Computers and Education Open最新文献_第3页

Measuring Teachers' competencies for AI integration: Development and validation of the AI-TPACK in vocational education 衡量教师整合人工智能的能力：职业教育中AI- tpack的开发与验证

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2025-12-01 DOI: 10.1016/j.caeo.2025.100319

Andri Setiyawan , Soeharto Soeharto , Tommy Tanu Wijaya , Lilla Korenova , Zsolt Lavicza

Integrating artificial intelligence (AI) into education necessitates teachers acquiring competencies aligned with technological advancements, especially within vocational contexts. This study aimed to adapt and validate a concise self-report instrument, the AI-integrated Technological Pedagogical Content Knowledge (AI-TPACK) scale, grounded in the TPACK framework, to measure vocational teachers' competencies in integrating AI into instructional practices. A total of 460 pre-service and in-service vocational teachers from Indonesia participated. The adapted instrument encompasses seven constructs, including AI Pedagogical Knowledge, AI Content Knowledge, AI Technological Knowledge, and their intersections, culminating in a comprehensive AI-TPACK construct. Confirmatory factor analysis confirmed strong model fit, and convergent and discriminant validity, internal consistency, and composite reliability met acceptable thresholds. Structural equation modeling revealed significant predictive relationships among constructs, while measurement invariance tests supported its suitability across pre-service and in-service teachers. These findings affirm the adapted AI-TPACK scale as a reliable and valid tool for assessing AI-integrated pedagogical competencies specifically within vocational education contexts.

将人工智能（AI）融入教育需要教师获得与技术进步相匹配的能力，特别是在职业背景下。本研究旨在调整和验证一种简洁的自我报告工具，即基于TPACK框架的AI集成技术教学内容知识（AI-TPACK）量表，以衡量职业教师将AI整合到教学实践中的能力。来自印度尼西亚的460名职前和在职职业教师参加了此次调查。调整后的工具包括七个结构，包括人工智能教学知识、人工智能内容知识、人工智能技术知识及其交叉点，最终形成一个全面的人工智能tpack结构。验证性因子分析证实模型拟合强，收敛效度和判别效度、内部一致性和复合信度均达到可接受的阈值。结构方程模型揭示了构式之间显著的预测关系，而测量不变性检验支持其在职前和在职教师中的适用性。这些发现证实了适应性AI-TPACK量表是评估职业教育背景下ai集成教学能力的可靠有效工具。

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引用次数: 0

Effects of an AI-based reading progress tool on third-grade EFL learners’ oral reading fluency 基于人工智能的阅读进度工具对三年级英语学习者口语阅读流畅性的影响

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2025-12-01 Epub Date: 2025-08-26 DOI: 10.1016/j.caeo.2025.100283

Reem Mahdi Al-Bogami , Nsreen Abdulhamid Alahmadi

Oral reading fluency (ORF) is a critical skill for young English as a foreign language (EFL) learners. Researchers have explored various traditional and digital interventions to enhance EFL learners’ ORF. However, studies integrating cutting-edge technologies, such as artificial intelligence (AI) tools, in EFL remain limited. This mixed-methods quasi-experimental study aimed to examine the effectiveness of Reading Progress, an AI-based tool on the Microsoft Teams platform (MTRP), in improving the ORF, regarding accuracy, speed, and prosody, of third-grade EFL learners in Saudi Arabia. Additionally, it investigated the perspectives of the experimental group’s parents regarding the tool’s usage. Participants included 56 third-grade EFL learners (boys and girls) from a public elementary school in Jeddah, divided into two groups. The experimental group (n = 28) utilised MTRP as an intervention, while the control group (n = 28) engaged in traditional paper-based assignments. Pre- and post-tests and semi-structured interviews were conducted to gather data. The quantitative data were analysed using SPSS, while the qualitative data were transcribed, translated, and then analysed through NVivo. The results indicated that the experimental group, achieved significantly higher scores in ORF skills after utilizing MTRP compared to the control group. The experimental group’s parents reported positive feedback, expressing satisfaction with the tool’s impact on their children’s ORF. However, the children were initially challenged due to time constraints and lengthy texts. Nevertheless, they believed that consistent practice and high goal-setting enabled them to overcome obstacles. The findings are expected to provide valuable insights for EFL educators, policymakers, and researchers.

口语阅读流畅性（ORF）是年轻英语学习者的一项重要技能。研究人员已经探索了各种传统和数字干预措施来提高英语学习者的ORF。然而，将人工智能（AI）工具等前沿技术整合到外语教学中的研究仍然有限。这项混合方法的准实验研究旨在检验微软团队平台（MTRP）上基于人工智能的阅读进步工具在提高沙特阿拉伯三年级英语学习者在准确性、速度和韵律方面的ORF方面的有效性。此外，它还调查了实验组父母对该工具使用的看法。参与者包括来自吉达一所公立小学的56名三年级英语学习者（男孩和女孩），他们被分为两组。实验组（n = 28）采用MTRP作为干预手段，而对照组（n = 28）采用传统的纸质作业。进行了前后测试和半结构化访谈以收集数据。定量数据采用SPSS软件分析，定性数据采用转录、翻译、NVivo软件分析。结果表明，实验组在使用MTRP后的ORF技能得分显著高于对照组。实验组的父母报告了积极的反馈，对该工具对孩子的ORF的影响表示满意。然而，由于时间限制和冗长的文本，孩子们最初受到了挑战。然而，他们认为，坚持不懈的实践和高目标的设定使他们能够克服障碍。研究结果有望为英语教育者、政策制定者和研究人员提供有价值的见解。

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引用次数: 0

AI-driven gamified speech training for primary students: framework and evaluation 基于ai的小学生游戏化语音训练：框架与评价

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2025-12-01 Epub Date: 2025-11-17 DOI: 10.1016/j.caeo.2025.100312

Xing Sun , Zi-Xiang Xu , Ling-Chen Meng , Ding-Nan Shi

Traditional public speaking education often suffers from limited learner engagement, delayed formative feedback, and a lack of interactive and adaptive training environments. This study proposes an AI-driven gamified speech learning framework (AI-GSLF), which combines real-time feedback technologies with motivational game design principles to address these issues. Based on this framework, a serious game—Strongest Speech Streamer—was developed using the Godot engine. The system integrates automatic speech recognition, sentiment analysis, and a novel speech rate detection algorithm to provide immediate feedback, helping learners adjust pacing, reduce anxiety, and enhance fluency during practice. A true experimental design was employed, involving 57 primary school students randomly assigned to either the experimental group using the gamified system or a control group following traditional methods over one month. Quantitative results showed that the experimental group demonstrated statistically significant improvements in motivation, confidence, speech accuracy, and delivery fluency. To our knowledge, few prior studies have integrated real-time AI feedback with systematic gamification for primary-level formal speech training. Findings support the potential of AI-GSLF as an effective, scalable approach to enhancing student performance and engagement in public speaking education.

传统的公共演讲教育往往存在学习者参与度有限、形成性反馈滞后、缺乏互动性和适应性训练环境等问题。本研究提出了一个人工智能驱动的游戏化语音学习框架（AI-GSLF），它将实时反馈技术与动机游戏设计原则相结合，以解决这些问题。基于这个框架，我们使用Godot引擎开发了一款严肃的游戏——《最强的语音流》。该系统集成了自动语音识别、情感分析和一种新颖的语音率检测算法，提供即时反馈，帮助学习者在练习中调整节奏，减少焦虑，提高流利度。采用真正的实验设计，在一个月的时间里，57名小学生被随机分配到使用游戏化系统的实验组和使用传统方法的对照组。定量结果显示，实验组在动机、信心、语言准确性和表达流畅性方面表现出统计学上显著的改善。据我们所知，之前很少有研究将实时人工智能反馈与系统游戏化相结合，用于初级水平的正式语音训练。研究结果支持AI-GSLF作为一种有效的、可扩展的方法来提高学生在公共演讲教育中的表现和参与度的潜力。

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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 : 2025-12-01 Epub 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

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 : 2025-12-01 Epub 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

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 : 2025-12-01 Epub 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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引用次数: 0

Fostering Intelligent-TPACK through AI-assistance: A multi-method study in pre-service teacher education 通过人工智能辅助培养智能tpack：职前教师教育的多方法研究

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2025-12-01 Epub Date: 2025-11-24 DOI: 10.1016/j.caeo.2025.100314

Sabine Seufert, Philipp Hartmann, Lukas Spirgi

As generative artificial intelligence (GenAI) rapidly becomes a structural element of education, teacher preparation programs face urgent challenges in developing both pedagogical competence and ethical awareness among future educators. This exploratory study investigates how Swiss pre-service teachers in business education conceptualize, design, and evaluate GenAI-supported instruction through the lens of the Intelligent-TPACK framework, an expanded model integrating technological, pedagogical, content, and ethical knowledge. Twelve master-level pre-service teachers participated in a mixed-method study that combined self-report surveys, group-based instructional design artefacts, and AI-driven prompt analysis using OpenAI o3. Results show that participants reported high confidence in technological and pedagogical AI knowledge, but they exhibited weaker confidence and reliability in ethical knowledge, particularly regarding transparency and accountability. All chatbot designs address the "Active" rather than "Interactive" level of the ICAP hierarchy. The AI-based analysis further highlighted gaps in Socratic questioning and metacognitive prompting, underscoring limited opportunities for reflection and co-construction. These findings reveal a need to move beyond surface-level tool familiarity towards integrating ethical reflection and explicit design-in-action practices within teacher education. Ultimately, the study underscores the importance of cultivating pre-service teachers as co-designers of pedagogical experiences who are equipped to navigate both the technical and ethical complexities of AI-mediated classrooms.

随着生成式人工智能（GenAI）迅速成为教育的结构要素，教师培训计划在培养未来教育者的教学能力和道德意识方面面临着紧迫的挑战。本探索性研究通过智能tpack框架（一个集成了技术、教学、内容和伦理知识的扩展模型）的视角，调查了瑞士商业教育的职前教师如何概念化、设计和评估genai支持的教学。12名大师级职前教师参与了一项混合方法研究，该研究结合了自我报告调查、基于小组的教学设计工件和使用OpenAI o3的人工智能驱动的提示分析。结果显示，参与者对人工智能的技术和教学知识有很高的信心，但他们对伦理知识的信心和可靠性较弱，特别是在透明度和问责制方面。所有的聊天机器人设计都针对ICAP层次结构中的“活动”而不是“交互”级别。基于人工智能的分析进一步突出了苏格拉底式提问和元认知提示的差距，强调了反思和共同构建的机会有限。这些发现表明，有必要超越表面上对工具的熟悉，在教师教育中整合道德反思和明确的行动设计实践。最后，该研究强调了培养职前教师作为教学经验的共同设计师的重要性，他们有能力应对人工智能介导的课堂的技术和道德复杂性。

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引用次数: 0

Revisiting generalizability theory in the age of artificial intelligence: Implications for empirical educational research 人工智能时代对概括性理论的重新审视：对实证教育研究的启示

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2025-12-01 Epub Date: 2025-07-31 DOI: 10.1016/j.caeo.2025.100278

Peer-Benedikt Degen

The rise of AI in education presents both transformative opportunities and methodological challenges. This paper revisits Generalizability Theory (G-Theory) as a robust framework to assess the reliability and fairness of AI-driven tools across diverse educational contexts. It is argued that G-Theory’s variance decomposition logic is uniquely suited to disentangle the multifaceted sources of error introduced by evolving AI systems, user diversity, and complex learning environments. Through empirical use cases it is illustrated how G-Theory can support the design of equitable, scalable, and context-sensitive AI applications. We further A G-Theory Readiness Checklist to guide researchers in designing studies with AI as a methodological facet is proposed. Finally, conceptual, technical, ethical, pedagogical, and regulatory limitations and implications for study designs are highlighted. The paper concludes with suggestions for future research.

人工智能在教育领域的兴起既带来了变革机遇，也带来了方法论上的挑战。本文将概括性理论（g理论）作为一个强大的框架来评估人工智能驱动的工具在不同教育背景下的可靠性和公平性。有人认为，g理论的方差分解逻辑非常适合于解决由不断发展的人工智能系统、用户多样性和复杂的学习环境引入的多方面的错误来源。通过实证用例，说明了G-Theory如何支持公平、可扩展和上下文敏感的人工智能应用程序的设计。我们进一步提出了一个g理论准备检查表，以指导研究人员设计人工智能作为方法学方面的研究。最后，强调了研究设计的概念、技术、伦理、教学和监管方面的限制和影响。最后，对今后的研究提出了建议。

引用次数: 0

Understanding pre-service teachers’ needs for integrating AI-based tools in instruction through intelligent TPACK framework 了解职前教师通过智能TPACK框架将基于人工智能的工具整合到教学中的需求

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2025-12-01 Epub Date: 2025-11-29 DOI: 10.1016/j.caeo.2025.100317

Xiaolu Rui, Ismail Celik, Justin Edwards

As Artificial Intelligence in Education (AIEd) transforms teaching and learning with accompanying technological, pedagogical, and ethical challenges, understanding pre-service teachers’ AI perceptions and ensuring their adequate preparation is crucial for effective AIEd implementation. While previous research has examined pre-service teachers’ AI competencies and perspectives using either quantitative or qualitative methods, a critical gap remains in understanding their specific needs for AI-based instructional tools and relevant training through an integrated theoretical lens. This mixed-methods study addresses this gap by investigating 49 pre-service teachers’ needs under the Intelligent-TPACK framework. Statistical and thematic analyses revealed pre-service teachers’ ambivalent attitudes toward AI-based tools and associated training. Participants expressed needs for technological, pedagogical, content, and ethical knowledge related to AI in education, along with concerns about AI-based tools. While individual requirements for AI-relevant knowledge varied, participants consistently demonstrated high demand for training specifically in AI ethics.

Overall, this study revealed pre-service teachers’ unpreparedness regarding AIEd, furtherly uncovering critical gaps between their knowledge demands and existing teacher training programs. The findings call for an integrated approach combining AI-technical expertise with hands-on pedagogical practices within teacher education programs. This research contributes to the field by validating the Intelligent TPACK framework and providing recommendations for educational program designers to create effective training for AI-based tools.

随着教育中的人工智能（AIEd）改变了教学方式，并带来了技术、教学和道德方面的挑战，了解职前教师对人工智能的看法，并确保他们做好充分的准备，对于有效实施AIEd至关重要。虽然之前的研究使用定量或定性方法检查了职前教师的人工智能能力和观点，但在通过综合理论视角了解他们对基于人工智能的教学工具和相关培训的具体需求方面，仍然存在一个关键差距。这项混合方法研究通过在智能- tpack框架下调查49名职前教师的需求来解决这一差距。统计和专题分析揭示了职前教师对基于人工智能的工具和相关培训的矛盾态度。与会者表达了对教育中与人工智能相关的技术、教学、内容和伦理知识的需求，以及对基于人工智能的工具的担忧。虽然每个人对人工智能相关知识的要求各不相同，但参与者始终对人工智能伦理方面的培训表现出很高的要求。总体而言，本研究揭示了职前教师对AIEd的准备不足，进一步揭示了他们的知识需求与现有教师培训计划之间的严重差距。研究结果呼吁将人工智能技术专长与教师教育项目中的动手教学实践相结合。这项研究通过验证智能TPACK框架，并为教育计划设计者提供建议，为基于人工智能的工具创建有效的培训，从而对该领域做出了贡献。

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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 : 2025-12-01 Epub 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