Pub Date : 2025-12-01DOI: 10.1016/j.caeai.2025.100516
Lok Ming Eric Cheung , On-Ting Lo , Huiwen Shi
As generative AI (GenAI) becomes increasingly embedded in higher education, this study examines how students' perceived AI ability (AIA) relates to their independent learning disposition (INL) and academic self-efficacy (ASE). We administered a quantitative survey to 302 undergraduate students in Hong Kong. Results indicated significant positive correlations among AIA, INL, and ASE. Mediation analysis further showed that INL mediates the association between AIA and ASE: students who perceive themselves as more capable with AI also report stronger independent learning dispositions, which in turn are linked to higher academic self-efficacy. We discuss the potential of AI tools to scaffold self-directed learning and strengthen students’ academic confidence, outline pedagogical implications for embedding AI competency training in curricula, and propose directions for future research.
{"title":"Exploring the relationships among perceived AI ability, academic self-efficacy and independent learning disposition in the tertiary contexts","authors":"Lok Ming Eric Cheung , On-Ting Lo , Huiwen Shi","doi":"10.1016/j.caeai.2025.100516","DOIUrl":"10.1016/j.caeai.2025.100516","url":null,"abstract":"<div><div>As generative AI (GenAI) becomes increasingly embedded in higher education, this study examines how students' perceived AI ability (AIA) relates to their independent learning disposition (INL) and academic self-efficacy (ASE). We administered a quantitative survey to 302 undergraduate students in Hong Kong. Results indicated significant positive correlations among AIA, INL, and ASE. Mediation analysis further showed that INL mediates the association between AIA and ASE: students who perceive themselves as more capable with AI also report stronger independent learning dispositions, which in turn are linked to higher academic self-efficacy. We discuss the potential of AI tools to scaffold self-directed learning and strengthen students’ academic confidence, outline pedagogical implications for embedding AI competency training in curricula, and propose directions for future research.</div></div>","PeriodicalId":34469,"journal":{"name":"Computers and Education Artificial Intelligence","volume":"9 ","pages":"Article 100516"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145681352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The identification of intelligent individuals through culturally pertinent and objective evaluation frameworks is essential for the development of talent and the advancement of education. This study introduces a novel composite intelligence evaluation system that is specifically tailored to the socio-cultural and educational environment of Kazakhstan. The framework encompasses three critical domains: educational achievement, cognitive capabilities, and inventive performance. The study introduces the predictive intelligence analysis model (PIAM) and the dynamic intelligence scoring algorithm (DISA) to evaluate and predict high-potential individuals. A hierarchical weighted multi-metric integration model (HWMMIM) is employed in the methodology to evaluate the efficacy of innovation. This model incorporates sophisticated mathematical formulations, such as polynomial weighted GPA, harmonic mean-based cognitive indexes, and a recursive aggregation model. The DISA model obtained an AUC-ROC of 0.95, precision of 91 %, recall of 89 %, and accuracy of 94 % on a dataset consisting of 10,000 individuals. The composite intelligence score (CIS) is modified through logistic transformation to facilitate the probabilistic interpretation of classification problems. The proposed models facilitate strategic initiatives such as “Kazakhstan 2050″ by enabling the identification of intellectual talent through the use of scalable, data-driven methodologies. In comparison to conventional IQ-based approaches, this research not only demonstrates improved prediction efficacy but also establishes a reproducible framework for culturally adaptive intelligence modeling in developing countries.
{"title":"A composite intelligence scoring framework for identifying high-potential individuals using multi-metric predictive models","authors":"Abdul Razaque , Zhuldyz Kalpeyeva , Uskenbayeva Raissa Kabiyevna , Ryskhan Zhakanovna Satybaldiyeva , Yulia Vladimirovna Ferens , Shynara Sarkambayeva","doi":"10.1016/j.caeai.2025.100508","DOIUrl":"10.1016/j.caeai.2025.100508","url":null,"abstract":"<div><div>The identification of intelligent individuals through culturally pertinent and objective evaluation frameworks is essential for the development of talent and the advancement of education. This study introduces a novel composite intelligence evaluation system that is specifically tailored to the socio-cultural and educational environment of Kazakhstan. The framework encompasses three critical domains: educational achievement, cognitive capabilities, and inventive performance. The study introduces the predictive intelligence analysis model (PIAM) and the dynamic intelligence scoring algorithm (DISA) to evaluate and predict high-potential individuals. A hierarchical weighted multi-metric integration model (HWMMIM) is employed in the methodology to evaluate the efficacy of innovation. This model incorporates sophisticated mathematical formulations, such as polynomial weighted GPA, harmonic mean-based cognitive indexes, and a recursive aggregation model. The DISA model obtained an AUC-ROC of 0.95, precision of 91 %, recall of 89 %, and accuracy of 94 % on a dataset consisting of 10,000 individuals. The composite intelligence score (CIS) is modified through logistic transformation to facilitate the probabilistic interpretation of classification problems. The proposed models facilitate strategic initiatives such as “Kazakhstan 2050″ by enabling the identification of intellectual talent through the use of scalable, data-driven methodologies. In comparison to conventional IQ-based approaches, this research not only demonstrates improved prediction efficacy but also establishes a reproducible framework for culturally adaptive intelligence modeling in developing countries.</div></div>","PeriodicalId":34469,"journal":{"name":"Computers and Education Artificial Intelligence","volume":"9 ","pages":"Article 100508"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145681353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01DOI: 10.1016/j.caeai.2025.100510
Zhiwei Xie, Tse-Tin Chan, Philip L.H. Yu
Scene description tasks effectively enhance students' English writing skills in contextual settings, facilitating the establishment of authentic situational connections. However, evaluating descriptive quality and providing accurate, level-appropriate feedback present significant challenges. Although Multimodal Large Language Models (MLLMs) have demonstrated strong capabilities in vision-language tasks, their generated feedback for scene description tasks often remains generic. It fails to account for students' educational stages. To address this limitation, we construct a novel level-specific feedback dataset for scene description tasks. This dataset is constructed using GPT-4o with Retrieval-Augmented Generation (RAG), guided by the Hong Kong primary and secondary school English word lists, which categorize vocabulary into four educational stages (key stages 1–4). We fine-tuned a designed MLLM on this dataset and evaluated its performance against open-source and closed-source baselines. Experimental results demonstrate that the proposed fine-tuned MLLM significantly enhances educational stage relevance in feedback generation while reducing hallucinated content. These findings substantiate the efficacy of fine-tuned MLLM in providing level-specific feedback for scene description tasks, advancing the potential for more adaptive AI-assisted writing support in educational contexts.
场景描述任务有效地提高了学生在语境中的英语写作能力，促进了真实情景联系的建立。然而，评估描述的质量和提供准确的、水平适当的反馈提出了重大挑战。尽管多模态大语言模型（Multimodal Large Language Models, mllm）在视觉语言任务中表现出了强大的能力，但它们在场景描述任务中生成的反馈往往是通用的。它没有考虑到学生的教育阶段。为了解决这一限制，我们为场景描述任务构建了一个新的特定级别反馈数据集。该数据集使用gpt - 40与检索增强生成（RAG）构建，以香港中小学英语单词表为指导，将词汇分为四个教育阶段（关键阶段1-4）。我们在这个数据集上对设计的MLLM进行了微调，并根据开源和闭源基线评估了它的性能。实验结果表明，所提出的微调MLLM显著提高了反馈生成的教育阶段相关性，同时减少了幻觉内容。这些发现证实了微调后的MLLM在为场景描述任务提供特定级别反馈方面的有效性，提高了在教育环境中更自适应的人工智能辅助写作支持的潜力。
{"title":"Level-specific feedback generation for scene descriptions via fine-tuning multimodal large language models","authors":"Zhiwei Xie, Tse-Tin Chan, Philip L.H. Yu","doi":"10.1016/j.caeai.2025.100510","DOIUrl":"10.1016/j.caeai.2025.100510","url":null,"abstract":"<div><div>Scene description tasks effectively enhance students' English writing skills in contextual settings, facilitating the establishment of authentic situational connections. However, evaluating descriptive quality and providing accurate, level-appropriate feedback present significant challenges. Although Multimodal Large Language Models (MLLMs) have demonstrated strong capabilities in vision-language tasks, their generated feedback for scene description tasks often remains generic. It fails to account for students' educational stages. To address this limitation, we construct a novel level-specific feedback dataset for scene description tasks. This dataset is constructed using GPT-4o with Retrieval-Augmented Generation (RAG), guided by the Hong Kong primary and secondary school English word lists, which categorize vocabulary into four educational stages (key stages 1–4). We fine-tuned a designed MLLM on this dataset and evaluated its performance against open-source and closed-source baselines. Experimental results demonstrate that the proposed fine-tuned MLLM significantly enhances educational stage relevance in feedback generation while reducing hallucinated content. These findings substantiate the efficacy of fine-tuned MLLM in providing level-specific feedback for scene description tasks, advancing the potential for more adaptive AI-assisted writing support in educational contexts.</div></div>","PeriodicalId":34469,"journal":{"name":"Computers and Education Artificial Intelligence","volume":"9 ","pages":"Article 100510"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145736667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01DOI: 10.1016/j.caeai.2025.100505
Ehsan Namaziandost , Fidel Çakmak
Artificial Intelligence (AI) and gamified learning have attracted interest from language educators and researchers in the field of teaching English as a Foreign Language (EFL) for their potential to enhance vocabulary acquisition outcomes. However, the relative effectiveness of AI-generated storytelling and gamified learning in EFL vocabulary acquisition has been underexplored. This study examines the influence of AI-generated storytelling and gamified learning on the vocabulary retention and engagement of intermediate-level EFL learners. Ninety participants were allocated among three groups: a control group that employed conventional vocabulary methods (e.g., rote memorization using flashcards and quizzes), a gamified learning group that utilized Duolingo's exercises, and an AI-generated interactive narrative group. Over a four-week period, the gamified group completed interactive exercises, the control group followed conventional instruction, and the AI group engaged with personalized, context-rich narratives that embedded target vocabulary. Data was gathered through pre- and post-tests, which included immediate and delayed vocabulary assessments, a learner engagement questionnaire, and semi-structured interviews. The interviews were analyzed using grounded theory. The AI-generated storytelling group outperformed both the gamified learning and control groups in both immediate and delayed vocabulary tests, as evidenced by quantitative results. This suggests that the AI-generated storytelling group exhibited superior long term vocabulary retention. The control group was also surpassed by the gamified learning group in both assessments, albeit to a lesser extent. The qualitative results suggested that the AI-generated storytelling group reported a higher level of engagement, attributing their motivation to immersive and meaningful narratives. The gamified learning group found the approach to be enjoyable but less profound, and the control group described traditional methods as structured yet uninspiring. These results indicate that AI-generated storytelling is a potent instrument for improving vocabulary acquisition and engagement in EFL settings.
{"title":"Impact of AI-generated storytelling vs. gamified learning on vocabulary retention and engagement in CALL environments","authors":"Ehsan Namaziandost , Fidel Çakmak","doi":"10.1016/j.caeai.2025.100505","DOIUrl":"10.1016/j.caeai.2025.100505","url":null,"abstract":"<div><div>Artificial Intelligence (AI) and gamified learning have attracted interest from language educators and researchers in the field of teaching English as a Foreign Language (EFL) for their potential to enhance vocabulary acquisition outcomes. However, the relative effectiveness of AI-generated storytelling and gamified learning in EFL vocabulary acquisition has been underexplored. This study examines the influence of AI-generated storytelling and gamified learning on the vocabulary retention and engagement of intermediate-level EFL learners. Ninety participants were allocated among three groups: a control group that employed conventional vocabulary methods (e.g., rote memorization using flashcards and quizzes), a gamified learning group that utilized Duolingo's exercises, and an AI-generated interactive narrative group. Over a four-week period, the gamified group completed interactive exercises, the control group followed conventional instruction, and the AI group engaged with personalized, context-rich narratives that embedded target vocabulary. Data was gathered through pre- and post-tests, which included immediate and delayed vocabulary assessments, a learner engagement questionnaire, and semi-structured interviews. The interviews were analyzed using grounded theory. The AI-generated storytelling group outperformed both the gamified learning and control groups in both immediate and delayed vocabulary tests, as evidenced by quantitative results. This suggests that the AI-generated storytelling group exhibited superior long term vocabulary retention. The control group was also surpassed by the gamified learning group in both assessments, albeit to a lesser extent. The qualitative results suggested that the AI-generated storytelling group reported a higher level of engagement, attributing their motivation to immersive and meaningful narratives. The gamified learning group found the approach to be enjoyable but less profound, and the control group described traditional methods as structured yet uninspiring. These results indicate that AI-generated storytelling is a potent instrument for improving vocabulary acquisition and engagement in EFL settings.</div></div>","PeriodicalId":34469,"journal":{"name":"Computers and Education Artificial Intelligence","volume":"9 ","pages":"Article 100505"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145614520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01DOI: 10.1016/j.caeai.2025.100506
Lindai Xie , Yingying Jiang , Chi-Ning Chang , Xin-Ying Zeng , Jun Hong , Fangfang Mo
This multi-informant mixed-methods study uses a concurrent parallel sampling approach to investigate undergraduate students' and faculty's perceptions of utilizing AI in teaching and learning at U.S. universities. A survey developed based on the Technology Acceptance Model, Social Influence Theory, and existing literature was implemented to collect undergraduate students' data regarding students' perceived AI learning environment, perceived others' attitudes toward AI, and personal attitudes toward AI. Faculty's opinions were collected through semi-structured interviews in accordance with the survey variables. Quantitative findings indicated that the effect of the AI learning environment on students' personal attitudes toward AI was fully mediated by their perceptions of others' attitudes. This finding highlights the critical role of perceived others' attitudes towards AI since students tend to adapt to the AI learning environment by mirroring the attitudes they perceive from others. The qualitative findings explored faculty's use of AI tools, their attitudes toward AI and students' usage, the challenges they experienced, and the need for clear guidance and support to facilitate better incorporation of AI into their professional practices. The integration of quantitative and qualitative phases compares students' and faculty's usage and attitudes toward AI and brings important insights that focus on improving the AI-using environment, ensuring sufficient financial support, and offering professional training for both faculty and students. Based on the findings, students can be guided in developing informed attitudes about AI utilization through faculty's demonstration of appropriate AI usage, fostering meaningful conversations about AI integration, and experiential learning opportunities to practice AI-assisted learning.
{"title":"How are faculty and college students embracing AI? — A multi-informant mixed method study","authors":"Lindai Xie , Yingying Jiang , Chi-Ning Chang , Xin-Ying Zeng , Jun Hong , Fangfang Mo","doi":"10.1016/j.caeai.2025.100506","DOIUrl":"10.1016/j.caeai.2025.100506","url":null,"abstract":"<div><div>This multi-informant mixed-methods study uses a concurrent parallel sampling approach to investigate undergraduate students' and faculty's perceptions of utilizing AI in teaching and learning at U.S. universities. A survey developed based on the Technology Acceptance Model, Social Influence Theory, and existing literature was implemented to collect undergraduate students' data regarding students' perceived AI learning environment, perceived others' attitudes toward AI, and personal attitudes toward AI. Faculty's opinions were collected through semi-structured interviews in accordance with the survey variables. Quantitative findings indicated that the effect of the AI learning environment on students' personal attitudes toward AI was fully mediated by their perceptions of others' attitudes. This finding highlights the critical role of perceived others' attitudes towards AI since students tend to adapt to the AI learning environment by mirroring the attitudes they perceive from others. The qualitative findings explored faculty's use of AI tools, their attitudes toward AI and students' usage, the challenges they experienced, and the need for clear guidance and support to facilitate better incorporation of AI into their professional practices. The integration of quantitative and qualitative phases compares students' and faculty's usage and attitudes toward AI and brings important insights that focus on improving the AI-using environment, ensuring sufficient financial support, and offering professional training for both faculty and students. Based on the findings, students can be guided in developing informed attitudes about AI utilization through faculty's demonstration of appropriate AI usage, fostering meaningful conversations about AI integration, and experiential learning opportunities to practice AI-assisted learning.</div></div>","PeriodicalId":34469,"journal":{"name":"Computers and Education Artificial Intelligence","volume":"9 ","pages":"Article 100506"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145614595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01DOI: 10.1016/j.caeai.2025.100518
Ming Ma , Davy Tsz Kit Ng , Zhichun Liu , Jionghao Lin , Gary K.W. Wong
While artificial intelligence (AI) education is expanding globally, the implementation of AI ethics education in K-12 AI curricula remains a critical challenge, particularly in top-down curriculum reform contexts like China. This study investigates the factors influencing secondary information technology (IT) teachers' intention to teach AI ethics, grounding in Theory of Planned Behaviour (TPB). Through semi-structured interviews with 14 in-service teachers in China's Greater Bay Area (GBA), we identified key behavioural beliefs (e.g., fostering critical thinking, navigating rapid technological change, and adapting western-centric ethics), normative beliefs (e.g., new curriculum policy, school-level expectations, and student interest), and control beliefs (e.g., deficient content knowledge, misaligned professional development, and classroom constraints). These factors interactively influence teachers' attitudes, social norms and perceived behavioural control, shaping their intentions to implement AI ethics education in their classroom practices. The findings reveal that while some teachers recognize the importance of teaching AI ethics, their intentions of implementing this domain are predominantly constrained by low perceived behavioural control and social norms that prioritizes technical aspects. This study advocates for collaborative professional learning where teachers develop competencies of AI ethics through group moral reasoning engaging with core ethical principles, which in turn enables them to reflect on pedagogical designs for creating active learning activities, thereby bridging the gap between policy commitment and classroom practice.
虽然人工智能（AI）教育正在全球范围内扩展，但在K-12人工智能课程中实施人工智能伦理教育仍然是一项重大挑战，特别是在中国这样自上而下的课程改革背景下。本研究以计划行为理论（Theory of Planned behavior， TPB）为基础，探讨影响中学信息技术（IT）教师人工智能伦理教学意向的因素。通过对中国大湾区（GBA） 14名在职教师的半结构化访谈，我们确定了关键的行为信念（例如，培养批判性思维、驾驭快速技术变革和适应以西方为中心的伦理）、规范信念（例如，新课程政策、学校水平期望和学生兴趣）和控制信念（例如，内容知识不足、专业发展不一致和课堂约束）。这些因素相互作用地影响教师的态度、社会规范和感知的行为控制，塑造他们在课堂实践中实施人工智能伦理教育的意愿。研究结果显示，虽然一些教师认识到教授人工智能伦理的重要性，但他们实施这一领域的意图主要受到低感知行为控制和优先考虑技术方面的社会规范的限制。本研究倡导协作式专业学习，教师通过与核心伦理原则相结合的群体道德推理来培养人工智能伦理能力，这反过来使他们能够反思创造主动学习活动的教学设计，从而弥合政策承诺与课堂实践之间的差距。
{"title":"Why don't teachers teach AI ethics? Understanding teachers' beliefs and intentions in Chinese AI curriculum implementation through the theory of planned behaviour","authors":"Ming Ma , Davy Tsz Kit Ng , Zhichun Liu , Jionghao Lin , Gary K.W. Wong","doi":"10.1016/j.caeai.2025.100518","DOIUrl":"10.1016/j.caeai.2025.100518","url":null,"abstract":"<div><div>While artificial intelligence (AI) education is expanding globally, the implementation of AI ethics education in K-12 AI curricula remains a critical challenge, particularly in top-down curriculum reform contexts like China. This study investigates the factors influencing secondary information technology (IT) teachers' intention to teach AI ethics, grounding in Theory of Planned Behaviour (TPB). Through semi-structured interviews with 14 in-service teachers in China's Greater Bay Area (GBA), we identified key behavioural beliefs (e.g., fostering critical thinking, navigating rapid technological change, and adapting western-centric ethics), normative beliefs (e.g., new curriculum policy, school-level expectations, and student interest), and control beliefs (e.g., deficient content knowledge, misaligned professional development, and classroom constraints). These factors interactively influence teachers' attitudes, social norms and perceived behavioural control, shaping their intentions to implement AI ethics education in their classroom practices. The findings reveal that while some teachers recognize the importance of teaching AI ethics, their intentions of implementing this domain are predominantly constrained by low perceived behavioural control and social norms that prioritizes technical aspects. This study advocates for collaborative professional learning where teachers develop competencies of AI ethics through group moral reasoning engaging with core ethical principles, which in turn enables them to reflect on pedagogical designs for creating active learning activities, thereby bridging the gap between policy commitment and classroom practice.</div></div>","PeriodicalId":34469,"journal":{"name":"Computers and Education Artificial Intelligence","volume":"9 ","pages":"Article 100518"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145736668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01DOI: 10.1016/j.caeai.2025.100520
King Woon Yau , Tianle Dong , Ching Sing Chai , Thomas K.F. Chiu , Helen Meng , Irwin King , Savio W.H. Wong , Yeung Yam
Teachers play a vital role in driving successful artificial intelligence (AI) education. Research on teachers' competency to teach AI (TCAI) is still limited. This study investigated the progression of in-service teachers' AI competency with the Technological Pedagogical Content Knowledge (TPACK) framework using Epistemic Network Analysis (ENA). Seven secondary school teachers who engaged in an AI education project were interviewed over a three-year period of curriculum development and implementation. The differences in ENA patterns in various stages indicated an evolution of teachers’ TPACK over the years. The ENA results also revealed different patterns between experienced and less experienced teachers. Experienced teachers tend to integrate their TPACK components with pedagogical considerations, whereas less experienced teachers focus more on content-related elements. The differences in ENA patterns indicate distinct progression paths with different focuses, highlighting the need to tailor professional development activities for different groups of teachers at various stages. These findings underscore the importance of continuous support and targeted training to enhance teachers' AI competency in AI education.
{"title":"Epistemic network analysis of in-service teachers’ competency to teach artificial intelligence for secondary education","authors":"King Woon Yau , Tianle Dong , Ching Sing Chai , Thomas K.F. Chiu , Helen Meng , Irwin King , Savio W.H. Wong , Yeung Yam","doi":"10.1016/j.caeai.2025.100520","DOIUrl":"10.1016/j.caeai.2025.100520","url":null,"abstract":"<div><div>Teachers play a vital role in driving successful artificial intelligence (AI) education. Research on teachers' competency to teach AI (TCAI) is still limited. This study investigated the progression of in-service teachers' AI competency with the Technological Pedagogical Content Knowledge (TPACK) framework using Epistemic Network Analysis (ENA). Seven secondary school teachers who engaged in an AI education project were interviewed over a three-year period of curriculum development and implementation. The differences in ENA patterns in various stages indicated an evolution of teachers’ TPACK over the years. The ENA results also revealed different patterns between experienced and less experienced teachers. Experienced teachers tend to integrate their TPACK components with pedagogical considerations, whereas less experienced teachers focus more on content-related elements. The differences in ENA patterns indicate distinct progression paths with different focuses, highlighting the need to tailor professional development activities for different groups of teachers at various stages. These findings underscore the importance of continuous support and targeted training to enhance teachers' AI competency in AI education.</div></div>","PeriodicalId":34469,"journal":{"name":"Computers and Education Artificial Intelligence","volume":"9 ","pages":"Article 100520"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145681342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01DOI: 10.1016/j.caeai.2025.100474
Matthew Nyaaba , Xiaoming Zhai
As educators begin using Generative AI (GenAI) for lesson planning, they often encounter generated content that fails to consider the classroom's cultural context. In this study, we address this issue by adopting a design science research approach to develop a theory-based prompt grounded in culturally responsive pedagogy (CRP) and using it to customize a Culturally Responsive Lesson Planner (CRLP) GPT. Guided by the CRP framework, the CRLP uses an InteractiveSemi-Automated (ISA) prompt architecture that engages teachers in dialogue to collect cultural and contextual details before generating a lesson plan. To evaluate the CRLP's effectiveness, we asked two expert reviewers to compare Grade 7 “States of Matter” lesson plans for Ghana's Ashanti Region, generated with both the CRLP and the base GPT-4o using a standard prompt. The expert reviewers rated the CRLP-generated lesson plan higher in cultural elements identified (36 vs. 21 elements), accuracy (1.8 vs. 1.2), and curriculum relevance (2.0 vs. 1.3) than that created by the standard prompt within the base GPT-4o. The CRLP-generated lesson plan also included more Asante Twi examples such as “Solid” (ɛpono [wooden furniture], dadeɛ [metal objects], aboɔ [stones], and ntadeɛ [clothing]), recommended local teaching resources, and allowed teachers to make final revisions before generating the complete lesson plan. Additionally, the CRLP included the developer's contact details to encourage ongoing feedback and improvement. However, cultural hallucinations were slightly higher (0.75 vs. 0.5) in the CRLP-generated lesson plan compared with the standard GPT-4o prompt. These findings suggest that a GenAI tool grounded in educational theory is more effective in supporting the goals of education than the standard version. Furthermore, the CRLP and its ISA prompt strategy represent Human-in-the-loop system that has the potential to enhance teachers' AI literacy and culturally responsive pedagogy as they engage with the tool. We recommend future studies comparing CRLP and human-generated lesson plans, as well as empirical research that tests CRLP lesson plans in classroom settings.
{"title":"Developing a theory-grounded AI tool for the generation of culturally responsive lesson plans","authors":"Matthew Nyaaba , Xiaoming Zhai","doi":"10.1016/j.caeai.2025.100474","DOIUrl":"10.1016/j.caeai.2025.100474","url":null,"abstract":"<div><div>As educators begin using Generative AI (GenAI) for lesson planning, they often encounter generated content that fails to consider the classroom's cultural context. In this study, we address this issue by adopting a design science research approach to develop a theory-based prompt grounded in culturally responsive pedagogy (CRP) and using it to customize a <em>Culturally Responsive Lesson Planner (CRLP) GPT.</em> Guided by the CRP framework, the CRLP uses an <em>Interactive</em> <em>S</em><em>emi-</em><em>Automated</em> (ISA) prompt architecture that engages teachers in dialogue to collect cultural and contextual details before generating a lesson plan. To evaluate the CRLP's effectiveness, we asked two expert reviewers to compare Grade 7 “States of Matter” lesson plans for Ghana's Ashanti Region, generated with both the CRLP and the base GPT-4o using a standard prompt. The expert reviewers rated the CRLP-generated lesson plan higher in cultural elements identified (36 vs. 21 elements), accuracy (1.8 vs. 1.2), and curriculum relevance (2.0 vs. 1.3) than that created by the standard prompt within the base GPT-4o. The CRLP-generated lesson plan also included more Asante Twi examples such as “Solid” (<em>ɛpono</em> [wooden furniture], <em>dadeɛ</em> [metal objects], <em>aboɔ</em> [stones], and <em>ntadeɛ</em> [clothing]), recommended local teaching resources, and allowed teachers to make final revisions before generating the complete lesson plan. Additionally, the CRLP included the developer's contact details to encourage ongoing feedback and improvement. However, cultural hallucinations were slightly higher (0.75 vs. 0.5) in the CRLP-generated lesson plan compared with the standard GPT-4o prompt. These findings suggest that a GenAI tool grounded in educational theory is more effective in supporting the goals of education than the standard version. Furthermore, the CRLP and its ISA prompt strategy represent Human-in-the-loop system that has the potential to enhance teachers' AI literacy and culturally responsive pedagogy as they engage with the tool. We recommend future studies comparing CRLP and human-generated lesson plans, as well as empirical research that tests CRLP lesson plans in classroom settings.</div></div>","PeriodicalId":34469,"journal":{"name":"Computers and Education Artificial Intelligence","volume":"9 ","pages":"Article 100474"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145681348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01DOI: 10.1016/j.caeai.2025.100524
Danial Hooshyar , Gustav Šír , Yeongwook Yang , Eve Kikas , Raija Hämäläinen , Tommi Kärkkäinen , Dragan Gašević , Roger Azevedo
Despite significant advancements in AI-driven educational systems and ongoing calls for responsible AI for education, several critical issues remain unresolved—acting as elephant in the room within AI in education, learning analytics, educational data mining, learning sciences, and educational psychology communities. This critical analysis identifies and examines nine persistent challenges across the conceptual, methodological, and ethical dimensions that continue to undermine the fairness, transparency, and effectiveness of current AI methods and applications in education. These include: 1) the lack of clarity around what AI for education truly means—often ignoring the distinct purposes, strengths, and limitations of different AI families—and the trend of equating it with domain-agnostic, company-driven large language models; 2) the widespread neglect of essential learning processes such as motivation, emotion, and (meta)cognition in AI-driven learner modelling and their contextual nature; 3) limited integration of domain knowledge and lack of stakeholder involvement in AI design and development; 4) continued use of non-sequential machine learning models on temporal educational data; 5) misuse of non-sequential metrics to evaluate sequential models; 6) using unreliable explainable AI methods to provide explanations for black-box models; 7) ignoring ethical guidelines in addressing data inconsistencies during model training; 8) use of mainstream AI methods for pattern discovery and learning analytics without systematic benchmarking; and 9) overemphasis on global prescriptions while overlooking localized, student-specific recommendations. Supported by theoretical and empirical research, we demonstrate how hybrid AI methods—specifically neural-symbolic AI—can address the elephant in the room and serve as the foundation for responsible, trustworthy AI systems in education.
{"title":"Towards responsible AI for education: Hybrid human-AI to confront the elephant in the room","authors":"Danial Hooshyar , Gustav Šír , Yeongwook Yang , Eve Kikas , Raija Hämäläinen , Tommi Kärkkäinen , Dragan Gašević , Roger Azevedo","doi":"10.1016/j.caeai.2025.100524","DOIUrl":"10.1016/j.caeai.2025.100524","url":null,"abstract":"<div><div>Despite significant advancements in AI-driven educational systems and ongoing calls for responsible AI for education, several critical issues remain unresolved—acting as <em>elephant in the room</em> within AI in education, learning analytics, educational data mining, learning sciences, and educational psychology communities. This critical analysis identifies and examines nine persistent challenges across the conceptual, methodological, and ethical dimensions that continue to undermine the fairness, transparency, and effectiveness of current AI methods and applications in education. These include: 1) the lack of clarity around what AI for education truly means—often ignoring the distinct purposes, strengths, and limitations of different AI families—and the trend of equating it with domain-agnostic, company-driven large language models; 2) the widespread neglect of essential learning processes such as motivation, emotion, and (meta)cognition in AI-driven learner modelling and their contextual nature; 3) limited integration of domain knowledge and lack of stakeholder involvement in AI design and development; 4) continued use of non-sequential machine learning models on temporal educational data; 5) misuse of non-sequential metrics to evaluate sequential models; 6) using unreliable explainable AI methods to provide explanations for black-box models; 7) ignoring ethical guidelines in addressing data inconsistencies during model training; 8) use of mainstream AI methods for pattern discovery and learning analytics without systematic benchmarking; and 9) overemphasis on global prescriptions while overlooking localized, student-specific recommendations. Supported by theoretical and empirical research, we demonstrate how hybrid AI methods—specifically neural-symbolic AI—can address the <em>elephant in the room</em> and serve as the foundation for responsible, trustworthy AI systems in education.</div></div>","PeriodicalId":34469,"journal":{"name":"Computers and Education Artificial Intelligence","volume":"9 ","pages":"Article 100524"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145681351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01DOI: 10.1016/j.caeai.2025.100512
Ayşe Candan Şimşek , Gerrit Anders , Jonathan Göth , Luisa Specht , Markus Huff
Online videos have become a central tool in modern education. Alongside this shift, Artificial Intelligence (AI) is reshaping personalized learning experiences, with generative large language models like ChatGPT offering new ways to tailor information to individual learners. Based on the Cognitive Theory of Multimedia Learning (CTML), which proposes two principles that relate to the interaction with the learning material (segmenting and generative activity), we conducted two experiments in which participants were asked to pause an educational video at times of comprehension difficulty. In Experiment 1 (N = 101), we examined whether GPT-generated summaries -introduced at self-paced pause points-result in better learning compared to video transcripts. In Experiment 2 (N = 215), we compared the role of GPT-generated summaries and GPT-generated reflective prompts. Those elicited open-ended answers from the participants. We measured retention and transfer learning, as well as mental effort, and perceived task difficulty. Contrary to our expectations, we observed no differences between AI summaries and transcripts in terms of retention and transfer outcomes. Participants showed a learning effect indicating more correct answers after watching the video, but this effect did not differ between conditions. We can especially note that the motivation to engage in the material, as well as the difficulty and length of the video, may have affected the results. As research investigating the role of AI in educational settings is still new, future research can delve into finding the optimal conditions under which AI can benefit learning outcomes.
{"title":"Is ChatGPT a good study companion? The role of AI-generated summaries and reflective prompts in learning from educational videos","authors":"Ayşe Candan Şimşek , Gerrit Anders , Jonathan Göth , Luisa Specht , Markus Huff","doi":"10.1016/j.caeai.2025.100512","DOIUrl":"10.1016/j.caeai.2025.100512","url":null,"abstract":"<div><div>Online videos have become a central tool in modern education. Alongside this shift, Artificial Intelligence (AI) is reshaping personalized learning experiences, with generative large language models like ChatGPT offering new ways to tailor information to individual learners. Based on the Cognitive Theory of Multimedia Learning (CTML), which proposes two principles that relate to the interaction with the learning material (segmenting and generative activity), we conducted two experiments in which participants were asked to pause an educational video at times of comprehension difficulty. In Experiment 1 (<em>N</em> = 101), we examined whether GPT-generated summaries -introduced at self-paced pause points-result in better learning compared to video transcripts. In Experiment 2 (<em>N</em> = 215), we compared the role of GPT-generated summaries and GPT-generated reflective prompts. Those elicited open-ended answers from the participants. We measured retention and transfer learning, as well as mental effort, and perceived task difficulty. Contrary to our expectations, we observed no differences between AI summaries and transcripts in terms of retention and transfer outcomes. Participants showed a learning effect indicating more correct answers after watching the video, but this effect did not differ between conditions. We can especially note that the motivation to engage in the material, as well as the difficulty and length of the video, may have affected the results. As research investigating the role of AI in educational settings is still new, future research can delve into finding the optimal conditions under which AI can benefit learning outcomes.</div></div>","PeriodicalId":34469,"journal":{"name":"Computers and Education Artificial Intelligence","volume":"9 ","pages":"Article 100512"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145614497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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