Journal of Computer Assisted Learning最新文献

{"title":"The Nexus of Gen-AI Features in Higher Education: Reducing Cognitive Load, Building Resilience, and Personalising Learning","authors":"Nadia Rehman,&nbsp;Xiao Huang,&nbsp;Yanjun Zhang,&nbsp;Amir Mahmood","doi":"10.1002/jcal.70312","DOIUrl":"https://doi.org/10.1002/jcal.70312","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Generative Artificial Intelligence (Gen-AI) is reshaping higher education by offering adaptive support, instant feedback, and greater learner autonomy. Despite the increasing use of tools such as ChatGPT in university learning, empirical evidence on how their motivational affordances influence academic resilience and personalised learning remains limited, especially among non-native learners.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Objectives</h3>\u0000 \u0000 <p>This study examines how five motivational features of Gen-AI-Adaptive Learning Support (ALS), Instantaneous Problem Resolution (IPR), Subject-Specific Guidance (SSG), Temporal Flexibility (TF), and Language Accessibility (LA) influence cognitive load, academic resilience, and personalised learning. Specifically, it addresses three research questions: (1) To what extent do generative AI affordances influence cognitive load reduction among higher education students? (2) To what extent do generative AI affordances influence academic resilience and personalised learning? (3) Does cognitive load reduction mediate the relationship between generative AI affordances and academic resilience and personalised learning?</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methodology</h3>\u0000 \u0000 <p>A cross-sectional survey was conducted with 813 non-native university students from seven higher education institutions in China. Structural equation modelling was used to test the hypothesised relationships among Gen-AI affordances, cognitive load, academic resilience, and personalised learning.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>All five motivational features positively influenced cognitive load reduction, personalised learning, and academic resilience. Cognitive load reduction significantly mediated these relationships, suggesting that cognitive load reduction represents an important indirect pathway linking Gen-AI affordances with learning efficiency, academic resilience, and personalised learning.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Gen-AI can support personalised and resilient learning by reducing cognitive burden. This study clarifies the mediating role of cognitive processes and offers practical implications for educators and EdTech developers designing adaptive and inclusive learning environments.</p>\u0000 </section>\u0000 </div>","PeriodicalId":48071,"journal":{"name":"Journal of Computer Assisted Learning","volume":"42 5","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148849142","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":"Deceptive Overgeneralization: When Adaptive Learning Enables Systematic Misapplication","authors":"Marshall An,&nbsp;Bruce M. McLaren,&nbsp;John Stamper","doi":"10.1002/jcal.70311","DOIUrl":"https://doi.org/10.1002/jcal.70311","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Adaptive learning systems infer mastery from observed correctness, but this approach may fall short when learners exhibit ‘deceptive overgeneralization’—performing correct actions based on incomplete understanding. This phenomenon can mislead systems into prematurely stopping practice, leaving knowledge gaps unaddressed. Clarifying when deceptive overgeneralization occurs and how to address it can improve the precision of adaptive learning technologies.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Objectives</h3>\u0000 \u0000 <p>This research empirically examines whether deceptive overgeneralization occurs in adaptive learning, assesses its consequences for mastery-based stopping rules, and evaluates a systematic procedure for detecting and remedying it.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We conducted 11 experiments using Intelligent Tutoring Systems (ITSs) for Riichi Mahjong across multiple semesters with university students (total <i>N</i> = 192). The studies employed a pretest–posttest design to assess ITS effectiveness, and within-subjects designs with adaptive and full-exposure conditions to examine overgeneralization and remediation. Tailored remediation consisted of targeted ‘do-not-act’ problems with feedback that names the missing application constraints.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results and Conclusions</h3>\u0000 \u0000 <p>ITSs improved learning outcomes, but learners frequently misapplied acquired actions in contexts where they should have refrained, indicating deceptive overgeneralization. Under adaptive learning, mastery stopping rules often ended practice before learners encountered such ‘do-not-act’ contexts, leaving the issue undetected. Tailored remediation using targeted ‘do-not-act’ problems with constraint-naming feedback reduced misapplication to near-floor levels across skills and cohorts, highlighting a limitation of correctness-based mastery inference and a practical method to strengthen mastery assessment for knowledge compilation. A secondary analysis of K–12 decimal learning data further illustrates how the framework applies in a traditional educational domain.</p>\u0000 </section>\u0000 </div>","PeriodicalId":48071,"journal":{"name":"Journal of Computer Assisted Learning","volume":"42 5","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jcal.70311","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148848639","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":"Collaborative Learning: A Transactional Approach to Examining Collaborative Processes and Outcomes","authors":"Hanni Muukkonen,&nbsp;Jun Oshima,&nbsp;Crina Damşa","doi":"10.1002/jcal.70306","DOIUrl":"https://doi.org/10.1002/jcal.70306","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Research on collaborative learning has long examined processes and outcomes separately, with distinct fields—Computer-Supported Collaborative Learning (CSCL), learning sciences and recently learning analytics—drawing on differing epistemological premises. Integrated analytical accounts connecting collaboration processes to the outcomes of collaboration remain scarce. This special issue addresses that gap by studies of collaborative learning that employ conceptualisations and analytical methods aligned with a transactional approach.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Contributions in This Special Issue</h3>\u0000 \u0000 <p>The issue comprises an editorial, seven empirical studies and two commentaries. Collectively, the studies show that scaffolding collaboration can target reflection, regulation, motivation, knowledge-object development and learner–technology relations, each mediating transactions differently. Using multimodal and multilayered analytical approaches—including epistemic network analysis, multi-layered temporal network analysis, sequence mining and multilevel modelling—the contributions examine how tools, artefacts, discourse and motivational states jointly shape learning outcomes across authentic educational settings, from higher education to school and after-school contexts. The commentaries situate these findings within broader theoretical and methodological debates in CSCL and research that emphasises understanding the relational and situated nature of collaborative learning.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Implications for Future Research</h3>\u0000 \u0000 <p>The authenticity of the studied settings carries implications for data collection, analysis and design for collaborative learning, offering insight for researchers, teachers, learning designers and technology developers. Future research should give attention to the relational, temporal, multimodal aspects combined—that is, employ an integrative approach in the interpretation and revisit conceptualisations that integrate material-ecological, epistemic-discursive and motivational-psychological perspectives on the relationship between collaboration processes and outcomes.</p>\u0000 </section>\u0000 </div>","PeriodicalId":48071,"journal":{"name":"Journal of Computer Assisted Learning","volume":"42 5","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148848738","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":"Emergent Learner Agency in Implicit Human–AI Collaboration: How Supportive and Contrarian AI Personas Reshape Interaction","authors":"Yueqiao Jin,&nbsp;Roberto Martinez-Maldonado,&nbsp;Dragan Gašević,&nbsp;Xibin Han,&nbsp;Lixiang Yan","doi":"10.1002/jcal.70310","DOIUrl":"https://doi.org/10.1002/jcal.70310","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>As agentic artificial intelligence (AI) systems move beyond tool-like support toward more autonomous, team-like roles, a central question concerns the extent to which such systems can meaningfully participate in collaborative learning. Emerging work suggests that agentic AI may adopt distinct interactional personas, such as supportive or contrarian roles, yet little is known about how these personas shape learner agency and group dynamics when AI operates as an undisclosed teammate.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Objectives</h3>\u0000 \u0000 <p>This study investigates how supportive and contrarian AI personas influence emergent learner agency, discourse patterns, and experiential outcomes in implicit human–AI creative collaboration.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A total of 224 university students were randomly assigned to 97 online triads in human-only, supportive-AI, or contrarian-AI conditions. Teams completed an individual–group–individual creative movie-plot task via a 10-min text chat. Discourse was coded using a creative–regulatory framework and analysed via transition network analysis, sequential pattern mining, and Gaussian mixture clustering to characterise emergent agency patterns. These patterns were linked to cognitive load, psychological safety, teamwork satisfaction, and creative performance.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results and Conclusions</h3>\u0000 \u0000 <p>The findings revealed that contrarian AI produced challenge- and reflection-rich discourse indicative of productive friction, whereas supportive AI promoted agreement-centred trajectories. However, while contrarian personas stimulated critical engagement, they reduced teamwork satisfaction and psychological safety without yielding corresponding gains in creative performance. Educational designs need to balance epistemic challenge with the preservation of the affective climate to ensure that hybrid collaboration supports rather than undermines the learner experience.</p>\u0000 </section>\u0000 </div>","PeriodicalId":48071,"journal":{"name":"Journal of Computer Assisted Learning","volume":"42 5","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jcal.70310","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148784798","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":"Students and Instructors' Perceptions of Integrating Generative AI in Assessment Within the Context of the UAE","authors":"Fatima Salem Al Mohsen,&nbsp;Areej ElSayary","doi":"10.1002/jcal.70304","DOIUrl":"https://doi.org/10.1002/jcal.70304","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Generative artificial intelligence (GenAI) is transforming assessment practices in higher education, offering opportunities for efficiency and personalization while raising concerns related to academic integrity, fairness, and responsible use. Understanding how different users perceive and engage with GenAI is essential, particularly within rapidly advancing digital contexts such as the United Arab Emirates (UAE).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Objectives</h3>\u0000 \u0000 <p>This study investigates students' and instructors' perceptions of GenAI integration in assessment and examines the relationships among perceived usefulness, perceived ease of use, perceived risk, attitudes, and responsible use, including the moderating role of user type.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>An explanatory sequential mixed-methods design was employed. Quantitative data were collected from 193 participants (163 students and 30 instructors) using a validated survey instrument. Correlation, regression, mediation, and moderation analyses were conducted, complemented by qualitative thematic analysis of open-ended responses.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Findings revealed significant positive relationships among all constructs. Perceived usefulness and ease of use were combined into a higher-order Technology Acceptance Model construct that strongly predicted attitudes. Perceived risk significantly influenced both attitudes and responsible use, emerging as the strongest predictor of responsible behaviour. Attitude partially mediated the relationship between technology acceptance and responsible use. User role significantly moderated this relationship, with a stronger effect observed among instructors. Qualitative findings indicated generally positive but cautious attitudes, highlighting concerns about over-reliance, accuracy, and academic integrity, alongside a continued intention to use GenAI.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>GenAI adoption in assessment is shaped by a balance between perceived benefits and risk awareness. The findings highlight the importance of promoting responsible use through clear policies, training, and assessment design, offering practical implications for integrating GenAI within higher education systems.</p>\u0000 </section>\u0000 </div>","PeriodicalId":48071,"journal":{"name":"Journal of Computer Assisted Learning","volume":"42 5","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148752724","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":"Teacher Mediation and the Contingent Promise of Generative AI in Mathematics Education","authors":"Allan Mesa Canonigo","doi":"10.1002/jcal.70307","DOIUrl":"https://doi.org/10.1002/jcal.70307","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Background&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;The integration of generative AI (GenAI) into education, specifically into teaching and learning, offers potential for personalised learning, but at the same time, it poses a challenge to whether it fosters student agency. Moreover, it also threatens to exacerbate entrenched inequalities, which have the potential to create new barriers for most of the learners. A gap exists in understanding how GenAI can be rethought as a dynamic scaffold that promotes cognitive struggle for conceptual understanding while actively advancing equitable participation.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Objective&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;The study aims to critically analyse how secondary school mathematics teachers implement GenAI for adaptive scaffolding. Moreover, this study aims to investigate the pedagogical and ethical tensions, particularly concerning equity and power dynamics, that emerge from using GenAI during classroom interactions.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Methods&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;This study employed a qualitative research design. Data were collected over an eight-week intervention, which included in-depth teacher interviews (&lt;i&gt;n&lt;/i&gt; = 10), student focus groups (&lt;i&gt;n&lt;/i&gt; = 24), and classroom observations in two public secondary schools purposively selected for their contrasting socioeconomic and technology integration profiles.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Results&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Based on the results and findings, five distinct teacher-mediated GenAI scaffolding practices that support equitable knowledge construction were identified. Findings indicate that strategies such as prompting for problem formulation through AI-generated inquiry and facilitating critical comparison of AI-generated outputs were associated with deeper student engagement and reasoning within the conditions of this study, particularly when teachers exercised intentional critical mediation. Conversely, unmediated use of GenAI amplified significant challenges, such as a redistribution of epistemic authority, a tangible “algorithmic divide” in AI performance between well-resourced and under-resourced schools, and risks due to the absence of how data should be handled. The technology consistently amplified existing pedagogical conditions, which are both effective and inequitable.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Conclusion&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;The promise of GenAI is strictly contingent on a commitment to equity. This means that its outcomes are determined not by the GenAI itself but by pedagogical intentionality and systemic support. Unexamined adoption poses risks that worsen disparities for marginal","PeriodicalId":48071,"journal":{"name":"Journal of Computer Assisted Learning","volume":"42 5","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148752432","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":"The Effects of K-12 Artificial Intelligence Education in Enhancing AI Literacy: A Meta-Analysis","authors":"Wonjin Yu,&nbsp;Nari Kim,&nbsp;Ammi Chang,&nbsp;Wanju Huang","doi":"10.1002/jcal.70308","DOIUrl":"https://doi.org/10.1002/jcal.70308","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>The initiation of K-12 artificial intelligence education has been led by numerous governments and institutions. However, most existing meta-analyses have focused on the effectiveness of AI tools for learning support (AI in education; AIED), rather than AI education, and research on comprehensive evidence on how AI education improves learners' AI literacy remains limited.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Objectives</h3>\u0000 \u0000 <p>This meta-analysis aims to provide an additional understanding of the future direction of K-12 AI education by synthesising and analysing existing interventions in AI education classes to enhance learners' AI literacy and holistically discussing their effectiveness.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A total of 16 source articles were searched and analysed from 8 databases, considering our inclusion and exclusion criteria, yielding 57 effect sizes. These effect sizes were used to examine the effectiveness of various K-12 AI education classes in enhancing AI literacy.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results and Conclusion</h3>\u0000 \u0000 <p>The analysis showed a significant overall mean effect of AI education classes (<i>g</i> = 0.892, CI: [0.548, 1.236], <i>p</i> &lt; 0.001) on enhancing AI literacy. Moreover, moderator analyses by publication type, year of publication, and school level of the participants did not reveal any significant patterns, indicating that K-12 AI education classes exhibit consistently positive effects across different contexts. The findings underscore the importance of continued research on K-12 AI education and offer a strong rationale for its ongoing integration by policymakers across various governments. To support discussion on this topic, future studies can reference this research and investigate the topic with more published articles.</p>\u0000 </section>\u0000 </div>","PeriodicalId":48071,"journal":{"name":"Journal of Computer Assisted Learning","volume":"42 5","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148752708","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":"The Impact of ChatGPT on Higher Education: A Systematic Review of Global Opportunities, Perceptions, and Challenges","authors":"Olukayode Emmanuel Apata,&nbsp;Oi-Man Kwok,&nbsp;Segun Timothy Ajose","doi":"10.1002/jcal.70309","DOIUrl":"https://doi.org/10.1002/jcal.70309","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>The advent of artificial intelligence (AI) has fundamentally changed how education is delivered and how students perform. Since its launch, ChatGPT, a popular generative AI tool, has generated mixed reactions.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Objectives</h3>\u0000 \u0000 <p>This research systematically reviews the opportunities, perceptions, and challenges of using ChatGPT among faculty members and students at higher education levels. Our review identified key themes and patterns in the literature and provided a comprehensive understanding of the different opinions held by students and faculty.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We included peer-reviewed journal articles and conference papers published between January 1, 2022 and October 20, 2025, sourced from APA PsycInfo, CINAHL Ultimate, Education Source Ultimate, ERIC and Web of Science. From 1046 initial records, 58 articles met the inclusion criteria. We also conducted a targeted search on December 10, 2024, which identified 12 additional studies from Google Scholar (6) and Consensus App (6). We then performed a final search on October 20, 2025, using the Consensus App, resulting in 12 more recent studies, bringing the total to 82 articles. Thematic content analysis and descriptive statistics identified key themes and patterns across the literature.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results and Conclusion</h3>\u0000 \u0000 <p>Our review showed that faculty members and students hold differing opinions regarding ChatGPT's accuracy, reliability, and ethical implications, as well as its future role in higher education. Some view ChatGPT as a valuable tool for teaching and learning, while others remain sceptical. We conclude that appropriate measures should be taken to ensure that ChatGPT is used responsibly and equitably in higher education.</p>\u0000 </section>\u0000 </div>","PeriodicalId":48071,"journal":{"name":"Journal of Computer Assisted Learning","volume":"42 5","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jcal.70309","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148752703","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":"A Study on the Role of Midjourney, A Generative Artificial Intelligence Tool, in Enhancing Student Creativity in Illustration Design Classes","authors":"Shuaisen He","doi":"10.1002/jcal.70305","DOIUrl":"https://doi.org/10.1002/jcal.70305","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Generative artificial intelligence text-to-image tools are increasingly used in design education, but evidence is limited on whether structured classroom integration improves expert-rated creativity outcomes while controlling homogenisation. The objective was to test whether a Studio GenAI Creativity Enhancement Model (S GCEM) workflow using Midjourney improves expert-rated illustration creativity versus a conventional non-generative workflow and whether process metrics explain observed differences.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A parallel-group, two-section quasi-experiment was conducted in an undergraduate illustration design studio. Students in the intervention section used S GCEM with Midjourney for early-stage ideation only, mandatory prompt and version logs, and identical critique milestones to the control section (manual brainstorming, thumbnail sketching, non-generative references). The analytic sample was <i>n</i> = 162 (S GCEM <i>n</i> = 82; control <i>n</i> = 80). Baseline and final artefacts were scored on 1–5 rubrics for originality, appropriateness, and elaboration. Ideation breadth and iteration depth were derived from logs. Analyses used analysis of covariance and regression.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Final Product Creativity was higher under S GCEM than control after baseline adjustment, F(1,159) = 9.000, <i>p</i> = 0.003, partial η² = 0.053. Final Originality differed by condition, F(1,159) = 6.000, <i>p</i> = 0.015, partial η² = 0.036; Appropriateness, F(1,159) = 4.000, <i>p</i> = 0.047, partial η² = 0.024; and Elaboration, F(1,159) = 9.000, <i>p</i> = 0.003, partial η² = 0.054. Ideation breadth increased under S GCEM, B = 0.95, t = 4.13, <i>p</i> = 0.001, 95% CI 0.50–1.40, and iteration depth increased, B = 0.70, t = 3.50, <i>p</i> = 0.001, 95% CI 0.30–1.10. Homogenisation was lower under S GCEM, U = 2500, z = −3.05, <i>p</i> = 0.002, <i>r</i> = 0.24.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>A structured Midjourney-supported ideation workflow improved expert-rated illustration creativity while increasing documented ideation activity and reducing homogenisation indicators.</p>\u0000 </section>\u0000 </div>","PeriodicalId":48071,"journal":{"name":"Journal of Computer Assisted Learning","volume":"42 5","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148616750","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":"Effects of Digital Technology on Creativity and Emotional Skills in Music Education: A Quantitative Study","authors":"Ya Ni,&nbsp;Yingjin Shan","doi":"10.1002/jcal.70303","DOIUrl":"https://doi.org/10.1002/jcal.70303","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Digital technology has greatly transformed the traditional way of teaching music, particularly in the field of music creation. Students are less aware of using digital technology to gain creativity, skills and engagement. This study aims to evaluate the effects of digital technology on the creativity and emotions of students.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Method</h3>\u0000 \u0000 <p>A quantitative method was employed using an online questionnaire and responses were collected from 487 respondents. Data analysis through Statistical Package for the Social Sciences (SPSS) helps to test Pearson correlation, Confirmatory Factor Analysis, path coefficient, one-way ANOVA and two-way ANOVA.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The outcome reveals that there is a substantial positive correlation among the constructs in music education (<i>r</i> = 0.505 to 0.786, <i>p</i> &lt; 0.01). Younger participants have experienced a higher impact on learning than older groups (<i>F</i> = 89.619, <i>η</i>² = 0.360). Age and gender differences affect music education (<i>p</i> &lt; 0.05), except for playing instruments (<i>p</i> = 0.074). In this interaction, age shows a stronger and more reliable effect than gender, which is considered a main factor. Digital technology in music creates a substantial effect in learning style, personalised learning experiences, engagement, emotions and creativity (<i>β</i> = 0.228 to 0.786).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Digital technology positively influences music education in terms of better engagement with students, the development of their skills and makes learning personal and collaborative. Adaptive platforms, virtual instruments and other tools facilitate cognitive, emotional and creative development. The effect of its influence on music coaching, especially on the psychological development and reflective practice of students, should be studied in future.</p>\u0000 </section>\u0000 </div>","PeriodicalId":48071,"journal":{"name":"Journal of Computer Assisted Learning","volume":"42 5","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148616749","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}