Journal of Computer Assisted Learning最新文献

{"title":"The Contribution of Generative Artificial Intelligence as a Novice Learner to Students in the Learning by Teaching Model","authors":"Haiyan Wang, Congyao Wang, Jiawen Liu, Mei Cao","doi":"10.1002/jcal.70299","DOIUrl":"https://doi.org/10.1002/jcal.70299","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Learning by Teaching (LbT) is regarded as an effective generative learning approach. However, its effectiveness is often constrained by implementation conditions, as well as the cognitive level and motivation of students acting as learners, limiting its full potential. With the widespread application of generative artificial intelligence (GAI) in education, this approach has gained new opportunities for development. Nevertheless, existing studies have mainly focused on the “student questions–GAI answers” interaction, which essentially remains a form of passive learning and fails to promote deep knowledge construction.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Objectives</h3>\u0000 \u0000 <p>This study introduces GAI into the LbT model, assigning it the role of a novice learner. Students shift from knowledge receivers to knowledge constructors, aiming to enhance their knowledge understanding, self-efficacy and metacognitive strategies.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>The participants were 68 preservice teachers, including 33 in the experimental group and 35 in the control group. Two GAI roles were designed: generative artificial intelligence as a novice learner (GAI-NL) and generative artificial intelligence as a teacher (GAI-T). Students in the experimental group explained knowledge to GAI-NL, while students in the control group asked questions to GAI-T.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The results indicate that students in the experimental group showed significant improvement in the knowledge comprehension dimension. Their self-efficacy was also significantly higher than that of the control group. Regarding metacognitive strategies, the experimental group scored significantly higher than the control group on the dimension of metacognitive knowledge and learning strategies; however, no significant difference was found between the groups in the ability to plan and monitor learning. In terms of attitudes toward the GAI role, the experimental group reported a higher mean score on perceived usefulness, whereas no significant difference was observed between the groups on perceived ease of use.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>The GAI-NL role operationalizes and extends the LbT model, creating a replicable and transferable learning environment. Through dialogue strategies such as Request for Explanation, Request for Examples, Verification Reasoning and Request for Supplementation, it guides students to deepen their understanding of knowledge. Students' self-efficacy was strengthened, and their knowle","PeriodicalId":48071,"journal":{"name":"Journal of Computer Assisted Learning","volume":"42 5","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148617248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the Impact of AI-Based Learning Environments on Student Self-Regulation and Adaptive STEM Learning","authors":"Ataallh Aodh Alatoai,&nbsp;Ali Saleh Alshahri","doi":"10.1002/jcal.70297","DOIUrl":"https://doi.org/10.1002/jcal.70297","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Although AI-based STEM environments provide adaptive feedback and personalised learning pathways, empirical evidence explaining <i>how</i> these systems shape students' self-regulation and adaptive problem-solving processes remains limited. Prior research has largely focused on perceptions of AI use or tool validation rather than modelling the cognitive mechanisms underlying adaptive STEM learning.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>This study examines how AI-supported STEM environments influence secondary school students' self-regulated learning processes and identifies which metacognitive components most strongly predict adaptive STEM performance.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A total of 649 secondary students engaged in structured AI-enhanced STEM learning activities. Interaction-derived indicators of metacognitive monitoring, strategic adjustment, cognitive flexibility and transfer were analysed. Network analysis was employed to model the structural interrelations among cognitive and metacognitive processes, and multiple regression models were conducted to determine their predictive contribution to adaptive STEM learning outcomes.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Network modelling revealed a densely connected structure among metacognitive and adaptive learning processes, with cognitive transfer and adaptability (CTA) and AI-enhanced self-regulated learning (ASRL) emerging as central nodes. Regression analyses indicated that metacognitive and adaptive components significantly predicted adaptive STEM performance, explaining 68% of the variance in learning outcomes (<i>R</i>² = 0.68, <i>F</i>[4, 55] = 31.74, <i>p</i> &lt; 0.001). AI-based metacognitive awareness (<i>β</i> = 0.38, <i>p</i> &lt; 0.001) and cognitive transfer and adaptability (<i>β</i> = 0.29, <i>p</i> &lt; 0.001) were the strongest predictors of adaptive learning performance.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>AI-supported STEM environments foster adaptive learning primarily through strengthening students' metacognitive awareness and cognitive transfer processes. These findings clarify the cognitive mechanisms through which AI feedback enhances learning and provide empirical guidance for designing intelligent systems that promote transferable and self-regulated STEM competencies.</p>\u0000 </section>\u0000 </div>","PeriodicalId":48071,"journal":{"name":"Journal of Computer Assisted Learning","volume":"42 5","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148617249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research Trends on the TPACK in Science Education From 2009 to 2023: A Systematic Content Analysis","authors":"İdris Aktaş,&nbsp;Haluk Özmen","doi":"10.1002/jcal.70287","DOIUrl":"https://doi.org/10.1002/jcal.70287","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>The number of TPACK studies has increased significantly in recent years, aiming to explore effective ways to support teachers. However, subject-specific content has received limited attention, especially within review-based research. Therefore, there is a need for a systematic analysis of TPACK studies in the context of subject content.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Objectives</h3>\u0000 \u0000 <p>This study aims to reveal the research trends of articles published on TPACK in science education from 2009 to 2023 through systematic content analysis.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A total of 90 articles published in English retrieved from the Web of Science, ERIC, and Scopus databases were analysed. The articles were analysed according to the following themes: research rationales, research objectives, examined variables, technological tools/teaching methods/science content domains, research methods, implementation processes, sampling groups, data collection tools, and main results.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The articles were generally carried out to determine the TPACK levels of science teachers and to develop their competencies for integrating technology into science teaching such as wikis, Facebook, WhatsApp, Moodle, 3D technologies, mobile devices, virtual reality, or new teaching methods such as inquiry-based instruction supported by technology. Many studies reported that teachers demonstrated relatively low or moderate levels of TPACK, including in some studies conducted after professional development programs. In recent years the articles focused on new research areas such as examining the impact of TPACK-based science teaching on student learning outcomes, developing teachers' inquiry-based teaching skills with digital technologies, and linking TPACK with educational robotics, ICT, and STEM education in science education.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>TPACK-based professional development programs in science education improve teachers' professional knowledge and skills. TPACK research has expanded with new pedagogical methods and technological advances. However, domain-specific TPACK remains underexplored.</p>\u0000 </section>\u0000 </div>","PeriodicalId":48071,"journal":{"name":"Journal of Computer Assisted Learning","volume":"42 5","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jcal.70287","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148616125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Human or GenAI Support? Conditions Impacting Students' Strategy Choices in an Essay Revision Task","authors":"Sehrish Iqbal,&nbsp;Jelena Jovanović,&nbsp;Yizhou Fan,&nbsp;Mladen Raković,&nbsp;Xinyu Li,&nbsp;Guanliang Chen,&nbsp;Dragan Gašević","doi":"10.1002/jcal.70300","DOIUrl":"https://doi.org/10.1002/jcal.70300","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Students often struggle to effectively self-regulate their learning strategies during writing tasks and typically require external support from teachers, peers or AI tools to overcome these challenges. While emerging research explores how generative AI can dialogically interact with students to support the writing process, little is known about how such AI support influences writing strategies during the essay-revision task. Furthermore, there is limited understanding of how GenAI support compares to other external conditions (human feedback or no support) in shaping writing strategies and how it interacts with internal learning factors such as writing skills, metacognitive accuracy and learning motivation.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Objectives</h3>\u0000 \u0000 <p>Our study aims to investigate the relationship of self-regulated learning (SRL) strategies that students adopt for an essay-revision task with learning conditions (both internal and external) and learning performance.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Using a technology-enhanced learning environment, we collected trace data from 87 participants during essay-writing and revision tasks. We applied Mixture Markov Models (MMMs) to model the writing/revision processes, then used the resulting transition matrix within the Expectation–Maximization (EM) clustering algorithm to identify SRL strategies. A series of statistical tests were applied to examine the relationship of strategies that students adopt for a revision task with learning conditions (internal and external) and learning performance.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results and Conclusions</h3>\u0000 \u0000 <p>We found moderate associations between writing strategies during the essay-revision task and internal conditions (writing strategies adopted in the preceding essay-writing task) and large associations with external conditions (external support). However, no association was found between writing strategies during the essay-revision task and learning performance. These findings inform the development of educational interventions to improve students' writing strategies.</p>\u0000 </section>\u0000 </div>","PeriodicalId":48071,"journal":{"name":"Journal of Computer Assisted Learning","volume":"42 5","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jcal.70300","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148616592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Effects of Out-of-Class AI-Generated Feedback on EFL Learners' Writing Achievement and Emotions: Evidence From a Quasi-Experimental Study","authors":"Ge Qu,&nbsp;Zhenhua Lyu,&nbsp;Qing Yang","doi":"10.1002/jcal.70293","DOIUrl":"https://doi.org/10.1002/jcal.70293","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Research has investigated how AI-generated feedback affects L2 writing development. Less attention has been paid, however, to students' emotional experiences when using generative AI tools in informal language learning or to how these experiences relate to writing outcomes.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Objectives</h3>\u0000 \u0000 <p>This study examined EFL learners' writing performance and emotional experiences in relation to their use of AI-generated feedback beyond the classroom.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Guided by the broaden-and-build theory, this quasi-experimental mixed-methods study involved 110 Chinese EFL learners enrolled in an Academic Writing course in northern China. Participants were recruited from two intact Academic Writing classes, with one existing class assigned to the experimental group (<i>n</i> = 55) and the other existing class assigned to the control group (<i>n</i> = 55). Both groups received the same classroom instruction on academic writing. Compared with the control group, the experimental group received additional guidance on how to use ChatGPT to generate out-of-class writing feedback and engaged in self-directed AI-supported writing revision outside the classroom. The final writing assessment was conducted in class under comparable conditions for both groups. Writing performance was evaluated using an analytic rubric, and learners' anxiety, enjoyment and boredom were measured by established self-report scales. Open-ended responses were analysed inductively.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results and Conclusions</h3>\u0000 \u0000 <p>The experimental group achieved significantly higher writing scores and reported significantly lower anxiety, boredom and enjoyment than the control group. Qualitative data showed that learners found AI-generated feedback helpful for organising ideas and improving language use; however, they also expressed concerns about communication issues and over-dependence on AI tools. These findings contribute to research on AI-mediated informal digital learning of English and suggest that AI-generated feedback may be associated with both L2 writing development and learners' emotional experiences.</p>\u0000 </section>\u0000 </div>","PeriodicalId":48071,"journal":{"name":"Journal of Computer Assisted Learning","volume":"42 5","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148616593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and Validation of the AI Learning, Metacognitive, and Epistemic Barriers Scale (AIME-B) for Secondary School Principals in Jordan","authors":"Yusra Jadallah abed Khasawneh,&nbsp;Najwa Ahmed Salim Khasawneh,&nbsp;Mohamad Ahmad Saleem Khasawneh,&nbsp;Nabil Saleh Sufyan,&nbsp;Asma Jadallah Khasawneh","doi":"10.1002/jcal.70301","DOIUrl":"https://doi.org/10.1002/jcal.70301","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Rapid adoption of artificial intelligence (AI) in education is reshaping teaching, learning, and administration. Beyond technical skills, AI integration requires cognitive, metacognitive/self-regulatory, and epistemic preparedness; thus, latent barriers may constrain how secondary school principals interpret, evaluate, and use AI-generated information in decision-making, creating a need for a psychometrically sound instrument.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Objectives</h3>\u0000 \u0000 <p>This study aimed (1) to develop and (2) to validate the AI Learning, Metacognitive, and Epistemic Barriers Scale (AIME-B) for secondary school principals in Jordan, to assess cognitive, metacognitive/self-regulatory, and epistemic constraints that hinder reflective, context-appropriate engagement with AI outputs in school administration and decision-making.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Using an exploratory mixed-methods design, data were collected from 712 secondary school principals in Jordan (2025). The initial psychometric form contained 40 items; following item purification, the final scale retained 38 items loading on six factors.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results and Conclusions</h3>\u0000 \u0000 <p>Internal consistency was high (α = 0.891–0.958; ω = 0.880–0.958), and within-model convergent evidence was acceptable (AVE &gt; 0.50; CR &gt; 0.84). The first-order CFA supported six correlated dimensions. Revision-stage comparisons using the item-level confirmatory dataset favoured the correlated six-factor model over one-factor, four-factor, two theoretically plausible five-factor, second-order, and bifactor alternatives. The bifactor solution did not support a strong general factor, reinforcing primary interpretation of the six subscales. Gender invariance and bootstrapped EGA provided supplementary evidence regarding score comparability and clustering stability. Overall, the AIME-B provides initial, context-specific evidence supporting the internal structure and internal consistency of scores among Jordanian secondary school principals; temporal stability, external validity, and cross-context generalizability require further study.</p>\u0000 </section>\u0000 </div>","PeriodicalId":48071,"journal":{"name":"Journal of Computer Assisted Learning","volume":"42 5","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148616602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Can Generative AI Empower Tibetan Students in English Writing? Exploring Performance Gains, Self-Efficacy Enhancement and Cultural Expression","authors":"Ming Cheng","doi":"10.1002/jcal.70298","DOIUrl":"https://doi.org/10.1002/jcal.70298","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Tibetan university students often write in English within a complex multilingual environment involving Tibetan, Chinese and English. Previous studies have suggested that multilingual learners may experience challenges related to cross-linguistic transfer, as well as difficulties in expressing culturally specific concepts in English writing. At the same time, limited research has examined how contextually relevant AI-supported feedback is experienced in minority-language educational contexts.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>This study explored associations between participation in a contextually relevant GenAI-supported writing intervention (implemented through the Doubao app) and Tibetan college students' English writing performance, self-efficacy (SE) and experiences of cultural expression in English writing.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A 16-week quasi-experiment was conducted with 60 Tibetan students paired into an experimental group (EG, <i>n</i> = 30, using AI with cultural support) and a control group (CG, <i>n</i> = 30, traditional teaching). Data were collected through writing tests, an SE scale and semi-structured interviews.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The EG demonstrated higher scores than the CG on several writing measures, particularly Vocabulary and Language Use, and these differences remained evident in the delayed post-test. Positive patterns were also observed across writing SE measures. Interview data suggested that some participants perceived cultural terminology support and contrastive linguistic feedback as helpful during writing and revision activities.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>The findings suggest that participation in a contextually relevant AI-supported writing intervention was associated with positive writing outcomes among Tibetan trilingual learners. Participants perceived feedback addressing Tibetan–English structural differences and culturally specific terminology as helpful during writing tasks. Further research is needed to better understand the specific contribution of these context-sensitive features.</p>\u0000 </section>\u0000 </div>","PeriodicalId":48071,"journal":{"name":"Journal of Computer Assisted Learning","volume":"42 5","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148616594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From Competency to Agency: Teachers' Views of the Purposes of AI Education","authors":"Janne Fagerlund,&nbsp;Pekka Mertala,&nbsp;Jukka Lehtoranta,&nbsp;Emilia Mattila,&nbsp;Laura Salo,&nbsp;Tiina Korhonen","doi":"10.1002/jcal.70302","DOIUrl":"https://doi.org/10.1002/jcal.70302","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>As artificial intelligence (AI) becomes an increasingly prominent societal and educational phenomenon, teachers are required to determine not only how but also <i>why</i> students are educated about it in the classroom. Understanding teachers' thinking about the purposes of AI education is crucial, as they guide day-to-day choices about classroom uses of AI.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Objectives</h3>\u0000 \u0000 <p>This qualitative study investigates the purposes that teachers associate with AI education in primary and early childhood contexts and how AI has been taken up in teaching and learning.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Drawing on Gert Biesta's three-dimensional framework of well-rounded educational purpose, which emphasises students' holistic growth through the domains of qualification, socialisation, and subjectification, this study presents an abductive analysis of interview data from 13 Finnish teachers involved in national AI education development projects.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results and Conclusions</h3>\u0000 \u0000 <p>The teachers' views reflected an exploratory phase of AI education, characterised by an emphasis on skill development (qualification); a focus that risks overlooking students' critical and personal engagement with these prominent technologies. It also triggers diverse interpretations and teaching practices, ranging from an instrumental approach, where education is guided by goals such as future employability, to an orientation that foregrounds emancipation and self-expression in the present. While the teachers acknowledged the importance of fostering AI agency (subjectification), their approaches lacked robust strategies to support it. This study foregrounds such gaps and conceptualises a potential synergy between the domains where students' self-determined, informed engagement with AI is placed at the centre of educational efforts in AI education.</p>\u0000 </section>\u0000 </div>","PeriodicalId":48071,"journal":{"name":"Journal of Computer Assisted Learning","volume":"42 5","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jcal.70302","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148616595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Not All Received Peer Feedback Is Equal: The Self-Regulation Bridge Between Online Peer Feedback and Learning Outcomes","authors":"Erqi Zhang,&nbsp;Zhaoli Zhang,&nbsp;Xiaojuan Li,&nbsp;Jiahao Li,&nbsp;Hai Liu","doi":"10.1002/jcal.70288","DOIUrl":"https://doi.org/10.1002/jcal.70288","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Peer feedback is widely used to enhance learning outcomes in online or blended learning. Previous research has suggested that online peer feedback could promote self-regulation, while the precise role of self-regulated learning in the relationship between specific types of received peer feedback and learning outcomes remains unclear. This knowledge gap limits the effective design of online peer feedback interventions.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Objectives</h3>\u0000 \u0000 <p>This study aimed to examine the effects of three types of peer feedback (affective feedback, cognitive feedback, and metacognitive feedback) on feedback receivers' learning outcomes and to explore the mediating role of self-regulated learning in these relationships.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A total of 248 undergraduate students participated in the online peer feedback activity for a blended English writing course. At the end of the course, students completed a questionnaire measuring their perceptions of the received peer feedback and their self-regulated learning. Structural equation modelling was employed to investigate the direct and indirect effects among the variables.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results and Conclusions</h3>\u0000 \u0000 <p>Results showed that only metacognitive feedback had a significant direct effect on feedback receivers' learning outcomes, but affective feedback and cognitive feedback did not. Importantly, self-regulated learning significantly mediated the effects of all three types of peer feedback on feedback receivers' learning outcomes. These findings highlight that not all received peer feedback is equal, and fostering self-regulated learning is crucial for translating online peer feedback into improved learning outcomes.</p>\u0000 </section>\u0000 </div>","PeriodicalId":48071,"journal":{"name":"Journal of Computer Assisted Learning","volume":"42 4","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148386344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Not All Received Peer Feedback Is Equal: The Self-Regulation Bridge Between Online Peer Feedback and Learning Outcomes","authors":"Erqi Zhang,&nbsp;Zhaoli Zhang,&nbsp;Xiaojuan Li,&nbsp;Jiahao Li,&nbsp;Hai Liu","doi":"10.1002/jcal.70288","DOIUrl":"https://doi.org/10.1002/jcal.70288","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Peer feedback is widely used to enhance learning outcomes in online or blended learning. Previous research has suggested that online peer feedback could promote self-regulation, while the precise role of self-regulated learning in the relationship between specific types of received peer feedback and learning outcomes remains unclear. This knowledge gap limits the effective design of online peer feedback interventions.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Objectives</h3>\u0000 \u0000 <p>This study aimed to examine the effects of three types of peer feedback (affective feedback, cognitive feedback, and metacognitive feedback) on feedback receivers' learning outcomes and to explore the mediating role of self-regulated learning in these relationships.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A total of 248 undergraduate students participated in the online peer feedback activity for a blended English writing course. At the end of the course, students completed a questionnaire measuring their perceptions of the received peer feedback and their self-regulated learning. Structural equation modelling was employed to investigate the direct and indirect effects among the variables.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results and Conclusions</h3>\u0000 \u0000 <p>Results showed that only metacognitive feedback had a significant direct effect on feedback receivers' learning outcomes, but affective feedback and cognitive feedback did not. Importantly, self-regulated learning significantly mediated the effects of all three types of peer feedback on feedback receivers' learning outcomes. These findings highlight that not all received peer feedback is equal, and fostering self-regulated learning is crucial for translating online peer feedback into improved learning outcomes.</p>\u0000 </section>\u0000 </div>","PeriodicalId":48071,"journal":{"name":"Journal of Computer Assisted Learning","volume":"42 4","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148386343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}