British Journal of Educational Technology最新文献

{"title":"A systematic review of generative AI in education: Empirical insights from a human–AI interaction perspective","authors":"Zhiping Liang,&nbsp;Kaixun Yang,&nbsp;Lele Sha,&nbsp;Dragan Gašević,&nbsp;Lixiang Yan,&nbsp;Guanliang Chen","doi":"10.1111/bjet.70055","DOIUrl":"https://doi.org/10.1111/bjet.70055","url":null,"abstract":"<p>With the increasing application of GenAI in education, researchers and practitioners are paying more and more attention to its effectiveness and impact in teaching and learning. This can be evidenced by the increasing number of literature reviews on this topic published in recent years. However, these literature reviews seldom analysed the effectiveness of GenAI-powered educational applications from the human–AI interaction perspective, which is a widely recognized critical factor influencing the effectiveness of such GenAI-powered educational applications. In response, this study systematically reviews 56 empirical studies on the application of GenAI in education. To explicitly address this gap from the human–AI interaction perspective, we analysed the reviewed studies using the AIED-HCD framework, which conceptualizes three human–AI interaction modes along the dimensions of human control and AI automation, and examined educational contextual factors and educational tasks supported by GenAI to assess how interaction modes vary across teaching and learning contexts. In addition, we conducted a sensitivity analysis to evaluate the robustness of findings across these modes. We demonstrated that, although current educational practices remain cautious towards interaction modes with a high level of AI automation, the mode characterized by both high human control and high AI automation has begun to emerge as a trend, demonstrating promising potential for integrating the respective strengths of humans and AI. Furthermore, sensitivity analysis reveals that many studies lack sufficient detail in their statistical reporting, and the reported effect sizes often fall below the thresholds required for acceptable statistical power. Based on these findings, we recommend: (i) beyond exploring how to improve the practical use of AI automation under human supervision and control, educational researchers and practitioners should carefully choose and implement suitable human–AI interaction settings according to the specific context of use, with higher levels of AI automation applied only when supported by appropriate task design and pedagogical guidance; (ii) researchers should improve methodological transparency by estimating appropriate sample sizes and testing assumptions to ensure the reliability of empirical findings.</p><p>\u0000 \u0000 </p>","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"57 5","pages":"1221-1271"},"PeriodicalIF":13.0,"publicationDate":"2026-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/bjet.70055","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148696633","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 MOOCs: How technical and structural factors shape learner engagement, retention and inclusivity across online learning platforms","authors":"Rotimi Ogunsakin,&nbsp;Deborah Okonu,&nbsp;Paschal Nwokeocha","doi":"10.1111/bjet.70066","DOIUrl":"https://doi.org/10.1111/bjet.70066","url":null,"abstract":"<p>This study examines the critical influence of technical and structural factors—such as interface design, technical performance, payment barriers, certification access, pricing models and accessibility constraints—on learner Engagement, Retention and Inclusivity (ERI) in Massive Open Online Courses (MOOCs) and other large-scale online learning platforms. Employing a novel mixed-methods analytical method termed 2TS (Topic Modelling, Sentiment Classification, Systematic Keyword Selection and Thematic Analysis), we analysed over 226,000 user reviews from six globally recognised online learning platforms, including traditional MOOC providers and other large-scale digital learning systems: Coursera, edX, Udemy, Alison, uLesson and Khan Academy. Our findings suggest that technical instability, limited offline functionalities and unclear pricing structures have a negative impact on learner engagement and retention. Furthermore, regional payment restrictions and ambiguous subscription terms disproportionately disadvantage learners in low-resource settings, significantly undermining the inclusivity of online learning platforms. The research highlights how carefully structured platform design and robust technical infrastructure have a significant influence on digital learning outcomes. Practical implications underscore the need for platform providers to enhance platform usability, promote transparent pricing and adopt inclusive technological solutions, particularly to ensure equitable access and engagement among diverse global audiences.</p><p>\u0000 \u0000 </p>","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"57 5","pages":"1348-1373"},"PeriodicalIF":13.0,"publicationDate":"2026-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148695823","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":"Enhancing creative writing with robot–LLM integration: The interplay of embodiment, AI creativity and user engagement","authors":"Yuqing Liu,&nbsp;Yao Song","doi":"10.1111/bjet.70071","DOIUrl":"https://doi.org/10.1111/bjet.70071","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;p&gt;This study explores the impact of robot–LLM (Large Language Model) integration on collaborative creative writing, focusing on how embodiment and AI creativity influence various aspects of creative output. A total of 150 undergraduate students participated in a structured experimental design with five collaboration conditions: Human–Human (HH), Human–Computer with High-Creativity LLM (HC), Human–Robot with High-Creativity LLM (HR), Human–Robot with Low-Creativity LLM (RL) and Human–Computer with Low-Creativity LLM (CL). Creativity was assessed through expert ratings and computational analysis of originality, imagery, voice and semantic flow. The results revealed that while the Human–Robot (High-Creativity LLM) condition significantly enhanced originality, Human–Human and Human–LLM (text-based) collaborations excelled in imagery and voice. The study identified an ‘embodiment paradox’, where robot embodiment amplified creativity in high-creativity AI conditions, yet human collaboration remained superior in stylistic expression. Mediation analysis revealed that user engagement acted as a mediator, with embodiment compensating for low-creativity AI and amplifying the creative process with high-creativity AI. The findings have important implications for the design of collaborative AI systems, highlighting the need for a balanced integration of embodiment and AI creativity to optimize creative outcomes. This research contributes to our understanding of how human–robot–LLM collaborations can expand creative potential in writing, offering insights for future AI applications in educational and creative industries.&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;Previous studies have explored the impact of AI in creative collaborations, with a focus on text-based models like LLMs enhancing writing quality.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;Embodiment in AI systems, such as humanoid robots, has been shown to affect user engagement and emotional responses, influencing creativity.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;Human collaboration has traditionally been seen as superior in generating stylistic elements like imagery and voice, while AI excels in originality and idea generation.&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 research demonstrates that robot–LLM collaboration significantly boosts originality, particularly when high-creativity AI is used.&lt;/li&gt;\u0000 \u0000 ","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"57 5","pages":"1320-1347"},"PeriodicalIF":13.0,"publicationDate":"2026-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/bjet.70071","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148696286","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":"Will, Skill, Not Tool: Chinese university students' acceptance of generative AI for academic writing in informal English medium instruction settings","authors":"Pengzhan Yang,&nbsp;Wenqian Huang","doi":"10.1111/bjet.70075","DOIUrl":"https://doi.org/10.1111/bjet.70075","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;p&gt;The generative AI (GenAI) has exhibited diverse functions in supporting academic writing, especially for university students who encounter both disciplinary content and academic English challenges in English medium instruction (EMI). However, existing studies have rarely addressed EMI students' behavioural intentions to use GenAI for academic writing beyond formal classroom settings. By adopting the Will, Skill, Tool (WST) model, this study explores how EMI students' intentions are shaped by emerging AI-specific variables, including perceived risks, perceived importance of policy (PIP) and AI literacy. Survey data were collected from 512 university students at an EMI university in China. The structural equation model explained 56% of the variance in EMI students' behavioural intentions. The findings indicated that will-related factors (eg, attitudes, perceived risks, PIP) and the skill factor (AI literacy) were significant antecedents of students' intentions, whereas the tool component (facilitating conditions) was not. Attitudes and perceived risks served as significant indirect pathways through which PIP and AI literacy were associated with behavioural intentions. The findings have implications for pedagogical practices and policy design in promoting EMI students' adoption of GenAI for academic writing.&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;Some Chinese university students use generative AI (GenAI) tools to address both disciplinary and linguistic challenges in academic writing outside English medium instruction (EMI) classrooms.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;Students' perceived risks, perceived importance of policy and AI literacy have emerged as key predictors of their adoption of GenAI, but the interplay among these variables remains underexplored.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;Despite the wide application in teachers' technology adoption, the Will, Skill, Tool (WST) model has rarely been adopted to explain students' intention to use GenAI in informal learning contexts.&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;The WST-based model explained 56% of the variance of Chinese EMI students' behavioural intention to use GenAI for academic writing in informal settings.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;The will and skill components significantly predicted students' behavioural intentions, whereas the tool element did not.&lt;/li&gt;\u0000 ","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"57 5","pages":"1374-1394"},"PeriodicalIF":13.0,"publicationDate":"2026-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/bjet.70075","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148696525","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 chatbots as reading companions in self-directed out-of-class reading: A self-determination theory perspective","authors":"Mengru Pan,&nbsp;Chun Lai,&nbsp;Kai Guo","doi":"10.1111/bjet.70002","DOIUrl":"https://doi.org/10.1111/bjet.70002","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;p&gt;Self-directed learning (SDL) is pivotal for academic success and lifelong learning, in which motivation has been a major challenge, especially in the context of self-directed out-of-class reading. According to self-determination theory (SDT), the fulfilment of three basic psychological needs (BPNs)—autonomy, competence and relatedness—is key to autonomous motivation. However, it is hard to provide personalised support to BPNs in self-directed out-of-class reading. Generative artificial intelligence (GenAI), with its capacity for generative human-like outputs, makes the provision of personalised interactive support possible. This study thus examined how GenAI-powered chatbots can be utilised to serve as reading companions to fulfil BPNs in self-directed out-of-class reading. An online reading platform was developed, where students engaged in self-directed out-of-class reading. Sixty-nine students from two classes at a Chinese university were randomly assigned to two reading conditions: the experimental group (EG; &lt;i&gt;N&lt;/i&gt; = 36) where students had access to a GenAI-powered chatbot as a reading companion on the platform; and the control group (CG; &lt;i&gt;N&lt;/i&gt; = 33) where students did not have access to the GenAI-powered chatbot during reading. Multiple data sources were collected and analysed, including pre- and post-reading tests, pre- and post-questionnaires on reading motivation and self-directed out-of-class reading, log data on reading amounts and semi-structured interviews. The findings revealed that EG showed significant improvement in autonomous reading motivation and self-directed out-of-class reading. Reading performance did not differ between the two groups. This study offers valuable evidence on the efficacy of integrating GenAI chatbots into SDL through the lens of SDT. Pedagogically, educators can leverage GenAI technology to cater to students' BPNs, bolstering their SDL capabilities and academic achievement.&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 the topic\u0000\u0000 &lt;/p&gt;&lt;ul&gt;\u0000 \u0000 &lt;li&gt;Self-directed learning (SDL) has been recognised as a crucial competence for academic success and lifelong learning.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;Motivation has been a major challenge in self-directed out-of-class reading, with the fulfilment of basic psychological needs (BPNs) identified as vital for autonomous motivation according to self-determination theory (SDT).&lt;/li&gt;\u0000 \u0000 &lt;li&gt;Generative artificial intelligence (GenAI) has the potential to fulfil students' BPNs by providing personalised interactive support to enhance their motivation and SDL.&lt;/li&gt;\u0000 &lt;/ul&gt;\u0000 ","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"57 4","pages":"1036-1064"},"PeriodicalIF":8.1,"publicationDate":"2026-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148237548","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":"Learner agency in revising GenAI-generated statements of purpose","authors":"Yuanlan Jiang,&nbsp;Qian Wu,&nbsp;Yi Yang,&nbsp;Changle Jian,&nbsp;Jing Zhao","doi":"10.1111/bjet.70041","DOIUrl":"https://doi.org/10.1111/bjet.70041","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;p&gt;This study investigates how English as a foreign language (EFL) learners enact their agency in revising generative AI (GenAI)-generated academic texts, in this case the statement of purpose, or SOP. Conducted at a key university in Southern China, the study involved 121 English-major students. This study included two phases. First, we used the Curriculum Vitae (CVs) submitted by the participants to generate SOP drafts through a standardized prompt in ChatGPT-4. Second, the participants acted as evaluators and editors, engaging with the GenAI-generated SOPs to varying extents. Nine of the participants attended semi-structured retrospective interviews. Textual comparison of original and final SOPs was conducted to identify patterns of agency, non-parametric analysis was performed between learners' ratings and their identified agentic patterns, and thematic analysis was applied to the interview data. The study revealed three agentic revision patterns: compliance-oriented acceptance, form-oriented modification, and content-oriented innovation. Differences in learners' ratings of GenAI-generated SOPs were found across the three patterns. Two factors influenced these patterns: authentic voice versus GenAI-constructed textual voice and enthusiasm to present intentions. The study suggests that empowering EFL learners to activate their agency in using GenAI tools enhances their academic writing and supports them in meeting specific task requirements.&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;EFL learners often face challenges in academic writing, particularly in incorporating GenAI-generated material effectively.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;Agency in revision processes is important for EFL learners using GenAI tools.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;Previous research highlights the potential of GenAI in supporting writing tasks, but there is limited insight into how EFL learners express their agency during revisions of GenAI-generated content.&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 explored how EFL learners enact their agency when revising GenAI-generated academic texts (ie, SOPs).&lt;/li&gt;\u0000 \u0000 &lt;li&gt;We identified three specific patterns of agency enactment during the revision process, namely, compliance-oriented acceptance, form-oriented modification, and content-oriented innovation.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;The research highlighted factors influencing these agency patterns, including a","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"57 4","pages":"965-983"},"PeriodicalIF":8.1,"publicationDate":"2026-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148237690","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":"Can virtual technology be used to improve students' perspective taking ability? Evidence from a meta-analysis","authors":"Tianhui Hu,&nbsp;Bo He,&nbsp;Huanyou Chai,&nbsp;Shuai Yuan,&nbsp;Fan Yang","doi":"10.1111/bjet.70049","DOIUrl":"https://doi.org/10.1111/bjet.70049","url":null,"abstract":"<p>Virtual technology holds tremendous potential for cultivating students' perspective taking ability. It contributes to diminishing intergroup conflict and enhancing social harmony by facilitating students' understanding and adoption of an alternative perspective. This study used meta-analysis to test the overall effect of virtual technology on students' perspective taking ability. A total of 20 experimental (quasi) studies (published between 2009 and 2024) that documented either a comparison between a group adopting virtual technologies (21 effect sizes in 15 studies) and a control group or a pre–post comparison (six effect sizes in five studies) were identified through the literature search. Utilizing a random effects model to compute effect sizes, the results revealed that virtual technology exerts a medium influence on perspective taking ability (Hedges' <i>g</i> = 0.505), with notable heterogeneity observed across the studies. Furthermore, the moderator analysis results indicated that virtual technology is more influential (a) on adult learners, (b) when adopted to teach declarative knowledge, (c) when offering visual clues feedback, (d) when combined with inquiry-discovery or test assessment method, (e) when accompanied by observational learning and (f) when in an experimental period lasting 30–90 minutes. These findings provide beneficial insights for future research and practical applications aimed at adopting virtual technology to cultivate students' perspective-taking ability.</p><p>\u0000 \u0000 </p>","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"57 4","pages":"1071-1093"},"PeriodicalIF":8.1,"publicationDate":"2026-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148238037","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":"Enhancing self-directed learning and Python mastery through integration of a large language model and learning analytics dashboard","authors":"Ming Liu,&nbsp;Zhongming Wu,&nbsp;Haimin Dai,&nbsp;Yifei Su,&nbsp;Laiba Malik,&nbsp;Jian Liao,&nbsp;Wei Zhang,&nbsp;Shuo Guo,&nbsp;Li Liu,&nbsp;Junqiang Zhao","doi":"10.1111/bjet.70005","DOIUrl":"https://doi.org/10.1111/bjet.70005","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;p&gt;Self-directed learning (SDL) is a critical skill in the 21st century, particularly in online Python learning environments. Learning analytics (LA) can track and analyse learning processes, which can be leveraged to prompt students to reflect on their learning strategies and progress through learning analytics dashboards (LADs). However, LADs lack pedagogical domain knowledge and fail to provide effective personalised feedback and guidance. This study designs and presents a Generative AI-powered SDL tool, SDLChat. It integrates a large language model (ERNIE-3.5) with retrieval-augmented generation (RAG) technology to generate contextualised, actionable feedback for learners across the entire SDL cycle: planning, self-monitoring and self-reflection. To evaluate the impact of SDLChat on learners' SDL skills and Python knowledge, a randomised experimental study was conducted over a six-week Python online course. The study compared the changes in SDL skills and Python knowledge of students using both SDLChat and LAD group (&lt;i&gt;n&lt;/i&gt; = 39) and LAD-only group (&lt;i&gt;n&lt;/i&gt; = 35). The results indicate that: (1) students using SDLChat and LAD significantly outperformed those using LAD alone in Python knowledge mastery, self-monitoring and interpersonal skills and (2) the LAD-only group showed significant improvement only in Python knowledge mastery; however, (3) no significant differences were found in posttask motivation between these two groups. This study highlights the potential of integrating LLM with learning analytics to enhance SDL skills and learning performance in online learning contexts. It also establishes a theory-informed operational framework for understanding the LLM-empowered SDL process.&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;Self-directed learning (SDL) is essential for success in online learning environments, requiring learners to plan, manage, monitor and reflect on their learning processes.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;Learning analytics (LA), particularly in the form of learning analytics dashboards (LADs), is commonly used to track SDL processes and encourage learner reflection.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;Traditional LADs are incapable of providing personalised feedback, limiting their effectiveness in enhancing SDL skills and learning performance.&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;Introduces SDLChat, an LLM-powered SDL tool combining a large language model (ERNIE-3.5) and retrieval-augmented generation (RAG) technology to g","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"57 4","pages":"1009-1035"},"PeriodicalIF":8.1,"publicationDate":"2026-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148238158","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' professional agency in learning with AI: A case study of a generative AI-based knowledge building learning companion for teachers","authors":"Seng Chee Tan,&nbsp;Yee Yin Tan,&nbsp;Chew Lee Teo,&nbsp;Guangji Yuan","doi":"10.1111/bjet.70013","DOIUrl":"https://doi.org/10.1111/bjet.70013","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;p&gt;This study investigated using generative artificial intelligence (GAI) as a learning companion for elementary school teachers to understand, design and implement the knowledge building (KB) approach in their school. The KB learning companion for teachers (KB LCT), a GAI chatbot, was developed to support teachers, integrating human–AI and human–human interactions. The cultural-historical activity theory (CHAT) was used as a methodological lens to examine the professional learning of the teachers. We studied three lead teachers who first experienced the chatbot and facilitated the other 17 teachers in their learning. We uncovered how these lead teachers leveraged the affordances of the chatbot within the community for learning and exercised their professional agency to reconcile contradictions in the system for other teachers. Consequently, they showed evidence of learning about KB, deepening understanding and contextualising KB principles in classrooms, and transforming professional learning structures and activities for their colleagues.&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;A community approach to teacher professional development is effective.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;GAI has been used to support teachers' work by generating lesson plans.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;The knowledge building approach is effective in improving teachers' professional agency.&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;The use of GAI as a learning companion for teachers.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;Affordances and contradictions of the GAI learning companion in a community experienced by the teachers.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;Teachers' manifestation of agency in learning related to the GAI and the learning community.&lt;/li&gt;\u0000 &lt;/ul&gt;\u0000 &lt;p&gt;Implications for practice and/or policy\u0000\u0000 &lt;/p&gt;&lt;ul&gt;\u0000 \u0000 &lt;li&gt;Using a GAI learning companion in a community is effective for teacher learning.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;Teacher professional agency in learning can be manifested in how teachers leverage the affordances of the learning environment.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;Teacher professional agency in learning can be facilitated by providing the opportunity to reconcile contradictions they experience.&lt;/li&gt;\u0000 &lt;/ul&gt;\u0000 &lt;/div&gt;\u0000 &lt;/div&gt;\u0000 &lt;/","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"57 4","pages":"943-964"},"PeriodicalIF":8.1,"publicationDate":"2026-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148237363","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":"Supporting learner agency in collaborative writing with generative AI","authors":"Sujin Kim,&nbsp;Hyo-Jeong So,&nbsp;Kyudong Park","doi":"10.1111/bjet.70015","DOIUrl":"https://doi.org/10.1111/bjet.70015","url":null,"abstract":"<p>To support learner agency in LLM-powered writing environments, this research introduces a new interface that integrates two key features: an <i>Argument Outline</i> and a <i>Similarity Viewer</i>. These features were designed to enhance intentionality in writing and self-monitoring of reliance on AI-generated suggestions. Implemented in <i>CoAuthor</i>, a GPT-based writing tool, the interface was tested in an experiment with 52 university students who completed an argumentative writing task under two conditions: (1) SV group (<i>n</i> = 26) with Similarity Viewer only, and (2) SV-AO group (<i>n</i> = 26) with both Similarity Viewer and Argument Outline. Writing behaviours such as seeking, dismissing, accepting and modifying AI suggestions were analysed using statistical methods and epistemic network analysis (ENA). The average semantic similarity was calculated to measure the similarity between final texts and AI-generated content. Follow-up interviews were conducted to explore participants' perceptions of the two features. Results showed that the SV-AO group had stronger centrality in behaviours linked to independent text composition and produced final texts with lower semantic similarity. Interview data further indicated that participants viewed the Argument Outline as a useful scaffold for supporting independent writing while enabling the selective integration of AI-generated suggestions aligned with their writing goals. Overall, this study highlights the potential of interface design in optimizing the balance between learner agency and AI assistance in human–AI co-writing.</p>","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"57 4","pages":"984-1008"},"PeriodicalIF":8.1,"publicationDate":"2026-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/bjet.70015","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148237542","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}