British Journal of Educational Technology最新文献

{"title":"Investigating the impact of ChatGPT-assisted feedback on the dynamics and outcomes of online inquiry-based discussion","authors":"Shen Ba, Ying Zhan, Lingyun Huang, Guoqing Lu","doi":"10.1111/bjet.13605","DOIUrl":"10.1111/bjet.13605","url":null,"abstract":"<div>\u0000 \u0000 <section>\u0000 \u0000 <p>This study examines the impact of feedback assisted by generative artificial intelligence (GAI) on the dynamics and outcomes of online inquiry-based discussions (IBDs) in a higher education context. Specifically, it compares two distinct feedback types powered by GAI: idea-oriented and task-oriented. The study involved 105 preservice teachers from a public university in Northwestern China. Participants were pre-assigned into two classes, each receiving different types of GAI-assisted feedback during IBDs. A collection of data, including discussion transcripts, survey responses, and IBD performance, was collected and analysed with statistical methods and epistemic network analysis. The results demonstrated that idea-oriented feedback significantly enhanced cognitive presence and led to higher engagement in the exploration of different ideas and opinions. However, this type of feedback also induced greater negative emotional responses. In contrast, task-oriented feedback promoted more social interaction and group cohesion, though it was less effective in fostering higher-order thinking. The findings suggest that GAI tools can provide meaningful support in online learning settings, but the type of feedback must be carefully aligned with the desired learning outcomes. This research offers insights for optimizing GAI-assisted feedback mechanisms in higher education.</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>Feedback is key to fostering collaborative problem-solving and critical thinking in online inquiry-based discussions (IBDs).</li>\u0000 \u0000 <li>The Community of Inquiry (CoI) model emphasizes the interaction of cognitive, social, and teaching presence for worthwhile learning, with feedback playing a crucial role in regulating these presences.</li>\u0000 \u0000 <li>Generative artificial intelligence (GAI) tools have shown potential for providing real-time and personalized feedback.</li>\u0000 </ul>\u0000 <p>What this paper adds\u0000\u0000 </p><ul>\u0000 \u0000 <li>This study examines two types of GAI-assisted feedback (idea-oriented and task-oriented) and their impact on the dynamics and outcomes of online IBDs.</li>\u0000 \u0000 <li>Idea-oriented feedback significantly enhances cognitive presence and promotes deeper inquiry, while task-oriented feedback fosters social presence and group cohesion.</li>\u0000 \u0000 <li>GAI-assisted feedback, when aligned with specific learning objectives, can meaningfully promote IBD effective","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"56 5","pages":"1710-1734"},"PeriodicalIF":8.1,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144809293","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":"Metacognition meets AI: Empowering reflective writing with large language models","authors":"Seyed Parsa Neshaei, Paola Mejia-Domenzain, Richard Lee Davis, Tanja Käser","doi":"10.1111/bjet.13601","DOIUrl":"10.1111/bjet.13601","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <p>Reflective writing is known as a useful method in learning sciences to improve the metacognitive skills of students. However, students struggle to structure their reflections properly, limiting the possible learning gains. Previous works in educational technologies literature have explored the paradigms of learning from worked and modelling examples, but (a) their application to the domain of reflective writing is rare, (b) such methods might not scale properly to large-scale classrooms, and (c) they do not necessarily take the learning needs of each student into account. In this work, we suggest two approaches of integrating AI-enabled support in digital systems designed around learning from worked and modelling examples paradigms, to provide personalized learning and feedback to students using large language models (LLMs). We evaluate Reflectium, our reflective writing assistant, show benefits of integrating AI support into the learning from examples modalities and compare the perception of the users and their interaction behaviour when using each version of our tool. Our work sheds light on the applicability of generative LLMs to different types of providing support using the learning from examples paradigm, in the domain of reflective writing.</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>Reflective writing fosters metacognitive skills and improves learning gains and personal growth.</li>\u0000 \u0000 <li>The learning from <i>worked</i> and <i>modelling</i> examples paradigms is effective for skill acquisition and applying the acquired knowledge.</li>\u0000 \u0000 <li>Existing reflective writing assistants usually lack dynamic, AI-driven feedback or interactivity, limiting personalization and adaptability to each user's own needs in the learning process.</li>\u0000 </ul>\u0000 <p>What this paper adds\u0000\u0000 </p><ul>\u0000 \u0000 <li>It introduces Reflectium, an AI-enabled reflective writing assistant, integrating intelligent and interactive writing support for both the learning from <i>worked</i> and <i>modelling</i> examples paradigms.</li>\u0000 \u0000 <li>It demonstrates the use of a fine-tuned large language model (LLM) for providing feedback in the learning from <i>worked</i> examples version, and an LLM-powered conversational agent simulating instructor interactions for the learning from <i>modelling</i> examples version.</li>\u0000 \u0000 <li>It reports findings from a user study comparing the positive imp","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"56 5","pages":"1864-1896"},"PeriodicalIF":8.1,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144811282","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":"Ending well: Values in concluding or transitioning community educational technology projects","authors":"Caroline R. Pitt","doi":"10.1111/bjet.13598","DOIUrl":"10.1111/bjet.13598","url":null,"abstract":"<p>Community-partnered educational research projects exist in a complex network of stakeholders, values, time constraints and funding limitations. Many researchers are beholden to mandates around their funding, as well as the tenure clock and the ‘publish or perish’ mindset. However, building rapport and trust with communities takes time and resource investment that is not always prioritized in academia, and the ending process of a project is rarely explored. In this study, the educational technology project ecosystem and power dynamics in which researchers and participants exist is examined, drawing on the stakeholder analysis and value tensions of Value Sensitive Design to focus on the endings of such projects. Using a cross-case analysis of two long-term educational technology projects, the case study data corpus was qualitatively analysed to identify key themes involved in the ending process, based around retrospective interviews with participants from multiple stakeholder groups. This work identifies <i>types of</i> and <i>strategies for ending</i>, including individual endings and transitions, and develops recommendations for equitable ending processes in the context of educational technology projects. The study explores the dimensions and considerations in ending a project that involves a long-term partnership with a community, developing ways to understand, navigate and plan for the closing process and facilitating less extractive and more mutually beneficial community research partnerships.</p>","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"56 4","pages":"1415-1437"},"PeriodicalIF":8.1,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144273197","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":"Aligning human values and educational technologies with value-sensitive design","authors":"Luis P. Prieto,&nbsp;Olga Viberg,&nbsp;Jason C. Yip,&nbsp;Paraskevi Topali","doi":"10.1111/bjet.13602","DOIUrl":"10.1111/bjet.13602","url":null,"abstract":"","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"56 4","pages":"1299-1310"},"PeriodicalIF":8.1,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/bjet.13602","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144273373","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":"Enhancing self-regulated learning and learning experience in generative AI environments: The critical role of metacognitive support","authors":"Xiaoqing Xu,&nbsp;Lifang Qiao,&nbsp;Nuo Cheng,&nbsp;Hongxia Liu,&nbsp;Wei Zhao","doi":"10.1111/bjet.13599","DOIUrl":"10.1111/bjet.13599","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;p&gt;The rapid development of generative artificial intelligence (GenAI) has brought opportunities and new challenges to higher education. Students need a high level of self-regulated learning to adapt to this change. However, it is difficult for students to persist in self-regulation without guidance. Metacognitive support has a significant advantage in enhancing self-regulated learning, but fewer studies have explored the effects of its role in GenAI environments. The purpose of this study was to investigate the impacts of metacognitive support on college students' self-regulated learning and learning experiences in a GenAI environment. A quasi-experiment was designed in which 68 college students were divided into two groups. The experimental group (&lt;i&gt;N&lt;/i&gt; = 35) received explicit metacognitive support, while the control group (&lt;i&gt;N&lt;/i&gt; = 33) did not receive any metacognitive prompts. The experiment lasted 4 weeks. The study measured students' academic performance, self-regulated learning ability and learning experiences (including cognitive load and technology acceptance). The results indicate that in the GenAI environment, metacognitive support, while not producing significant between-group differences in achievement, enhances students' self-regulated learning abilities particularly in terms of task strategy and self-evaluation, as well as optimizing their learning experience. The study also found that students were at risk of decreasing their level of self-regulated learning if they lacked metacognitive support in the GenAI environment. The conclusion points out that GenAI supports learners to accomplish learning tasks while potentially reducing self-regulated learning effectiveness, and that metacognitive support is key to supporting effective regulation in learners' GenAI environments. This study provides an important theoretical and practical basis for how to better support learners' learning in GenAI environments.&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;SRL is vital for effective learning in digital environments.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;Generative AI tools, like ChatGPT, can enhance learning but require support.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;Learners often struggle to apply SRL strategies without guidance.&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;Metacognitive support improves SRL in Generative AI environments.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;It reduces cognitive load and increases the perceived usefulness of AI tools.&lt;/li&gt;\u0000 ","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"56 5","pages":"1842-1863"},"PeriodicalIF":8.1,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144811293","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 teachers' value-sensitive reflections on the cost–benefit dynamics of technology in educational practices","authors":"Davinia Hernández-Leo,&nbsp;Karina Ginoyan","doi":"10.1111/bjet.13592","DOIUrl":"10.1111/bjet.13592","url":null,"abstract":"<p>This paper explores the application of a benefits versus costs reflection approach within non-university teaching environments, grounded in the principles of Value-Sensitive Design. Aimed at integrating human values systematically into the adoption of digital educational tools, this study involved 136 in-service school teachers across various workshops in Catalonia. Through the use of a structured customisable worksheet, educators critically self-evaluated their feelings about both the benefits and costs associated with the use of digital technologies in their teaching practices. The study found that the approach was meaningful to the teachers, who were able to adapt the use of the workshop to their cases. The positive reception by teachers suggests not only a satisfactory level of usability and utility of the approach but also their agreement with the need to integrate related strategies in their training, learning design and community debate processes.</p><p>\u0000 \u0000 </p>","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"56 4","pages":"1350-1369"},"PeriodicalIF":8.1,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/bjet.13592","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144273195","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":"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}