Computers & Education最新文献

Trace-Based Measurement of Self-regulated Learning in Primary Mathematics: Implementation and Validation within an Adaptive Learning Platform 小学数学自我调节学习的追踪测量：自适应学习平台的实施与验证

IF 12 1区教育学

Computers & Education Pub Date : 2026-09-01 DOI: 10.1016/j.compedu.2026.105747

Borchen Kuo, Chiahua Lin, Frederic Taoyi Chang, Chihwei Yang, Xinyu Li, Mladen Raković, Tongguang Li, Linxuan Zhao, Dragan Gašević

引用次数: 0

Preparing pre-service teachers for collaborative design through technology integration familiarization: A mixed-methods study 通过技术整合熟悉为职前教师准备协同设计：一项混合方法研究

IF 12 1区教育学

Computers & Education Pub Date : 2026-09-01 DOI: 10.1016/j.compedu.2026.105748

Bram Cabbeke,Tijs Rotsaert,Tammy Schellens

引用次数: 0

A five-study Design-Based Research Approach to co-design an Institutional AI Digital Assistant 联合设计机构人工智能数字助理的五项研究设计为基础的研究方法

IF 12 1区教育学

Computers & Education Pub Date : 2026-08-31 DOI: 10.1016/j.compedu.2026.105746

Bart Rienties, Duygu Bektik, Emily Coughlan, Tim Coughlan, John Domingue, Chris Edwards, Audrey Ekuban, Christothea Herodotou, Joseph Kwarteng, Chris Sanders, Felipe Tessarolo, Thomas Ullmann

{"title":"A five-study Design-Based Research Approach to co-design an Institutional AI Digital Assistant","authors":"Bart Rienties, Duygu Bektik, Emily Coughlan, Tim Coughlan, John Domingue, Chris Edwards, Audrey Ekuban, Christothea Herodotou, Joseph Kwarteng, Chris Sanders, Felipe Tessarolo, Thomas Ullmann","doi":"10.1016/j.compedu.2026.105746","DOIUrl":"https://doi.org/10.1016/j.compedu.2026.105746","url":null,"abstract":"We report on a Design-Based Research (DBR) study that was conducted over a 13-month period to develop an institutional AI digital assistant (i-AIDA). Many students already use publicly available AI Digital Assistants (p-AIDA) like ChatGPT for academic purposes. However, there are concerns around the use of such p-AIDA tools, particularly in terms of academic integrity, data privacy, intellectual property, and the impact on the quality of education. Furthermore, many higher education institutions have substantial learning materials and data about students that they may not want to share with p-AIDA. Therefore, we explored the perspectives and experiences of developing an i-AIDA with 315 students and 20 staff at BLINDED in the UK in five consecutive studies using multiple methods and data sources (including surveys, interviews, think-aloud, alpha- and beta-testing of i-AIDA). The findings indicated that 24/7 immediate feedback relevant to academic learning was essential for learners. The beta-testing with students indicated that beyond chat participants particularly appreciated the design features of flashcards and quizzes to support their understanding. Participants’ perspectives on i-AIDA significantly improved after their engagement. Our research underscores the potential benefits and limitations of in-house i-AIDA in enhancing learning experiences without compromising academic integrity or privacy, although this still has to be validated in a larger institutional implementation.","PeriodicalId":10568,"journal":{"name":"Computers & Education","volume":"52 1","pages":""},"PeriodicalIF":12.0,"publicationDate":"2026-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148884731","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}

引用次数: 0

Corrigendum to “LLM-derived metrics in second language writing assessment: an explainable AI approach” [Computers & Education 254 (2026) 105721] “法学硕士衍生的第二语言写作评估指标：可解释的人工智能方法”的勘误表[计算机与教育254 (2026)105721]

IF 12 1区教育学

Computers & Education Pub Date : 2026-08-28 DOI: 10.1016/j.compedu.2026.105740

Jingying Hu, Yan Cong

引用次数: 0

The Design Framework for Pedagogical Agents Supporting Constructivist Learning with Generative AI 用生成式人工智能支持建构主义学习的教学代理设计框架

IF 12 1区教育学

Computers & Education Pub Date : 2026-08-28 DOI: 10.1016/j.compedu.2026.105744

Chen-Chung Liu, Chin-Chung Tsai, Ramazan Yilmaz, Jiun-Yu Wu, Maria Ranieri, Di Zou, Leanna Archambault, Johan van Braak, Gautam Biswas

{"title":"The Design Framework for Pedagogical Agents Supporting Constructivist Learning with Generative AI","authors":"Chen-Chung Liu, Chin-Chung Tsai, Ramazan Yilmaz, Jiun-Yu Wu, Maria Ranieri, Di Zou, Leanna Archambault, Johan van Braak, Gautam Biswas","doi":"10.1016/j.compedu.2026.105744","DOIUrl":"https://doi.org/10.1016/j.compedu.2026.105744","url":null,"abstract":"While generative AI lowers the barrier to creating pedagogical agents, unguided designs risk undermining learner agency and hindering deep learning. This paper argues that a constructivist framing provides a strong theoretical basis for realigning pedagogical agent design to promote active knowledge construction and problem-solving. Guided by constructivist principles, we propose a design framework that (1) incorporates context-aware, in-the-moment guidance within authentic, open-ended learning activities; (2) supports the development of interpretation through productive cognitive conflict; (3) encourages collaboration and group discussions; (4) allows artifact co-creation with formative evaluation; and (5) fine-tunes cognitive apprenticeship strategies and intervention timing. We demonstrate how agents implement these principles by applying generative AI and multimodal learning analytics across various constructivist learning settings, including science labs, simulations, and computational modeling environments. We also highlight key safeguards essential for designing and deploying responsible pedagogical agents, including roles in collaboration, teacher oversight, prompt-before-answer rules, positive and open attitudes toward agent use, and maintaining activity authenticity. Grounding pedagogical agents in constructivist principles enables effective use of generative AI’s capabilities while addressing its risks, thereby supporting deeper constructivist learning.","PeriodicalId":10568,"journal":{"name":"Computers & Education","volume":"14 1","pages":""},"PeriodicalIF":12.0,"publicationDate":"2026-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148884732","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}

引用次数: 0

Corrigendum to ‘Assessing information problem solving through a simulation game: Validation evidence from a municipal enforcement official context’ [Computers & Education 244 (2026) 105533] “通过模拟游戏评估信息问题解决：来自市政执法官员背景的验证证据”的勘误表[计算机与教育244 (2026)105533]

IF 12 1区教育学

Computers & Education Pub Date : 2026-08-20 DOI: 10.1016/j.compedu.2026.105741

Aranka Bijl, Sebastiaan de Klerk, Bernard P. Veldkamp, Saskia Wools

引用次数: 0

Corrigendum to “Mapping teacher education strategies to develop digital competence targets: A cross-framework analysis” [Computers & Education 254 (2026) 105699] “绘制教师教育策略以发展数字能力目标：跨框架分析”的勘误表[计算机与教育254 (2026)105699]

IF 12 1区教育学

Computers & Education Pub Date : 2026-08-11 DOI: 10.1016/j.compedu.2026.105729

Els Tanghe, Katie Goeman, Annelies Raes, Tijs Rotsaert, Tammy Schellens, Katrien Struyven, Jo Tondeur, Adriaan Herremans

引用次数: 0

Introduction to “From crisis to continuity: Re-examining online teaching and learning in elementary and secondary education” “从危机到持续：重新审视中小学教育的在线教学”导言

IF 12 1区教育学

Computers & Education Pub Date : 2026-08-07 DOI: 10.1016/j.compedu.2026.105727

Leanna Archambault, Jered Borup, Chin-Hsi Lin

引用次数: 0

Measuring different types and domains of AI knowledge: Developing and validating a performance-based scale 衡量不同类型和领域的人工智能知识：开发和验证基于绩效的量表

IF 10.5 1区教育学

Computers & Education Pub Date : 2026-07-01 Epub Date: 2026-01-12 DOI: 10.1016/j.compedu.2026.105573

Inbal Klein-Avraham , Rut Ston , Osnat Atias , Ido Roll , Ayelet Baram-Tsabari

{"title":"Measuring different types and domains of AI knowledge: Developing and validating a performance-based scale","authors":"Inbal Klein-Avraham , Rut Ston , Osnat Atias , Ido Roll , Ayelet Baram-Tsabari","doi":"10.1016/j.compedu.2026.105573","DOIUrl":"10.1016/j.compedu.2026.105573","url":null,"abstract":"<div><div>As artificial intelligence (AI) and generative AI (GenAI) technologies become increasingly integrated into everyday life, the need for validated tools that measure people's knowledge about AI grows. Here, we present the development and validation of a theoretically driven, performance-based scale for assessing AI and GenAI knowledge. The scale is grounded in a two-axial framework. One axis captures three knowledge types: content knowledge (what AI is and where it is encountered), procedural knowledge (how AI systems operate and are used), and epistemic knowledge (what features and construction processes characterize AI outputs). The other axis encompasses three knowledge domains: technology-related knowledge (AI systems), user-related knowledge (users' interaction with AI), and society-related knowledge (the social and ethical implications of AI). Based on an online survey of 800 internet-using adults from Israel, the 26-item scale was evaluated using confirmatory factor analysis, which demonstrated an acceptable model fit. It was further validated through two-stage structural equation modeling and group comparisons. Overall, the scale was found to be both valid and practically insightful: while it reproduces the expected relationships with additional constructs (e.g., trust in GenAI, attitudes toward AI) and expected differences between demographic groups, it also provides nuanced insights on the intricacies of AI knowledge. For example, the scale indicates that the relationship between trust in GenAI and knowledge about AI is grounded in both epistemic and societal knowledge. Thus, this novel tool affords more precise investigations into how different types and domains of AI knowledge relate to perceptions, behaviors, and decision-making in an AI-mediated world.</div></div>","PeriodicalId":10568,"journal":{"name":"Computers & Education","volume":"247 ","pages":"Article 105573"},"PeriodicalIF":10.5,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145957052","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}

引用次数: 0

Expressions of learner agency in virtual inquiry: Linking agency and evidence-centered game design 虚拟探究中学习者代理的表现：关联代理与循证游戏设计

IF 10.5 1区教育学

Computers & Education Pub Date : 2026-07-01 Epub Date: 2026-02-04 DOI: 10.1016/j.compedu.2026.105590

Jillianne Code , Kieran Forde , Rachel Moylan , Aimee Lutrin , Zahira Tasabehji , Rachel Ralph , Aashay Mehta , Nick Zap , Nesrine El Banna

{"title":"Expressions of learner agency in virtual inquiry: Linking agency and evidence-centered game design","authors":"Jillianne Code , Kieran Forde , Rachel Moylan , Aimee Lutrin , Zahira Tasabehji , Rachel Ralph , Aashay Mehta , Nick Zap , Nesrine El Banna","doi":"10.1016/j.compedu.2026.105590","DOIUrl":"10.1016/j.compedu.2026.105590","url":null,"abstract":"<div><div>Game-based learning environments often support exploration but rarely connect learner agency with rigorous, embedded assessment. This study reports on the design and pilot implementation of ALIVE (Agency for Learning in Immersive Virtual Environments), a virtual inquiry environment that integrates the Agency for Learning framework with Evidence-Centered Game Design. Nine middle and high school students completed an ecological investigation that required evidence collection, hypothesis testing, and causal explanation. Data included think-aloud protocols, gameplay logs, and brief feedback questions. Triangulated analyses captured both convergence and divergence between self-reported and observed agency. Learners showed intentional decisions, strategy shifts, and selective delegation to system supports at points of uncertainty. These findings show how aligned competency, evidence, and task models make inquiry actions visible and interpretable. The study also offers a multisource approach for examining expressions of agency within guided digital inquiry. Limitations include the small sample and single-session design. Future work should examine longer-term patterns, broader implementation, and transfer across domains.</div></div>","PeriodicalId":10568,"journal":{"name":"Computers & Education","volume":"247 ","pages":"Article 105590"},"PeriodicalIF":10.5,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146134173","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}

引用次数: 0