utilize artificial intelligence (AI) technologies for generating dialogue practice materials is relatively limited, given the constraints of existing AI systems that may produce incoherent output. This research investigates the potential of leveraging OpenAI's ChatGPT, an AI system known for producing coherent output, to generate reference dialogues for an EFL chatbot system. The study aims to assess the effectiveness of ChatGPT in generating high-quality dialogue materials suitable for EFL students. By employing multiple readability metrics, we analyze the suitability of ChatGPT-generated dialogue materials and determine the target audience that can benefit the most. Our findings indicate that ChatGPT's dialogues are well-suited for students at the Common European Framework of Reference for Languages (CEFR) level A2 (elementary level). These dialogues are easily comprehensible, enabling students at this level to grasp most of the vocabulary used. Furthermore, a substantial portion of the dialogues intended for CEFR B1 (intermediate level) provides ample stimulation for learning new words. The integration of AI-powered chatbots in EFL education shows promise in overcoming limitations and providing valuable learning resources to students.

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As chatbots evolve from scripted responders to reasoning companions, their role in language education is rapidly shifting. Yet, while their presence is expanding, a deeper understanding of how they interact-not just respond-with learners remains elusive. This systematic review repositions interaction as the core of chatbot-assisted language learning, drawing on 100 peer-reviewed studies published between 2009 and 2024. Through a three-phase grounded coding approach to extract and synthesize these literature, we identify four distinct interaction styles, such as interlocutor, narrator, entertainer, and facilitator, each shaping engagement across pedagogical, affective, and cognitive domains. We find that large language models (LLMs) have broadened the educational affordances of chatbots, particularly in writing, speaking, and emotional support. However, their potential to foster higher-order thinking remains largely untapped. Limitations such as hallucination, bias, and ethical risks are seldom addressed in practice, underscoring the need for more critical integration. We argue that effective use depends not only on technological sophistication, but on prompt design, emotional connection, and teachers as ethical gatekeepers.