British Journal of Educational Technology最新文献

{"title":"The role of critical thinking on undergraduates' reliance behaviours on generative AI in problem-solving","authors":"Chenyu Hou, Gaoxia Zhu, Vidya Sudarshan","doi":"10.1111/bjet.13613","DOIUrl":"10.1111/bjet.13613","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <p>There is a heightened concern over undergraduate students being over-reliant on Generative AI and using it recklessly. Reliance behaviours describe the frequencies and ways that people use AI tools for tasks such as problem-solving, influenced by individual factors such as trust and AI literacy. One way to conceptualise reliance is that reliance behaviours are affected by the extent to which learners consciously evaluate the relative performance of AI and humans, suggesting the potential impacts of critical thinking on reliance. This study, thus, empirically investigates the relationship between critical thinking and reliance behaviours. Critical thinking includes disposition and skills. However, limited empirical studies have investigated how critical thinking influences learners' reliance behaviours when solving problems with Generative AI. Hence, the current study conducted path analyses to investigate how critical thinking is associated with reliance behaviours and how it mediates the effect of individual factors on reliance behaviours. We collected 808 survey responses on critical thinking disposition and skills, reliance behaviours (a self-developed and validated scale, including reflective use, cautious use, thoughtless use, and collaborative use), trust towards AI, and AI literacy from undergraduates after a problem-solving task with Generative AI. The results indicate that (1) critical thinking is positively associated with the collaborative, reflective, and cautious use of Generative AI, suggesting that these three types of use of Generative AI could be considered desirable behaviours in human–AI problem-solving; (2) trust positively predicts thoughtless use; (3) critical thinking can offset the influence of trust on collaborative, reflective and cautious use; and (4) critical thinking can amplify the influence of AI literacy on reflective, cautious and collaborative use. This study contributes new insights into understanding the role of critical thinking in fostering desirable reliance behaviours, including reflective, cautious and collaborative use, and provides implications for future interventions when applying Generative AI for problem-solving.</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>Generative AI tools can potentially enhance problem-based learning (PBL) by supporting brainstorming and solution refinement.</li>\u0000 \u0000 <li>Reliance behaviours in human-AI collaboration are influenced by factors such as trust in AI and AI literacy.</li>\u0000 \u0000 <li","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"56 5","pages":"1919-1941"},"PeriodicalIF":8.1,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://bera-journals.onlinelibrary.wiley.com/doi/epdf/10.1111/bjet.13613","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144809288","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":"Beyond efficiency: Empirical insights on generative AI's impact on cognition, metacognition and epistemic agency in learning","authors":"Lixiang Yan,&nbsp;Viktoria Pammer-Schindler,&nbsp;Caitlin Mills,&nbsp;Andy Nguyen,&nbsp;Dragan Gašević","doi":"10.1111/bjet.70000","DOIUrl":"10.1111/bjet.70000","url":null,"abstract":"<p>Generative AI (GenAI) promises to reshape education, yet, beyond gains in efficiency and scalability, crucial questions remain regarding its deeper cognitive, epistemic, socio-emotional and ethical implications for learners. This special section collects emerging empirical evidence from diverse educational settings to examine how interactions with advanced GenAI systems—ranging from intelligent tutors and conversational agents to AI-generated feedback providers—shape learners' cognitive engagement, epistemic agency, metacognitive regulation and emotional relationships with technology. While studies reveal evidence of positive effects in personalised guidance, collaborative inquiry and enhanced self-reflection, findings also highlight risks such as diminished epistemic vigilance, superficial learning and emotional dependence on AI interlocutors. These insights advocate for an educational re-imagining that actively experiments with novel AI technologies while continuously gathering and critically evaluating evidence—envisioning education not merely as a spectrum ranging from fully human- or AI-led activities but also as genuinely collaborative endeavours that balance human intelligence and AI capabilities in meaningful and ethically grounded partnerships. Looking ahead, in this editorial, we propose an interdisciplinary research roadmap that urges deeper examination of the cognitive and neural consequences associated with sustained AI exposure, together with the development of comprehensive frameworks to cultivate AI literacy and evaluative judgement. It also encourages rigorous assessment of the long-term ethical implications of increasingly blurred human-AI boundaries. Ultimately, advancing education in GenAI-infused contexts requires proactive engagement with these emerging complexities, so that teachers and learners not only survive in an AI-infused future but also creatively, critically and ethically co-author it.</p>","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"56 5","pages":"1675-1685"},"PeriodicalIF":8.1,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://bera-journals.onlinelibrary.wiley.com/doi/epdf/10.1111/bjet.70000","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144809283","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":"Parent-led vs. AI-guided dialogic reading: Evidence from a randomized controlled trial in children's e-book context","authors":"Feiwen Xiao,&nbsp;Ellen Wenting Zou,&nbsp;Jiaju Lin,&nbsp;Zhaohui Li,&nbsp;Dandan Yang","doi":"10.1111/bjet.13615","DOIUrl":"10.1111/bjet.13615","url":null,"abstract":"<p>Large language model (LLM)-based conversational agents (CAs), with their advanced generative capabilities and human-like conversational interfaces, can serve as reading partners for children during dialogic reading and have shown promise in enhancing children's comprehension and conversational skills. However, there is limited research on the efficacy of LLM-based bilingual CAs in children's language acquisition in English as a Foreign Language (EFL) contexts. This randomized controlled trial study investigated the effectiveness of LLM-powered CAs compared with traditional parent–child shared reading in promoting engagement and improving learning outcomes among children with EFL. An interactive e-book featuring a LLM-powered CA was developed to engage children in dialogic reading through questioning and scaffolding. Sixty-seven children, aged 5 to 8, were randomly assigned to either an experimental (AI-led) group or a control (parent-led) group. The study found that children in the experimental group outperformed the control group in reading comprehension, with comparable benefits in vocabulary acquisition and story retelling, both immediately and in delayed tests. In the meantime, this study unpacks children's different engagement patterns when reading with the CA versus reading with their parents. Children reading with the CA demonstrated higher behavioural engagement and visual attention, while those in the parent-led group showed greater affective engagement and narrative-relevant vocalizations. The findings highlighted insights into the potential of LLM-powered CAs in children's language acquisition and suggested key design implications for developing better CAs for children from multilingual backgrounds.</p>","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"56 5","pages":"1784-1813"},"PeriodicalIF":8.1,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://bera-journals.onlinelibrary.wiley.com/doi/epdf/10.1111/bjet.13615","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144809275","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":"Breaking human dominance: Investigating learners' preferences for learning feedback from generative AI and human tutors","authors":"Huixiao Le,&nbsp;Yuan Shen,&nbsp;Zijian Li,&nbsp;Mengyu Xia,&nbsp;Luzhen Tang,&nbsp;Xinyu Li,&nbsp;Jiyou Jia,&nbsp;Qiong Wang,&nbsp;Dragan Gašević,&nbsp;Yizhou Fan","doi":"10.1111/bjet.13614","DOIUrl":"10.1111/bjet.13614","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;p&gt;Understanding learners' preferences in educational settings is crucial for optimizing learning outcomes and experience. As artificial intelligence (AI) becomes increasingly integrated into educational contexts, it is crucial to understand learners' preferences between AI and human tutors to support their learning. While AI demonstrates growing potential in education, the phenomenon of algorithm aversion, which is a tendency to favour human decision making over algorithmic solutions, requires further investigation. To explore this issue, an experiment involving 114 university students was conducted to measure learners' preferences for different feedback sources before and after exposure to one of four conditions: no feedback, human tutor feedback, ChatGPT feedback through a free-dialogue user interface, and AI-powered writing analytics tool feedback through a structured interface. Our results revealed a strong initial preference for human tutors. However, the post-task analysis showed an important nuance. While the general preference for human tutors persisted, learners' preference towards the free-dialogue interface (ChatGPT 4.0) of ChatGPT increased, whereas the structured AI interface (AI-powered writing analytics tool) reinforced the preference for human tutors. These findings offer theoretical and practical contributions by extending algorithm aversion theory to educational contexts and demonstrating that appropriate interaction design can mitigate this aversion. The success of free-dialogue interfaces suggests that overcoming algorithm aversion may depend more on creating natural, flexible interaction experiences than purely technical optimization. However, we must also consider that increased preference for AI tools, particularly those with more engaging interfaces, may potentially lead to over-reliance and metacognitive laziness among learners, highlighting the importance of balancing technological support with the development of independent learning skills.&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;Algorithm aversion exists across various contexts where individuals tend to prefer human over algorithmic decision-making.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;The introduction of generative AI brings new possibilities for AI-supported learning.&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;In academic writing tasks, learners show strong initial preference for human tutors over Generative AI feedback.&lt;/li","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"56 5","pages":"1758-1783"},"PeriodicalIF":8.1,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://bera-journals.onlinelibrary.wiley.com/doi/epdf/10.1111/bjet.13614","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144811138","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":"Investigating the motivational and knowledge affordances of conversational AI using induction, concretization and exemplification in math learning","authors":"Chenglu Li,&nbsp;Bailing Lyu","doi":"10.1111/bjet.13612","DOIUrl":"10.1111/bjet.13612","url":null,"abstract":"<p>A promising approach to support students' math learning effectively, automatically and at scale within existing learning environments is conversational artificial intelligence (ConvAI). Although previous studies have suggested ConvAI's potential to guide, facilitate and enhance learning, its effects on students' conceptual change and academic motivation—the latter a crucial moderator of conceptual change—in math education remain understudied. Our study expands understanding of how ConvAI can be used to support Algebra learning from a conceptual change perspective. Using a between-subjects, pre- and posttest design, we conducted an experimental study with 151 participants, with the experimental group accessing ConvAI developed with induction, concretization and exemplification teaching strategies. Results showed that participants in the ConvAI group exhibited higher mastery goal orientation and self-efficacy compared with the control group post-intervention. The frequency of visiting recommended learning resources by ConvAI significantly predicted participants' motivation changes, with increased visits correlating with higher motivation. Additionally, although there was no significant main effect on misconceptions between ConvAI and no-AI participants, significant interaction effects on misconceptions emerged between treatment conditions and student motivation. Our findings, revealed through open-sourced implementations, provide support and implications for educational practitioners and researchers to design and develop pedagogically meaningful ConvAI for math learning.</p>","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"56 5","pages":"1814-1841"},"PeriodicalIF":8.1,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://bera-journals.onlinelibrary.wiley.com/doi/epdf/10.1111/bjet.13612","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144811242","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":"Effects of AI-generated adaptive feedback on statistical skills and interest in statistics: A field experiment in higher education","authors":"Elisabeth Bauer,&nbsp;Constanze Richters,&nbsp;Amadeus J. Pickal,&nbsp;Moritz Klippert,&nbsp;Michael Sailer,&nbsp;Matthias Stadler","doi":"10.1111/bjet.13609","DOIUrl":"10.1111/bjet.13609","url":null,"abstract":"<p>This study explores whether AI-generated adaptive feedback or static feedback is favourable for student interest and performance outcomes in learning statistics in a digital learning environment. Previous studies have favoured adaptive feedback over static feedback for skill acquisition, however, without investigating the outcome of students' subject-specific interest. This study randomly assigned 90 educational sciences students to four conditions in a 2 × 2 Solomon four-group design, with one factor <i>feedback type</i> (adaptive vs. static) and, controlling for pretest sensitisation, another factor <i>pretest participation</i> (yes vs. no). Using a large language model, the adaptive feedback provided feedback messages tailored to students' responses for several tasks on reporting statistical results according to APA style, while static feedback offered a standardised expert solution. There was no evidence of pretest sensitisation and no significant effect of the feedback type on task performance. However, a significant medium-sized effect of feedback type on interest was found, with lower interest observed in the adaptive condition than in the static condition. In highly structured learning tasks, AI-generated adaptive feedback, compared with static feedback, may be non-essential for learners' performance enhancement and less favourable for learners' interest, potentially due to its impact on learners' perceived autonomy and competence.</p>","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"56 5","pages":"1735-1757"},"PeriodicalIF":8.1,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://bera-journals.onlinelibrary.wiley.com/doi/epdf/10.1111/bjet.13609","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144811019","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":"Group interaction patterns in generative AI-supported collaborative problem solving: Network analysis of the interactions among students and a GAI chatbot","authors":"Shihui Feng","doi":"10.1111/bjet.13611","DOIUrl":"10.1111/bjet.13611","url":null,"abstract":"<p>Collaborative problem solving (CPS) is an important skill enabling students to co-construct knowledge and tackle complex problems through group interactions. While the importance of group interactions in CPS is well recognized, it is unclear how the emergence of generative artificial intelligence (GAI), with advanced cognitive support, may alter group dynamics in CPS. This study bridges this gap by examining group interactions in GAI-supported CPS, focusing on the structural patterns and interaction content characterizing students' social dynamics. Six groups of three to five students used an online messaging tool with a GPT-4.0 enabled chatbot for a CPS activity. Group interactions were modelled using network analysis and interaction content was coded into socio-emotional, cognitive, metacognitive, and coordinative dimensions. Employing a network assortativity measure and a binomial test to the interactions among students and the GAI chatbot, we identified a GAI-centred interaction pattern in which students tended to interact significantly more with the chatbot than their peers in the collaborative problem-solving process. Students' interactions with the chatbot involved primarily cognitive interactions but also metacognitive and socio-emotional interactions. This study introduces novel network methods to analyse small group interactions and contributes new empirical evidence and theoretical insights into the social influence of GAI tools, emphasizing the need for further investigations on the factors influencing interaction dynamics among students and GAI tools in collaborative learning.</p>","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"56 5","pages":"2125-2145"},"PeriodicalIF":8.1,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://bera-journals.onlinelibrary.wiley.com/doi/epdf/10.1111/bjet.13611","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144809309","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":"Chatbots in tertiary education: Exploring the impact of warm and competent avatars on self-directed learning","authors":"Shahper Richter,&nbsp;Shohil Kishore,&nbsp;Inna Piven,&nbsp;Patrick Dodd,&nbsp;Guy Bate","doi":"10.1111/bjet.13610","DOIUrl":"10.1111/bjet.13610","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;p&gt;This study investigates how anthropomorphic AI chatbot avatars, designed in line with the Stereotype Content Model (SCM) dimensions of warmth and competence, influence university students' perceptions of support for self-directed learning (SDL) activities. We examined student responses to two distinct avatars—one projecting warmth and the other projecting competence. Using an Action Design Research (ADR) approach, we evaluated the chatbots across three university courses, incorporating perspectives from students, educators and learning designers. Findings reveal that students perceive the avatars differently. The warm avatar provides a stronger emotional connection, while the competent avatar offers more effective task-oriented learning support. These results highlight the importance of balancing warmth and competence in chatbot design to enhance their perceived usefulness for supporting SDL engagement. The study also supplies rich insights into practical implementation challenges and opportunities from multiple stakeholder viewpoints. Altogether, the research advances our understanding of SCM-informed chatbot design in educational settings and proposes practical principles for developing AI tools that students perceive as helpful, thereby contributing to the field of human–AI interaction.&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;The potential of AI chatbots to support aspects of self-directed learning (SDL) in higher education is currently being explored.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;User perceptions of AI systems are influenced by anthropomorphic design cues, often understood through dimensions like warmth and competence (related to the Stereotype Content Model—SCM).&lt;/li&gt;\u0000 \u0000 &lt;li&gt;Designing AI educational tools requires considering how different interactional styles (eg, warmth vs. competence) can affect student engagement and perceived usefulness.&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 insights into students' perceptions of chatbot avatars designed with varying levels of warmth and competence, based on the SCM, and how these perceptions relate to their reported engagement and perceived support for SDL in university courses.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;Evidence that students distinguish between warmth and competence in chatbot avatars, associating warmth with socio-emotional connection and competence with task-related lea","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"56 5","pages":"2102-2124"},"PeriodicalIF":8.1,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://bera-journals.onlinelibrary.wiley.com/doi/epdf/10.1111/bjet.13610","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144811105","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":"Human–AI collaborative learning in mixed reality: Examining the cognitive and socio-emotional interactions","authors":"Belle Dang,&nbsp;Luna Huynh,&nbsp;Faaiz Gul,&nbsp;Carolyn Rosé,&nbsp;Sanna Järvelä,&nbsp;Andy Nguyen","doi":"10.1111/bjet.13607","DOIUrl":"10.1111/bjet.13607","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;p&gt;The rise of generative artificial intelligence (GAI), especially with multimodal large language models like GPT-4o, sparked transformative potential and challenges for learning and teaching. With potential as a cognitive offloading tool, GAI can enable learners to focus on higher-order thinking and creativity. Yet, this also raises questions about integration into traditional education due to the limited research on learners' interactions with GAI. Some studies with GAI focus on text-based human–AI interactions, while research on embodied GAI in immersive environments like mixed reality (MR) remains unexplored. To address this, this study investigates interaction dynamics between learners and embodied GAI agents in MR, examining cognitive and socio-emotional interactions during collaborative learning. We investigated the paired interactive patterns between a student and an embodied GAI agent in MR, based on data from 26 higher education students with 1317 recorded activities. Data were analysed using a multi-layered learning analytics approach, including quantitative content analysis, sequence analysis via hierarchical clustering and pattern analysis through ordered network analysis (ONA). Our findings identified two interaction patterns: type (1) AI-led Supported Exploratory Questioning (AISQ) and type (2) Learner-Initiated Inquiry (LII) group. Despite their distinction in characteristic, both types demonstrated comparable levels of socio-emotional engagement and exhibited meaningful cognitive engagement, surpassing the superficial content reproduction that can be observed in interactions with GPT models. This study contributes to the human–AI collaboration and learning studies, extending understanding to learning in MR environments and highlighting implications for designing AI-based educational tools.&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;Socio-emotional interactions are fundamental to cognitive processes and play a critical role in collaborative learning.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;Generative artificial intelligence (GAI) holds transformative potential for education but raises questions about how learners interact with such technology.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;Most existing research focuses on text-based interactions with GAI; there is limited empirical evidence on how embodied GAI agents within immersive environments like Mixed Reality (MR) influence the cognitive and socio-emotional interactions for learning and regulation.&lt;/li&gt;\u0000 &lt;/ul&gt;\u0000 &lt;p&gt;Wha","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"56 5","pages":"2078-2101"},"PeriodicalIF":8.1,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://bera-journals.onlinelibrary.wiley.com/doi/epdf/10.1111/bjet.13607","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144811294","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":"AI-mediated sensemaking in higher education students’ learning processes: Tensions, sensemaking practices, and AI-assigned purposes","authors":"Anni Silvola,&nbsp;Anu Kajamaa,&nbsp;Joonas Merikko,&nbsp;Hanni Muukkonen","doi":"10.1111/bjet.13606","DOIUrl":"10.1111/bjet.13606","url":null,"abstract":"<p>Despite a proliferation of research on generative artificial intelligence (GenAI) and its applications in higher education (HE), our understanding of the transformative processes where students create productive and ethically grounded uses of GenAI and how AI mediates students' sensemaking is still limited. Based on an empirical investigation of bachelor's degree students from educational sciences (<i>N</i> = 22) carrying out an inquiry-based course assignment, we analysed students' reflective essays to explore how GenAI mediated their sensemaking throughout the academic writing process. We selected an abductive analysis as the main approach to examine the AI-mediated construction of new understanding. Cross-tabulation analysis complemented qualitative analysis, addressing differences in AI-mediated sensemaking processes based on students' age. Our findings capture a multidimensional constellation of AI-mediated sensemaking processes. We found three central dynamics that guided students' sensemaking process: assessing and adapting the textual characteristics of AI-mediated writing, adjusting and improving interactions with GenAI, and contextualising AI-mediated academic writing experiences around everyday study practices. The tensions and ambiguities highlighted the ethical aspects of adopting AI-mediated academic writing practices, although students did not overcome all of these tensions during their sensemaking processes. Our study contributes theoretically by developing the notion of an AI-mediated sensemaking approach, therefore adding to existing understanding about the dialogical trajectories of AI-mediated writing processes through which students create new meanings and understandings of GenAI use as a learning resource. Further, we discuss the collective aspects of AI-mediated sensemaking.</p>","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"56 5","pages":"2001-2018"},"PeriodicalIF":8.1,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://bera-journals.onlinelibrary.wiley.com/doi/epdf/10.1111/bjet.13606","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144811127","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}