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Intelligent‑TPACK in practice: design and evidence from a three‑week teacher preparation module 智能TPACK在实践中：设计和证据从一个为期三周的教师准备模块

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2025-11-08 DOI: 10.1016/j.caeo.2025.100306

Tugce Aldemir , Selcuk Kilinc , Ali Bicer , Patricia Grant , Trina Davis , Noelle Wall Sweany

Rapid advances in generative AI sharpen the need for teachers to develop pedagogical and ethical capacities for AI‑integrated instruction. While Technological Pedagogical Content Knowledge (TPACK) provides a valuable framework for technology integration, it does not fully capture AI’s unique complexities. This study presents an integrated i‑TPACK approach that extends Intelligent‑TPACK by adding AI‑as‑content (i‑CK) and AI‑for‑professional development (i‑PD) and by threading a five‑stage AI‑literacy progression (Know→ Use→ Evaluate→ Ethics→ Create) within each domain, treating ethics as distributed and iterative. We designed and examined a three-week professional development module for preservice teachers using a convergent mixed-methods design. Pre–post surveys (n = 25 matched pairs) with a six‑subscale Integrated i‑TPACK instrument showed statistically significant gains across all domains (Wilcoxon, Holm‑adjusted; medium‑to‑large effects). Qualitative analyses of lesson artifacts, decision logs, reflections, and micro-teaching documented instances of layered ethical decision-making (privacy/data governance, bias/fairness, transparency/provenance/accountability), progression along the AI literacy stages, and discipline-aligned pedagogical designs. Embedding an ethical decision‑making checkpoint across performance‑based activities made ethics visible in teacher work and coincided with more explicit safeguards and verification steps in lesson artifacts and micro‑teaching within the module. By detailing this empirically grounded model, our study offers theoretical and practical insights for teacher educators seeking to cultivate principled GenAI-supported instruction.

生成式人工智能的快速发展使教师更加需要培养与人工智能相结合的教学和伦理能力。虽然技术教学内容知识（TPACK）为技术集成提供了一个有价值的框架，但它并没有完全捕捉到人工智能独特的复杂性。本研究提出了一种集成的i - TPACK方法，通过在每个领域内添加AI - as - content （i - CK）和AI - for - professional development (i - PD)，并将AI - literacy的五阶段进展（Know→Use→Evaluate→Ethics→Create），将伦理视为分布式和迭代的，从而扩展了Intelligent - TPACK。我们采用融合混合方法设计，为职前教师设计并检验了一个为期三周的专业发展模块。使用六亚量表集成i - TPACK仪器进行的前后调查（n = 25对配对）显示，在所有领域（Wilcoxon， Holm调整；中大型效应）都有统计学上的显著收益。对课程人工制品、决策日志、反思和微观教学的定性分析记录了分层道德决策（隐私/数据治理、偏见/公平、透明度/来源/问责制）、人工智能素养阶段的进展以及与学科一致的教学设计的实例。在基于绩效的活动中嵌入道德决策检查点，使道德在教师工作中可见，并与模块内的课程工件和微教学中更明确的保障和验证步骤相吻合。通过详细介绍这个基于经验的模型，我们的研究为寻求培养有原则的基因人工智能支持教学的教师教育工作者提供了理论和实践见解。

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引用次数: 0

Intelligent-TPACK in teacher education: Examining preservice elementary teachers’ emerging views about AI classroom use 教师教育中的Intelligent-TPACK：职前小学教师关于人工智能课堂使用的新观点研究

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2025-11-08 DOI: 10.1016/j.caeo.2025.100307

Jeffrey Radloff , Ibrahim H. Yeter , Thomas K.F. Chiu

As artificial intelligence (AI) is increasingly implemented in educational contexts, elementary teacher preparation programs must equip preservice teachers (PSTs) with knowledge and skills related to AI. AI presents novel challenges for teachers while holding transformative potential for teaching and learning. Grounded in IntelligentTPACK, this study examines the perceptions of elementary (i.e., PK-6) PSTs regarding AI and its perceived classroom applications. Participants include 49 PSTs at a northeastern US teaching college enrolled in science methods and critical media literacy courses that explicitly and reflectively introduce AI applications and their uses. Data were collected through researcher-developed pre- and post-surveys, as well as open-ended Intelligent-TPACK reflections. Data were analyzed using thematic coding, with Intelligent-TPACK serving as the lens. Our analyses revealed that PSTs held mixed views and varied perceptions of AI's uses, as well as some uncertainty. Yet, most recognized the potential of AI for supporting differentiated learning, brainstorming, and the generation of teaching materials (I-PK). Trained as PK-6 ‘generalists,’ few PSTs expressed specific disciplinary connections (I-CK). Only half described concerns about AI biases and overreliance (Ethics), and the majority discussed AI as a tool (ITK). As such, PSTs demonstrated emerging Intelligent-TPACK, with a need for more attention to fostering content-specific uses and AI ethics. Findings support similar literature while providing novel PST perspectives, and as such, reveal discrete entry points for further Intelligent-TPACK consideration and research. Results further inform IntelligentTPACK explorations and underscore the role of teacher education in shaping PSTs’ ethical and effective use of AI in their future classrooms.

随着人工智能（AI）越来越多地应用于教育领域，小学教师培训项目必须为职前教师（pst）提供与人工智能相关的知识和技能。人工智能给教师带来了新的挑战，同时也为教学和学习带来了变革潜力。本研究以IntelligentTPACK为基础，考察了小学（即PK-6年级）学生对人工智能及其课堂应用的看法。参与者包括美国东北部一所教学学院的49名pst，他们参加了科学方法和批判性媒体素养课程，这些课程明确地、反思性地介绍了人工智能应用及其用途。数据收集通过研究人员开发的前后调查，以及开放式智能- tpack反思。数据分析采用主题编码，以Intelligent-TPACK为镜头。我们的分析显示，pst对AI的用途持有不同的观点和不同的看法，以及一些不确定性。然而，大多数人都认识到人工智能在支持差异化学习、头脑风暴和教材生成（I-PK）方面的潜力。作为PK-6的“通才”，很少有pst表现出特定的学科联系（I-CK）。只有一半的人表达了对人工智能偏见和过度依赖的担忧（伦理），大多数人认为人工智能是一种工具（ITK）。因此，pst展示了新兴的智能tpack，需要更多地关注促进特定内容的使用和人工智能伦理。研究结果支持了类似的文献，同时提供了新颖的PST视角，因此，为进一步的Intelligent-TPACK考虑和研究揭示了离散的切入点。结果进一步为IntelligentTPACK的探索提供了信息，并强调了教师教育在塑造pst在未来课堂中道德和有效地使用人工智能方面的作用。

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引用次数: 0

Relationship between pre-service teachers’ perceived competencies, affective dispositions, and readiness to use artificial intelligence: A study informed by the intelligent-TPACK 职前教师感知能力、情感倾向和使用人工智能的准备程度之间的关系：一项由智能tpack提供信息的研究

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2025-11-08 DOI: 10.1016/j.caeo.2025.100305

Ayesha Sadaf , Daniel Maxwell , Can Küplüce , Heiko Holz

As artificial intelligence (AI) continues to reshape educational contexts, understanding pre-service teachers’ (PSTs) readiness to integrate AI into their teaching is increasingly critical. This study explored the relationship among PSTs’ perceived AI competencies, affective dispositions, and intentions to use AI in teaching. Eighty PSTs from a southeastern U.S. university completed an online survey assessing their technological, pedagogical, content, and ethical knowledge related to AI, as well as affective dispositions such as interest, self-efficacy, attitudes, and behavioral intentions. Findings revealed that PSTs reported low-to-moderate AI competencies, with the highest confidence in TPACK and the lowest in technological knowledge and AI ethics.

Affective responses were mixed, with moderate interest and perceived relevance of AI, but low self-efficacy and intention to use. Correlational analyses showed strong relationships between AI competencies and positive affective dispositions, particularly self-efficacy and interest, which significantly predicted intention to use AI. Regression analyses further showed that ethical awareness and integrated pedagogical-technological knowledge predicted perceptions of teacher preparation, while ethics also reinforced technological content knowledge. This study highlights the interconnected roles of competency, affect, and ethics in shaping PSTs’ readiness for AI integration in teaching.

随着人工智能（AI）继续重塑教育环境，了解职前教师（pst）是否愿意将人工智能整合到他们的教学中变得越来越重要。本研究探讨了教师感知的人工智能能力、情感倾向和在教学中使用人工智能的意图之间的关系。来自美国东南部一所大学的80名pst完成了一项在线调查，评估了他们与人工智能相关的技术、教学、内容和伦理知识，以及兴趣、自我效能、态度和行为意图等情感倾向。调查结果显示，pst报告了低至中等的人工智能能力，对TPACK的信心最高，对技术知识和人工智能伦理的信心最低。情感反应是混合的，对人工智能的兴趣和感知相关性中等，但自我效能和使用意图较低。相关分析显示，人工智能能力与积极情感倾向之间存在很强的关系，尤其是自我效能感和兴趣，它们显著地预测了使用人工智能的意愿。回归分析进一步表明，伦理意识和综合教学技术知识预测教师准备的感知，而伦理也增强了技术内容知识。本研究强调了能力、情感和道德在塑造教师将人工智能融入教学的准备方面的相互关联的作用。

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引用次数: 0

Adaptive instructional designs in blended learning to enhance student engagement and self-regulation 混合式学习中的适应性教学设计，提高学生的参与度和自我调节能力

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2025-10-25 DOI: 10.1016/j.caeo.2025.100299

Jinsong Zou , Songyu Jiang

Blended-learning courses in Chinese higher education often suffer from student classroom-avoidance behaviours and weak self-regulation. To address this problem, the present study integrates the Technology Acceptance Model (TAM), the Community of Inquiry (CoI) framework, and Self-Regulated Learning (SRL) theory into a single structural model that explains how technology acceptance and teaching practice shape learning engagement. Beyond theory testing, the study also aims to curb avoidance behaviours by creating a closed-loop mechanism that links formative-assessment reform, the cultivation of integrated regulation competences, and subsequent behavioural transformation.

Survey data from 714 undergraduates across 25 Chongqing institutions were analysed with structural equation modelling. Perceived ease of use (PEU) showed a strong, significant relation with teaching presence (TP) (β = 0.466), whereas perceived usefulness (PU) did not. TP, operationalised via assessment for/as/of learning practices, directly enhanced learning presence (LP) (β = 0.333) and boosted blended-learning motivation (BLM) (β = 0.466). BLM exerted the largest direct effect on LP (β = 0.414) and fully mediated the PEU → LP and TP → LP pathways. These findings confirm motivation as the pivotal conduit through which user-friendly technology and formative teaching presence foster deeper learning engagement and reduce avoidance behaviours.

By validating a unified TAM–CoI–SRL model on a large, socio-economically diverse sample, the study advances blended-learning theory and offers practitioners evidence that enhancing usability and assessment-driven teaching presence is essential for improving learning presence and mitigating disengagement in digitally mediated classrooms.

我国高等教育中混合式学习课程存在学生回避课堂行为和自我调节能力弱的问题。为了解决这一问题，本研究将技术接受模型（TAM）、探究共同体（CoI）框架和自我调节学习（SRL）理论整合为一个单一的结构模型，解释技术接受和教学实践如何影响学习参与。除了理论测试之外，该研究还旨在通过创建一个将形成性评估改革、综合监管能力培养和随后的行为转变联系起来的闭环机制来遏制回避行为。采用结构方程模型对重庆市25所高校714名大学生的调查数据进行分析。感知易用性（PEU）与教学存在感（TP）表现出强烈的显著关系（β = 0.466），而感知有用性（PU）则没有显著关系。通过对学习实践的评估来实施TP，直接提高了学习存在感（LP）（β = 0.333）和混合式学习动机（BLM）（β = 0.466）。BLM对LP的直接影响最大（β = 0.414），完全介导PEU→LP和TP→LP通路。这些发现证实了动机是关键的渠道，通过它，用户友好的技术和形成性的教学存在促进了更深层次的学习参与，减少了回避行为。通过在一个大的、社会经济多样化的样本上验证统一的TAM-CoI-SRL模型，该研究推进了混合学习理论，并为实践者提供了证据，证明增强可用性和评估驱动的教学存在对于改善学习存在和减轻数字媒介课堂的脱离感至关重要。

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引用次数: 0

Competencies for teaching with and about artificial intelligence in the natural sciences — DiKoLAN AI 自然科学中的人工智能教学能力- DiKoLAN AI

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2025-10-24 DOI: 10.1016/j.caeo.2025.100303

Johannes Huwer , Christoph Thyssen , Sebastian Becker-Genschow , Lena von Kotzebue , Alexander Finger , Erik Kremser , Sandra Berber , Mathea Brückner , Nikolai Maurer , Till Bruckermann , Monique Meier , Lars-Jochen Thoms

The rapid advancement and widespread adoption of digital technologies have transformed the education sector. Among these developments, the emergence of generative artificial intelligence (AI) tools such as ChatGPT has had a considerable impact on teaching and learning practices. While the integration of AI into educational settings is becoming increasingly common, subject-specific analyses, especially in STEM education, are still lacking. This paper examines the specific challenges and potential of AI in the context of STEM education. It does so by exploring how AI has transformed scientific disciplines and how these changes impact teaching and learning. It highlights the necessity for educators to acquire specific competencies to effectively incorporate AI into their instructional practices. Building on existing frameworks such as DigCompEdu and the subject-specific DiKoLAN, the paper proposes an AI-focused framework: DiKoLAN AI. This framework aligns AI-related teacher competencies with instructional practice in science education. It also provides a structure for categorizing existing teacher training programs. The paper outlines the development of the DiKoLAN AI framework and its content consensus validation by a total of 64 experts through three iterative cycles. Its practical application is demonstrated through 20 case studies from different authors, which offer a practical approach for supporting teacher training and curriculum design in AI-integrated STEM education. The paper concludes with a discussion of opportunities, challenges and future research needs for teacher professionalization.

数字技术的迅速发展和广泛采用改变了教育部门。在这些发展中，ChatGPT等生成式人工智能（AI）工具的出现对教学和学习实践产生了相当大的影响。虽然人工智能与教育环境的整合变得越来越普遍，但特定学科的分析，特别是在STEM教育中，仍然缺乏。本文探讨了人工智能在STEM教育背景下的具体挑战和潜力。它通过探索人工智能如何改变科学学科以及这些变化如何影响教学来实现这一目标。它强调了教育工作者获得特定能力以有效地将人工智能纳入其教学实践的必要性。在现有框架（如DigCompEdu和特定学科的DiKoLAN）的基础上，本文提出了一个以人工智能为重点的框架：DiKoLAN AI。该框架将与人工智能相关的教师能力与科学教育的教学实践相结合。它还提供了一个对现有教师培训项目进行分类的结构。本文概述了DiKoLAN AI框架的开发及其内容共识验证，共有64位专家通过三个迭代周期进行验证。本文通过来自不同作者的20个案例研究展示了其实际应用，为支持人工智能集成STEM教育中的教师培训和课程设计提供了实用方法。文章最后对教师专业化的机遇、挑战和未来的研究需求进行了讨论。

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引用次数: 0

Exploring STEM educators’ perspectives on the integration of AI-enabled technologies in teaching and learning 探讨STEM教育工作者对人工智能技术在教学和学习中的整合的观点

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2025-10-24 DOI: 10.1016/j.caeo.2025.100304

Hulya Avci , Stephanie J. Lunn , Zahra Hazari

The innovation and proliferation of artificial intelligence (AI) technologies are transforming Science, Technology, Engineering, and Mathematics (STEM) disciplines and pedagogical practices, making it essential to prepare educators for this evolving landscape. This qualitative study explores STEM educators’ perceptions of how they developed proficiency with AI-enabled technologies, what motivated their adoption, and the challenges they faced in classroom integration. Using a cross-sectional survey design, we collected open-ended responses from 27 in-service secondary STEM educators in the United States and analyzed the data through the reflexive thematic analysis. Our findings revealed that STEM educators recognized AI as a cognitive scaffold that can support student understanding of complex content and a socio-affective tool that can help enhance teacher–student relationships. The participants highlighted that by streamlining routine tasks, AI allowed them to reclaim time for meaningful student interactions and responsive instruction. The participants also reported developing their AI proficiency through self-directed learning, peer support, and professional networks, driven by motivations to enhance instructional efficiency, support personalized learning, and prepare students for AI-driven futures. However, the participants emphasized several significant challenges, including lack of institutional support, the overwhelming abundance of AI tools, concerns about affordability, and students’ growing overreliance on AI systems. These insights highlight the need for structured, discipline-specific professional development for in-service educators to promote effective and human-centered integration of AI in STEM education. This study contributes to a growing body of research by centering STEM educators’ perspectives and offering novel insights into how to support their effective integration of AI technologies into STEM teaching and learning.

人工智能（AI）技术的创新和扩散正在改变科学、技术、工程和数学（STEM）学科和教学实践，因此教育工作者必须为这一不断变化的环境做好准备。这项定性研究探讨了STEM教育工作者对他们如何熟练使用人工智能技术的看法，他们采用人工智能技术的动机，以及他们在课堂整合中面临的挑战。采用横断面调查设计，我们收集了美国27名在职中学STEM教育工作者的开放式回答，并通过反身性主题分析对数据进行了分析。我们的研究结果表明，STEM教育工作者认为人工智能是一种认知支架，可以帮助学生理解复杂的内容，也是一种社会情感工具，可以帮助加强师生关系。参与者强调，通过简化日常任务，人工智能使他们能够腾出时间进行有意义的学生互动和响应式教学。参与者还报告说，他们通过自主学习、同伴支持和专业网络来提高人工智能水平，其动机是提高教学效率，支持个性化学习，并为学生为人工智能驱动的未来做好准备。然而，与会者强调了几个重大挑战，包括缺乏机构支持、人工智能工具的压倒性丰富、对负担能力的担忧以及学生对人工智能系统的日益过度依赖。这些见解强调了在职教育工作者需要结构化的、特定学科的专业发展，以促进人工智能在STEM教育中的有效和以人为本的整合。本研究通过集中STEM教育工作者的观点，并就如何支持他们有效地将人工智能技术整合到STEM教学和学习中提供新颖的见解，为越来越多的研究做出了贡献。

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引用次数: 0

Grammar and engagement in focus: Evaluating Gemini AI’s impact on an educational environment 语法和专注：评估双子座人工智能对教育环境的影响

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2025-10-18 DOI: 10.1016/j.caeo.2025.100302

Miew Luan Ng , Behnam Behforouz , Ali Al Ghaithi

The present paper aims to investigate the role of Gemini AI in improving learners' grammatical knowledge and measuring their level of engagement within the learning process using this AI tool. So, 75 Omani pre-intermediate EFL learners were equally divided into three groups, with 25 students in each group, including a control group (traditional in-class training), an experimental A group (traditional in-class and Gemini AI within the classroom), and an experimental B group (traditional in-class training and Gemini AI outside of the classroom). To collect the data, researcher-made grammar tests were designed, piloted, validated, and their reliability was established. Additionally, to measure the engagement level of students, a questionnaire was adapted from [1]. The study's findings on grammar tests showed the progress of all groups from pretest to posttest. Initially, experimental group A performed significantly better than the other two groups, and experimental group B outperformed the control group. The analysis of the delayed posttest showed consistent results in which the experimental group A significantly performed better than the other two groups. Although the other groups did not show significant enhancements, experimental group B outperformed the control group. The thorough analysis of the engagement questionnaire also revealed similar results in which the experimental group A's engagement level was significantly higher than that of the other groups, followed by better performance of experimental group B, and then the control group. The study's findings are helpful for teachers, students, and institutions.

本文旨在研究双子座人工智能在提高学习者语法知识方面的作用，并使用该人工智能工具测量他们在学习过程中的参与程度。因此，75名阿曼中级前英语学习者被平均分为三组，每组25名学生，包括对照组（传统的课堂培训），实验a组（传统的课堂培训和课堂内的Gemini AI）和实验B组（传统的课堂培训和课堂外的Gemini AI）。为了收集数据，研究者设计了自制的语法测试，进行了试点、验证，并建立了它们的可靠性。此外，为了衡量学生的参与水平，我们采用了一份来自[1]的调查问卷。该研究的语法测试结果显示了所有小组从测试前到测试后的进步。最初，实验组A的表现明显优于其他两组，实验组B的表现优于对照组。延迟后测分析结果一致，实验组A明显优于其他两组。虽然其他组没有明显的改善，但实验B组的表现优于对照组。对敬业度问卷的深入分析也发现了类似的结果，实验组A的敬业度水平显著高于其他组，其次是实验组B，最后是对照组。这项研究的发现对教师、学生和机构都有帮助。

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引用次数: 0

Patterns of mathematics problem solving and synthetic facial expressions in a personal instructing agent 解决数学问题的模式和合成面部表情的个人指导代理

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2025-10-17 DOI: 10.1016/j.caeo.2025.100301

John Lorenz Dela Cruz , Paulyn Joy Dela Cruz , Joyce Antonette Guadalupe , Jiabianca Macaraeg , Piolo Jose Montesa , Mark Paul Ramos , Rex P. Bringula , Kaoru Sumi

This study explored the patterns of mathematics problem-solving and synthetic facial expressions (SFEs) exhibited by a personal instructing agent named PIA. Toward this goal, 81 Grade 8 students participated in a three-day experiment where they were randomly assigned either to the facial (FG) or non-facial (NFG) group. The students’ interactions within the PIA were collected and stored as log files. The attributes extracted from the log files included types of mathematics problems solved (i.e., schema), status of the mathematics problems solved, difficulty levels of mathematics problems solved, and SFEs exhibited by the PIA. Lag sequential analysis (LSA) disclosed that there were similarities and differences in the sequence of math problem-solving behaviors among students. The Apriori algorithm revealed that struggling students tend to solve problems successfully, irrespective of their sex; however, struggling female students tend to solve more problems successfully than their male counterparts. Nonetheless, regardless of their levels of math competency and the version of software used, all students solved problems they were comfortable with and always started with easier problems, gradually progressing. Limitations and future research were also discussed.

本研究探讨了一个名为PIA的个人指导代理在数学问题解决和合成面部表情（sfe）方面的表现模式。为了实现这一目标，81名八年级学生参加了为期三天的实验，他们被随机分配到面部组（FG）和非面部组（NFG）。学生在PIA中的交互被收集并存储为日志文件。从日志文件中提取的属性包括解决的数学问题的类型（即模式）、解决的数学问题的状态、解决的数学问题的难度级别以及PIA显示的sfe。滞后序列分析（LSA）揭示了学生在数学问题解决行为的顺序上存在相似性和差异性。Apriori算法显示，无论性别如何，努力学习的学生都倾向于成功解决问题；然而，苦苦挣扎的女学生往往比男同学更能成功地解决问题。尽管如此，无论他们的数学能力水平和使用的软件版本如何，所有的学生都解决了他们熟悉的问题，并且总是从容易的问题开始，逐渐进步。讨论了研究的局限性和未来的研究方向。

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引用次数: 0

Teacher, peer, or AI? Comparing effects of feedback sources in higher education 老师、同伴还是人工智能？高等教育中反馈来源的效果比较

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2025-10-17 DOI: 10.1016/j.caeo.2025.100300

Joshua Weidlich , Flurin Gotsch , Kai Schudel , Claudia Marusic-Würscher , Jennifer Mazzarella , Hannah Bolten , Dario Bütler , Simon Luger , Bettina Wohlfender , Katharina Maag Merki

With the emergence of Large Language Models (LLMs), AI-generated feedback is gaining traction as a scalable feedback source for higher education. To qualify as viable alternatives for higher education classrooms, its relative effectiveness compared to teacher feedback and peer feedback needs to be better understood. To this end, a randomized field experiment (N = 90) compared the effects of three feedback sources—teacher, peer, and LLM—on students’ feedback perceptions and their achievement. To eliminate potential confounds, we (a) controlled for learning gains that may result from students giving feedback to their peers and (b) blinded feedback sources from feedback recipients. Results showed that students rated teacher feedback as less fair and harder to accept than peer and LLM feedback. Students receiving teacher feedback also indicated less willingness to revise their work based on the feedback. Conversely, teacher feedback produced the strongest improvements in scientific argumentation and formal quality of students’ work. Here, LLM feedback yielded the smallest improvement overall. Lastly, feedback literacy and intrinsic motivation partly moderated feedback effects on perceptions and achievement outcomes. For example, students with more productive attitudes toward feedback achieved higher argumentation quality after receiving teacher feedback than their peers. Findings indicate that the impact of feedback on both student perceptions and performance depends on the interplay between the feedback source and learner dispositions; deliberately aligning these factors could therefore amplify the benefits of feedback interventions. Future research should explore hybrid and adaptive feedback models that integrate human and AI input.

随着大型语言模型（llm）的出现，人工智能生成的反馈作为高等教育的可扩展反馈来源正在获得关注。为了使其成为高等教育课堂的可行替代方案，需要更好地了解其与教师反馈和同伴反馈相比的相对有效性。为此，一项随机现场实验（N = 90）比较了三种反馈来源——教师、同伴和法学硕士——对学生反馈感知和他们的成就的影响。为了消除潜在的混淆，我们(a)控制了学生向同龄人提供反馈可能带来的学习收益，(b)对反馈接受者的反馈来源进行了盲化。结果显示，学生认为教师的反馈比同伴和法学硕士的反馈更不公平，更难以接受。收到老师反馈的学生也表示不太愿意根据反馈修改他们的作业。相反，教师反馈在科学论证和学生作业的正式质量方面产生了最大的改善。在这里，LLM反馈产生了最小的整体改进。最后，反馈素养和内在动机在一定程度上调节了反馈对认知和成就结果的影响。例如，对反馈持更积极态度的学生在接受教师反馈后的论证质量高于同龄人。研究结果表明，反馈对学生认知和成绩的影响取决于反馈来源和学习者倾向之间的相互作用；因此，有意地调整这些因素可以放大反馈干预的好处。未来的研究应该探索混合和自适应反馈模型，将人类和人工智能的输入结合起来。

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引用次数: 0

Impact of virtual reality learning environments on skills development in students with ASD 虚拟现实学习环境对ASD学生技能发展的影响

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2025-10-02 DOI: 10.1016/j.caeo.2025.100298

Rui Manuel Silva , Paulo Martins , Tânia Rocha

Background

Students with Autism Spectrum Disorder (ASD) often face significant challenges in traditional educational environments, including difficulties in social interaction, engagement, and adapting to standard learning methods. These barriers can hinder their academic and personal development, highlighting the need for more inclusive and adaptive educational solutions.

Objective

This study investigated whether immersive VR-based STEM learning environments can support the cognitive, social and behavioural development of pupils with ASD. We evaluated usability and accessibility needs, validated the artefact through expert consensus, and measured pre–post changes using established standardised instruments.

Methodology

The research followed the Design Science Research (DSR) approach within STEM (Science, Technology, Engineering, and Mathematics) to develop VR-based learning experiences adapted to the needs of students with ASD. The Delphi method involved experts in defining best practices and educational strategies, helping to ensure that the proposed solutions were appropriate and aligned with student characteristics. The study included a control and an experimental group, both composed of students with ASD and typically developing students, assessing the impact of VR on learning and socialisation.

Results

The findings suggest that VR-based learning environments may support improvements in cognitive, behavioural and social skills, although causal inference is limited by the small sample size and absence of randomisation.

Conclusions

This study provides preliminary evidence that VR-based learning environments may help address educational barriers for students with ASD by offering structured, engaging and adaptable environments that could support inclusion and development.

自闭症谱系障碍（ASD）学生在传统的教育环境中经常面临重大挑战，包括社会互动、参与和适应标准学习方法方面的困难。这些障碍会阻碍他们的学业和个人发展，因此需要更具包容性和适应性的教育解决方案。目的探讨沉浸式vr STEM学习环境对ASD患儿认知、社交和行为发展的促进作用。我们评估可用性和可访问性需求，通过专家共识验证工件，并使用已建立的标准化工具测量前后变化。该研究遵循STEM（科学、技术、工程和数学）中的设计科学研究（DSR）方法，开发基于vr的学习体验，以适应自闭症学生的需求。德尔菲法让专家来定义最佳实践和教育策略，帮助确保提出的解决方案是适当的，并与学生的特点保持一致。该研究包括一个对照组和一个实验组，都由自闭症学生和正常发展的学生组成，评估虚拟现实对学习和社交的影响。研究结果表明，基于虚拟现实的学习环境可能支持认知、行为和社交技能的提高，尽管因果推理受到小样本量和缺乏随机化的限制。本研究提供了初步证据，表明基于虚拟现实的学习环境可以通过提供结构化、引人入胜和适应性强的环境来支持包容和发展，从而有助于解决自闭症学生的教育障碍。

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