British Journal of Educational Technology最新文献

{"title":"The dark side of affinity spaces for teacher professional learning","authors":"Daniel G. Krutka, Spencer P. Greenhalgh","doi":"10.1111/bjet.13593","DOIUrl":"https://doi.org/10.1111/bjet.13593","url":null,"abstract":"<div>\u0000 \u0000 <section>\u0000 \u0000 <p>The affinity space framework has proven useful for explaining and understanding teacher activity on social media platforms. In this study, we explore the ‘dark side’ of teacher affinity spaces by documenting a partisan teachers' group on an alternative social media platform. We used a mix of a priori and emergent coding to analyse screenshots of posts and comments from a public teachers' group and group administrators' activity on the broader platform. Findings indicate that although the group administrators began with a focus on teachers, most participants were non-teachers with political (rather than professional) concerns about US education. Furthermore, administrators both freely engaged with political talking points in their activity outside the teachers' group and allowed the broader platform culture—including conspiratorial thinking, explicit racism and out-group villainization—to seep in. We conclude by describing how these findings correspond with the key characteristics of an affinity space, including an overlapping of affinities, a lack of concern for professional qualifications, and influence from the broader platform. These findings provide an illustrative example of how teacher affinity spaces can drift from their stated intention within the larger platform context.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <div>\u0000 \u0000 <div>\u0000 \u0000 <h3>Practitioner notes</h3>\u0000 <p>What is already known about this topic\u0000\u0000 </p><ul>\u0000 \u0000 <li>Social media spaces have been conceptualized as affinity spaces for educators.</li>\u0000 \u0000 <li>Most studies provide optimistic accounts of teacher professional learning on social media.</li>\u0000 \u0000 <li>Most research has analysed teachers' use of mainstream platforms.</li>\u0000 </ul>\u0000 <p>What this paper adds\u0000\u0000 </p><ul>\u0000 \u0000 <li>We offer a detailed analysis of a partisan US teacher group on an alternative platform.</li>\u0000 \u0000 <li>Initial efforts to focus on teaching devolved into in-group identification and out-group villainization.</li>\u0000 \u0000 <li>This study highlights how the characteristics of affinity spaces can be detrimental to teacher professional learning.</li>\u0000 </ul>\u0000 <p>Implications for practice and/or policy\u0000\u0000 </p><ul>\u0000 \u0000 <li>Administrators of online groups should recognize their important role in ensuring group purposes are enacted.</li>\u0000 \u0000 <li>Educators should assess whether the larger platform is conducive to cultivatin","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"56 6","pages":"2573-2594"},"PeriodicalIF":8.1,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145248710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Teachers' digital initiatives to bridge students' in-class and out-of-class language learning and the influencing factors","authors":"Chun Lai,&nbsp;Zhan Shi","doi":"10.1111/bjet.13595","DOIUrl":"https://doi.org/10.1111/bjet.13595","url":null,"abstract":"<p>Given the significant and unique contributions of both in-class and out-of-class learning, pedagogical initiatives that connect learners' experiences across these two learning spheres would bolster language development. Technology can catalyse the integration. Whether and how teachers utilize this potential of technology to engage in digital bridging initiatives, initiatives that support the connection of students' in-class and out-of-class learning experiences with digital resources, deserve attention. Thematic analysis of 13 interview responses and exploratory and confirmatory factor analyses of 1101 survey responses of primary and secondary school Chinese EFL teachers unravelled two dimensions of digital bridging initiatives: inward bridging practices and outward bridging practices. SEM analysis of the survey responses further revealed various determinants of teachers' bridging initiatives, underscoring the strong and direct influence of teachers' awareness of students' out-of-class digital behaviours and the mediated influence of school culture. The study further highlighted the prominence of teacher internal factors, such as educator-oriented identity belief, awareness of students' out-of-class digital behaviours, and TPACK, for outward bridging. The study advocates attention to the nuances of teachers' digital bridging initiatives and influencing factors across contexts.</p>","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"56 6","pages":"2595-2622"},"PeriodicalIF":8.1,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://bera-journals.onlinelibrary.wiley.com/doi/epdf/10.1111/bjet.13595","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145248709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generative AI-supported progressive prompting for professional training: Effects on learning achievement, critical thinking, and cognitive load","authors":"Chia-Jung Li,&nbsp;Gwo-Jen Hwang,&nbsp;Ching-Yi Chang,&nbsp;Hui-Chi Su","doi":"10.1111/bjet.13594","DOIUrl":"https://doi.org/10.1111/bjet.13594","url":null,"abstract":"<p>In professional training, developing critical thinking is essential for professionals to analyse problem situations and respond effectively to emergencies. Conventional professional training typically employs multimedia materials combined with progressive prompting (PP) to support trainees in constructing knowledge and solving problems on their own. However, for those trainees who have insufficient knowledge or experience, it could be challenging for them to understand and utilise the prompts for finding solutions to the problems to be dealt with. To provide a personalised advisor during the progressive prompting-based training process, this study proposed a generative artificial intelligence (GenAI)-supported PP (GenAI-PP) learning approach by employing GenAI to facilitate discussions with individual trainees regarding the prompts, thereby encouraging deeper thinking and critical analysis at each stage. This study adopted a quasi-experimental design to compare the effects of GenAI-PP and the conventional PP (C-PP) approach on students' learning outcomes. The participants were 62 newly qualified nurses with less than one year of clinical experience in Taiwan, who needed to learn to interpret electrocardiograms (ECGs) as part of their professional training. Results showed that the GenAI-PP group significantly outperformed the C-PP group on test scores (<i>p</i> &lt; 0.01) and critical thinking (<i>p</i> &lt; 0.01). Moreover, the GenAI-PP group experienced significantly lower extraneous cognitive load compared to the C-PP group (<i>p</i> &lt; 0.001). These findings suggest the potential of GenAI-PP in professional training; that is, GenAI could serve as a learning partner to discuss with trainees the prompts provided by the instructor to help them master core concepts and develop key career competencies, especially for training programs that require in-depth analysis and decision-making.</p>","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"56 6","pages":"2550-2572"},"PeriodicalIF":8.1,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145248509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cognitive Echo: Enhancing think-aloud protocols with LLM-based simulated students","authors":"Longwei Zheng,&nbsp;Anna He,&nbsp;Changyong Qi,&nbsp;Haomin Zhang,&nbsp;Xiaoqing Gu","doi":"10.1111/bjet.13590","DOIUrl":"10.1111/bjet.13590","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;p&gt;In the field of education, the think-aloud protocol is commonly used to encourage learners to articulate their thoughts during the learning process, providing observers with valuable insights into learners' cognitive processes beyond the final learning outcomes. However, the implementation of think-aloud protocols faces challenges such as task interference and limitations in completeness and authenticity of verbal reports. This study proposes a method called Cognitive Echo, which leverages large language models (LLMs) trained with simulated student experiences to enhance the completeness and authenticity of think-aloud verbalizations. LLMs have been demonstrated to simulate human-like behaviour more effectively by memorizing experiences. In this work, we introduce specific learner roles and train the LLMs to act as distinct learners. Our method involves integrating transaction data from learners' interactions with a tutoring system and the tutor's content to create interactive experiences between learners and teachers, thereby training the model to become simulated students with learning experiences. To investigate the effectiveness of this approach, we designed a test playground based on the retrospective think-aloud protocol and examined how LLM-trained simulated students improve cognitive process transparency and generalization of learning strategies. The study found that Cognitive Echo not only reveals what simulated students genuinely think about their learning experiences but also enables them to transfer their different cognitive strategies to new tasks. By training simulated students on real learning behaviour data to ensure their cognitive processes reflect authentic learner experiences, this approach will extend think-aloud protocols to more practice-oriented applications.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;div&gt;\u0000 \u0000 &lt;div&gt;\u0000 \u0000 &lt;h3&gt;Practitioner notes&lt;/h3&gt;\u0000 &lt;p&gt;What is already known about this topic\u0000\u0000 &lt;/p&gt;&lt;ul&gt;\u0000 \u0000 &lt;li&gt;Think-aloud protocols are widely used in educational settings to explore students' cognitive processes by asking them to verbalize their thoughts while solving problems, but they are prone to issues like task interference and incomplete data reporting.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;Existed applications of simulating student cognition in educational research are rigid and less adaptive to individual learner characteristics.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;Artificial intelligences, especially large language models, have shown promise in educational contexts, particularly for simulating human-like behaviours.&lt;/li&gt;\u0000 &lt;/ul&gt;\u0000 &lt;p&gt;What this paper adds\u0000\u0000 &lt;/p&gt;&lt;ul&gt;\u0000 \u0000 ","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"56 5","pages":"2019-2042"},"PeriodicalIF":8.1,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144811128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generative AI and multimodal data for educational feedback: Insights from embodied math learning","authors":"Giulia Cosentino,&nbsp;Jacqueline Anton,&nbsp;Kshitij Sharma,&nbsp;Mirko Gelsomini,&nbsp;Michail Giannakos,&nbsp;Dor Abrahamson","doi":"10.1111/bjet.13587","DOIUrl":"10.1111/bjet.13587","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;p&gt;This study explores the role of generative AI (GenAI) in providing formative feedback in children's digital learning experiences, specifically in the context of mathematics education. Using multimodal data, the research compares AI-generated feedback with feedback from human instructors, focusing on its impact on children's learning outcomes. Children engaged with a digital body-scale number line to learn addition and subtraction of positive and negative integers through embodied interaction. The study followed a between-group design, with one group receiving feedback from a human instructor and the other from GenAI. Eye-tracking data and system logs were used to evaluate student's information processing behaviour and cognitive load. The results revealed that while task-based performance did not differ significantly between conditions, the GenAI feedback condition demonstrated lower cognitive load and students show different visual information processing strategies among the two conditions. The findings provide empirical support for the potential of GenAI to complement traditional teaching by providing structured and adaptive feedback that supports efficient learning. The study underscores the importance of hybrid intelligence approaches that integrate human and AI feedback to enhance learning through synergistic feedback. This research offers valuable insights for educators, developers and researchers aiming to design hybrid AI-human educational environments that promote effective learning outcomes.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;div&gt;\u0000 \u0000 &lt;div&gt;\u0000 \u0000 &lt;h3&gt;Practitioner notes&lt;/h3&gt;\u0000 &lt;p&gt;What is already known about this topic?\u0000\u0000 &lt;/p&gt;&lt;ul&gt;\u0000 \u0000 &lt;li&gt;Embodied learning approaches have been shown to facilitate deeper cognitive processing by engaging students physically with learning materials, which is especially beneficial in abstract subjects like mathematics.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;GenAI has the potential to enhance educational experiences through personalized feedback, making it crucial for fostering student understanding and engagement.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;Previous research indicates that hybrid intelligence that combines AI with human instructors can contribute to improved educational outcomes.&lt;/li&gt;\u0000 &lt;/ul&gt;\u0000 &lt;p&gt;What this paper adds?\u0000\u0000 &lt;/p&gt;&lt;ul&gt;\u0000 \u0000 &lt;li&gt;This study empirically examines the effectiveness of GenAI-generated feedback when compared to human instructor feedback in the context of a multisensory environment (MSE) for math learning.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;Findings from system logs and eye-tracking analysis reveal that","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"56 5","pages":"1686-1709"},"PeriodicalIF":8.1,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144809296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 GenAI-enabled coding hints on students' programming performance and cognitive load in an SRL-based Python course","authors":"Anna Y. Q. Huang,&nbsp;Cheng-Yan Lin,&nbsp;Sheng-Yi Su,&nbsp;Stephen J. H. Yang","doi":"10.1111/bjet.13589","DOIUrl":"10.1111/bjet.13589","url":null,"abstract":"<p>Programming education often imposes a high cognitive burden on novice programmers, requiring them to master syntax, logic, and problem-solving while simultaneously managing debugging tasks. Prior knowledge is a critical factor influencing programming learning performance. A lack of foundational knowledge limits students' self-regulated learning (SRL) abilities, resulting in a performance gap between students with high and low levels of prior knowledge. To address this problem, this study developed CodeFlow Assistant (CFA), a specifically developed generative artificial intelligence (GenAI) tool that provides four levels of scaffolding guidance (flowcharts, cloze coding, basic coding solutions, and advanced coding solutions) to support novice programmers in mastering skills ranging from foundational understanding to advanced application. Through a controlled experiment comparing SRL-based, teaching assistant (TA)-assisted programming (SRLP-TA) and SRL-based, CFA-assisted programming (SRLP-CFA), this study evaluated the effect of CFA on coding performance, cognitive loads, and SRL abilities among novice programming students. The results indicated that compared with the SRLP-TA group, the SRLP-CFA group achieved statistically significantly higher coding scores but showed comparable improvements in understanding programming concepts. Moreover, CFA reduced intrinsic and extraneous cognitive loads while enhancing germane load, fostering deeper knowledge integration and engagement. These findings highlight the role of CFA in enhancing coding performance, particularly in translating conceptual understanding into practice. This tool also statistically significantly improved SRL abilities, such as intrinsic goal orientation, task value, and metacognitive self-regulation.</p>","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"56 5","pages":"1942-1972"},"PeriodicalIF":8.1,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144809297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of a generative AI-powered teachable agent for middle school mathematics learning: A design-based research study","authors":"Wanli Xing,&nbsp;Yukyeong Song,&nbsp;Chenglu Li,&nbsp;Zifeng Liu,&nbsp;Wangda Zhu,&nbsp;Hyunju Oh","doi":"10.1111/bjet.13586","DOIUrl":"10.1111/bjet.13586","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;p&gt;This paper reports on a design-based research (DBR) study that aims to devise an artificial intelligence (AI)-powered teachable agent that supports secondary school students' learning-by-teaching practices of mathematics learning content. A long-standing pedagogical practice of learning-by-teaching is powered by a recent advancement of generative AI technologies, yielding our teachable agent called &lt;i&gt;ALTER-Math&lt;/i&gt;. This study chronicles one usability testing and three cycles of iterative design and implementation process of &lt;i&gt;ALTER-Math&lt;/i&gt;. The three empirical studies involved a total of 320 middle school students and six teachers in authentic classroom settings. The first study was exploratory, focusing on the qualitative feedback from the students and teachers through open-ended surveys, interviews and classroom observations. The second study yielded a medium-high (&lt;i&gt;M&lt;/i&gt; = 3.26) quantitative survey result on students' perceived engagement and usability on top of the qualitative findings. Finally, the final study included pre- and post-knowledge tests in a quasi-experimental study design as well as student and teacher interviews. The final study revealed a bigger significant knowledge improvement in students who used &lt;i&gt;ALTER-Math&lt;/i&gt; compared to the control group, suggesting a positive impact of AI-powered teachable agents on students' learning. The design implications learned from multiple iterations are discussed to inform the future design of AI-powered learning technologies.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;div&gt;\u0000 \u0000 &lt;div&gt;\u0000 \u0000 &lt;h3&gt;Practitioner notes&lt;/h3&gt;\u0000 &lt;p&gt;What is already known about this topic\u0000\u0000 &lt;/p&gt;&lt;ul&gt;\u0000 \u0000 &lt;li&gt;Learning-by-teaching is a long-standing effective pedagogical strategy to enhance students' domain knowledge and feelings of responsibility in learning.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;Various teachable agents have been developed and have demonstrated benefits in students' learning.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;Generative AI offers the potential to provide naturalistic, contextualised and adaptive conversations.&lt;/li&gt;\u0000 &lt;/ul&gt;\u0000 &lt;p&gt;What this paper adds\u0000\u0000 &lt;/p&gt;&lt;ul&gt;\u0000 \u0000 &lt;li&gt;Develops a novel generative AI-powered teachable agent for middle school mathematics learning, called &lt;i&gt;ALTER-Math&lt;/i&gt;.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;Reports the iterative design process involving empirical classroom implementations of &lt;i&gt;ALTER-Math&lt;/i&gt;.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;Reveals a bigger significant improvement in the student's mathematical knowledge after using &lt;i&gt;ALTER-Math&lt;/i&gt;, compared to the control group.&lt;/li&gt;\u0000 ","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"56 5","pages":"2043-2077"},"PeriodicalIF":8.1,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144811402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"“ChatGPT can make mistakes. Check important info.” Epistemic beliefs and metacognitive accuracy in students' integration of ChatGPT content into academic writing","authors":"Marek Urban,&nbsp;Cyril Brom,&nbsp;Jiří Lukavský,&nbsp;Filip Děchtěrenko,&nbsp;Veronika Hein,&nbsp;Filip Svacha,&nbsp;Petra Kmoníčková,&nbsp;Kamila Urban","doi":"10.1111/bjet.13591","DOIUrl":"10.1111/bjet.13591","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;p&gt;Recent studies have conceptualized ChatGPT as an epistemic authority; however, no research has yet examined how epistemic beliefs and metacognitive accuracy affect students' actual use of ChatGPT-generated content, which often contains factual inaccuracies. Therefore, the present experimental study aimed to examine how university students integrate correct and incorrect information from expert-written and ChatGPT-generated articles when writing independently (&lt;i&gt;N&lt;/i&gt; = 49) or with ChatGPT assistance (&lt;i&gt;N&lt;/i&gt; = 49). Students working with ChatGPT-4o integrated more correct information from both expert-written (&lt;i&gt;d&lt;/i&gt; = 0.64) and ChatGPT-generated articles (&lt;i&gt;d&lt;/i&gt; = 0.95), but ChatGPT-assisted writing did not affect the amount of incorrect information sourced from the ChatGPT-generated article. Regardless of the condition, hierarchical regressions revealed that lower metacognitive bias was moderately associated with increased inclusion of correct information from the expert-written article (&lt;i&gt;R&lt;/i&gt;&lt;sup&gt;2&lt;/sup&gt; = 12%). Conversely, a higher metacognitive bias (&lt;i&gt;R&lt;/i&gt;&lt;sup&gt;2&lt;/sup&gt; = 10%) and epistemic beliefs (&lt;i&gt;R&lt;/i&gt;&lt;sup&gt;2&lt;/sup&gt; = 12%) were moderately related to the inclusion of incorrect information from ChatGPT-generated articles. These findings suggest that while ChatGPT assistance enhances the integration of correct human- and AI-generated content, metacognitive skills remain essential to mitigate the risks of incorporating incorrect AI-generated information.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;div&gt;\u0000 \u0000 &lt;div&gt;\u0000 \u0000 &lt;h3&gt;Practitioner notes&lt;/h3&gt;\u0000 &lt;p&gt;What is already known about this topic\u0000\u0000 &lt;/p&gt;&lt;ul&gt;\u0000 \u0000 &lt;li&gt;Generative AI tools, such as ChatGPT, are increasingly regarded as epistemic authorities due to their authoritative tone and human-like interaction.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;ChatGPT has demonstrated utility in providing correct information and improving productivity in educational and professional contexts, but it is also prone to inaccuracies, hallucinations and misleading content.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;Students' epistemic beliefs and metacognitive skills predict their ability to critically evaluate and integrate conflicting information from multiple resources, particularly when searching for information on the Internet.&lt;/li&gt;\u0000 &lt;/ul&gt;\u0000 &lt;p&gt;What this paper adds\u0000\u0000 &lt;/p&gt;&lt;ul&gt;\u0000 \u0000 &lt;li&gt;This study experimentally examines how students integrate correct and incorrect information from expert-written and ChatGPT-generated articles when writing independently or with ChatGPT's assistance.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;The findings show that ChatGPT as","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"56 5","pages":"1897-1918"},"PeriodicalIF":8.1,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144811143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysing nontraditional students' ChatGPT interaction, engagement, self-efficacy and performance: A mixed-methods approach","authors":"Mohan Yang,&nbsp;Shiyan Jiang,&nbsp;Belle Li,&nbsp;Kristin Herman,&nbsp;Tian Luo,&nbsp;Shanan Chappell Moots,&nbsp;Nolan Lovett","doi":"10.1111/bjet.13588","DOIUrl":"10.1111/bjet.13588","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;p&gt;Generative artificial intelligence brings opportunities and unique challenges to nontraditional higher education students, stemming, in part, from the experience of the digital divide. Providing access and practice is critical to bridge this divide and equip students with needed digital competencies. This mixed-methods study investigated how nontraditional higher education students interact with ChatGPT in multiple courses and examined relationships between ChatGPT interactions, engagement, self-efficacy and performance. Data were collected from 73 undergraduate and graduate students through chat logs, course reflections and artefacts, surveys and interviews. ChatGPT interactions were analysed using four metrics: prompt number, depth of knowledge (DoK), prompt relevance and originality. Results showed that ChatGPT prompt numbers (&lt;i&gt;β&lt;/i&gt; = 0.256, &lt;i&gt;p&lt;/i&gt; &lt; 0.03) and engagement (&lt;i&gt;β&lt;/i&gt; = 0.267, &lt;i&gt;p&lt;/i&gt; &lt; 0.05) significantly predicted performance, while self-efficacy did not. Students' DoK (&lt;i&gt;r&lt;/i&gt; = 0.40, &lt;i&gt;p&lt;/i&gt; &lt; 0.01) and prompt relevance (&lt;i&gt;r&lt;/i&gt; = 0.42, &lt;i&gt;p&lt;/i&gt; &lt; 0.01) were positively correlated with performance. Text mining analysis identified distinct interaction patterns, with ‘strategic inquirers’ demonstrating significantly higher performance than ‘exploratory inquirers’ through more sophisticated follow-up questioning. Qualitative findings revealed that while most students were first-time ChatGPT users who initially showed resistance, they developed growing acceptance. Still, students tended to use ChatGPT sparingly and, even then, as only a starting point for assignments. The study highlights the need for targeted guidance in prompt engineering and AI literacy training to help nontraditional higher education students leverage ChatGPT more effectively for higher-order thinking tasks.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;div&gt;\u0000 \u0000 &lt;div&gt;\u0000 \u0000 &lt;h3&gt;Practitioner notes&lt;/h3&gt;\u0000 &lt;p&gt;What is already known about this topic\u0000\u0000 &lt;/p&gt;&lt;ul&gt;\u0000 \u0000 &lt;li&gt;Nontraditional students face unique challenges in higher education, such as limited technological literacy and digital access.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;The emergence of generative AI tools presents both opportunities and challenges for addressing educational disparities.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;Existing studies on AI implementation predominantly focus on traditional students.&lt;/li&gt;\u0000 &lt;/ul&gt;\u0000 &lt;p&gt;What this paper adds\u0000\u0000 &lt;/p&gt;&lt;ul&gt;\u0000 \u0000 &lt;li&gt;Empirical evidence of how nontraditional students interact with ChatGPT through multiple metrics (prompt n","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"56 5","pages":"1973-2000"},"PeriodicalIF":8.1,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://bera-journals.onlinelibrary.wiley.com/doi/epdf/10.1111/bjet.13588","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144811142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 impact of generative AI on academic integrity of authentic assessments within a higher education context","authors":"Alexander K. Kofinas,&nbsp;Crystal Han-Huei Tsay,&nbsp;David Pike","doi":"10.1111/bjet.13585","DOIUrl":"https://doi.org/10.1111/bjet.13585","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;p&gt;Generative AI (hereinafter GenAI) technology, such as ChatGPT, is already influencing the higher education sector. In this work, we focused on the impact of GenAI on the academic integrity of assessments within higher education institutions, as GenAI can be used to circumvent assessment approaches within the sector, compromising their quality. The purpose of our research was threefold: first, to determine the extent to which the use of GenAI can be detected via the marking and moderation process; second, to understand whether the presence of GenAI affects the marking process; and finally, to establish whether authentic assessments can safeguard academic integrity. We used a series of experiments in the context of two UK-based universities to examine these issues. Our findings indicate that markers, in general, are not able to distinguish assessments that have had GenAI input from assessments that did not, even though the presence of GenAI affects the way markers approach the marking process. Our findings also suggest that the level of authenticity in an assessment has no impact on the ability to safeguard against or detect GenAI usage in assessment creation. In conclusion, we suggest that current approaches to assessments in higher education are susceptible to GenAI manipulation and that the higher education sector cannot rely on authentic assessments alone to control the impact of GenAI on academic integrity. Thus, we recommend giving more critical attention to assessment design and placing more emphasis on assessments that rely on social experiential learning and are performative rather than output-based and asynchronously written.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;div&gt;\u0000 \u0000 &lt;div&gt;\u0000 \u0000 &lt;h3&gt;Practitioner notes&lt;/h3&gt;\u0000 &lt;p&gt;What is already known about this topic\u0000\u0000 &lt;/p&gt;&lt;ul&gt;\u0000 \u0000 &lt;li&gt;GenAI has enabled students to complete higher education assessments quickly and with good quality, leading to challenges in academic integrity.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;GenAI has transformed the requirements and considerations in assessment design in higher education.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;Authentic assessments are seen as a prominent way to tackle the GenAI challenge.&lt;/li&gt;\u0000 &lt;/ul&gt;\u0000 &lt;p&gt;What this paper adds\u0000\u0000 &lt;/p&gt;&lt;ul&gt;\u0000 \u0000 &lt;li&gt;We provide quantitative and qualitative experimental evidence suggesting that GenAI can generate authentic assessments that pass the scrutiny of experienced academics.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;We demonstrate how the use of authentic assessments alone does not pr","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"56 6","pages":"2522-2549"},"PeriodicalIF":8.1,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://bera-journals.onlinelibrary.wiley.com/doi/epdf/10.1111/bjet.13585","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145248565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}