Computers and Education Open最新文献_第2页

Perceived utility moderates motivational intervention effects in learning to teach responsibly with GenAI 感知效用调节了GenAI负责任教学的动机干预效果

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2025-12-13 DOI: 10.1016/j.caeo.2025.100324

Jana Boos , Thérése Eder , Andreas Lachner

Making responsible decisions when integrating artificial intelligence (AI) into teaching requires educators to simultaneously consider technological, pedagogical, and ethical knowledge. However, pre-service teachers often lack this integrated understanding, limiting their ability to reason responsibly in AI-supported educational contexts. Prior research has shown that motivational interventions, particularly those enhancing the utility-value of learning content, can support knowledge integration processes during learning. However, their potential effects on knowledge acquisition remain limited. In this experimental field study (N = 158), we investigated the effects of a scaffolded utility-value intervention on pre-service teachers’ knowledge integration and knowledge acquisition. Additionally, we explored potential aptitude-treatment interaction effects, as utility-value interventions are regarded as especially beneficial for learners with initial low perceived utility-value. Using a one-factorial experimental design with three conditions, participants were assigned to either a utility-value intervention without scaffolds, a scaffolded utility-value intervention, or a control condition before engaging with a digital learning environment that addressed technical, pedagogical, and ethical issues related to AI use in teaching. Overall, the analyses revealed no general effects of the interventions. However, exploratory moderation analyses suggested that the utility-value intervention was detrimental to the knowledge integration of pre-service teachers with high initial perceived utility-value. These findings highlight the importance of tailoring motivational support to learners’ individual prerequisites to foster the development of professional knowledge for the responsible integration of AI in teaching.

在将人工智能（AI）整合到教学中时，要做出负责任的决定，教育工作者需要同时考虑技术、教学和伦理知识。然而，职前教师往往缺乏这种综合理解，限制了他们在人工智能支持的教育环境中进行负责任推理的能力。已有研究表明，动机干预，特别是那些提高学习内容效用价值的干预，可以支持学习过程中的知识整合过程。然而，它们对知识获取的潜在影响仍然有限。在本实验研究中（N = 158），我们调查了脚手架效用价值干预对职前教师知识整合和知识获取的影响。此外，我们还探讨了潜在的能力倾向-治疗互动效应，因为效用价值干预被认为对最初感知效用价值较低的学习者特别有益。使用三种条件的单因子实验设计，参与者被分配到没有支架的效用价值干预，支架效用价值干预或控制条件，然后参与数字学习环境，解决与人工智能在教学中使用相关的技术，教学和伦理问题。总的来说，分析显示干预措施没有一般效果。然而，探索性调节分析表明，功利价值干预不利于初始感知功利价值高的职前教师的知识整合。这些发现强调了根据学习者的个人先决条件定制动机支持的重要性，以促进专业知识的发展，从而负责任地将人工智能整合到教学中。

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引用次数: 0

Pre-service teachers’ agency during their interactions with generative AI while designing for learning – a process view on Intelligent-TPACK 职前教师在设计学习过程中与生成式人工智能互动中的能动性——基于Intelligent-TPACK的过程观

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2025-12-13 DOI: 10.1016/j.caeo.2025.100325

Kristina Krushinskaia, Jan Elen, Annelies Raes

Agency is considered a core societal value, yet it remains unknown how human agency is affected in human-AI interactions. This study is among the first to use process-level indicators to examine teacher agency during interactions with generative AI (GenAI) in the context of instructional design. While instructional design is regarded as a key teacher role, research shows that teachers often avoid this cognitively demanding task. Today, GenAI offers new opportunities to support teachers in the design process. One approach involves using expert bots customized with one instructional design model, which scaffold the design process and offer ready-made components (e.g., learning goals). However, it is unclear how interactions with an expert bot versus basic ChatGPT influence teacher agency. In this research, we analyzed process data and qualitative feedback from an experiment involving 78 pre-service teachers randomly assigned to one of two conditions: (1) the expert bot condition, where participants interacted with a custom GenAI bot; and (2) the ChatGPT condition, where participants could request any type of support. Three indicators of agency were examined: (1) length of interaction, (2) ownership, and (3) collaborative problem-solving behaviors. The first two indicators suggested higher agency in the expert bot condition, as participants interacted longer and used significantly more self-generated prompts. However, the third indicator revealed reduced agency in both conditions. Instead of providing meaningful feedback on the bot’s suggestions, participants in the expert bot condition often agreed with its output. This suggests that the material contexts of ChatGPT and the expert bot both have unique constraints on teachers’ agency.

能动性被认为是一种核心的社会价值，但在人类与人工智能的互动中，人类的能动性是如何受到影响的仍不得而知。本研究是第一个使用过程级指标来检验教师在教学设计背景下与生成式人工智能（GenAI）互动时的能动性的研究。虽然教学设计被认为是教师的关键角色，但研究表明，教师往往会回避这项认知要求很高的任务。今天，GenAI提供了在设计过程中支持教师的新机会。其中一种方法是使用由一个教学设计模型定制的专家机器人，它支撑设计过程并提供现成的组件（例如，学习目标）。然而，目前尚不清楚与专家机器人的互动与基本ChatGPT的互动如何影响教师代理。在这项研究中，我们分析了78名职前教师的过程数据和定性反馈，这些教师被随机分配到两个条件之一：(1)专家机器人条件，参与者与定制的GenAI机器人进行互动；(2) ChatGPT条件，参与者可以要求任何类型的支持。研究了代理行为的三个指标：(1)互动时长；(2)所有权；(3)协作解决问题行为。前两个指标表明，在专家机器人条件下，参与者的能动性更高，因为他们互动的时间更长，使用的自我提示也明显更多。然而，第三个指标显示，在两种情况下，能动性都有所下降。专家机器人状态下的参与者通常同意机器人的输出，而不是对机器人的建议提供有意义的反馈。这表明ChatGPT和专家机器人的材料语境对教师的代理都有独特的约束。

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引用次数: 0

Unpacking ethics-domain of intelligent-TPACK scale in relation to in-service teachers’ trust and distrust 解开智能tpack量表伦理域与在职教师信任与不信任的关系

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2025-12-04 DOI: 10.1016/j.caeo.2025.100321

Ceren Ocak , Secil Caskurlu

This multiple case study explores how in-service teachers’ technical knowledge of artificial intelligence (AI) relates to their trust in using AI in educational contexts, and how this trust is shaped by their ethical perceptions. Framed within the Ethics dimension of the Intelligent-TPACK framework (Celik, 2023), this study focuses on four ethical constructs (i.e., transparency, fairness, accountability, and inclusiveness) as indicators of ethical AI and examines how teachers’ trust plays out in relation to these constructs. Data were drawn from written reflections of seven in-service teachers teaching across K-12 levels. These reflections were collected following their participation in a two-week, AI-focused online learning module designed to foster both technical and ethical understanding as part of a graduate-level computer science and instructional technology course. Findings suggest that without solid foundational technical knowledge, teachers often struggle to recognize how human decisions shape AI systems and outcomes. Moreover, the ethical constructs were found to be deeply interconnected and dynamic, with some constructs (e.g., fairness and accountability) often emerging together. In addition, while teachers frequently referred to fairness, inclusiveness, and accountability concerns, other ethical constructs such as transparency and human accountability in decision-making were not addressed nearly as often, emerging as areas that need greater attention in teacher education initiatives. Overall, these insights highlight the ways teachers’ classroom priorities influence how they interpret and engage with ethical considerations in AI, offering important implications for defining and operationalizing the Ethics dimension of Intelligent-TPACK in teacher education.

这个多案例研究探讨了在职教师对人工智能（AI）的技术知识与他们在教育环境中使用人工智能的信任之间的关系，以及他们的道德观念如何塑造这种信任。在智能- tpack框架的伦理维度框架内（Celik, 2023），本研究将重点放在四个伦理结构（即透明度、公平性、问责制和包容性）作为道德人工智能的指标，并研究教师信任如何与这些结构相关。数据来自7位在职教师在K-12阶段的书面反思。这些反思是在他们参加了为期两周的以人工智能为重点的在线学习模块后收集的，该模块旨在培养对技术和伦理的理解，作为研究生水平的计算机科学和教学技术课程的一部分。研究结果表明，如果没有扎实的基础技术知识，教师往往很难认识到人类的决策如何影响人工智能系统和结果。此外，我们还发现这些伦理结构是相互联系的，并且是动态的，其中一些结构（例如，公平和问责制）经常同时出现。此外，虽然教师经常提到公平、包容和问责问题，但其他道德建设，如决策中的透明度和人的问责制，几乎没有得到同样频繁的解决，成为教师教育倡议中需要更多关注的领域。总体而言，这些见解突出了教师的课堂优先级如何影响他们如何解释和参与人工智能中的伦理考虑，为教师教育中智能- tpack的伦理维度的定义和实施提供了重要启示。

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引用次数: 0

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

How do teachers use digital technology in classrooms? Characterizing mathematics teachers’ teaching practices 教师如何在课堂上使用数字技术？数学教师教学实践的特征

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2025-12-01 DOI: 10.1016/j.caeo.2025.100310

Alina Kadluba , Frank Reinhold , Andreas Obersteiner

Digital technology has the potential to improve student learning, especially in mathematics. However, the extent to which students benefit from technology largely depends on how it is integrated into instruction. Nevertheless, little is known about how teachers implement digital technology in the classroom. This study examines how 14 in-service teachers use a digital tool for teaching fractions in 15–20 lessons. We used classroom observation to identify teaching practices, and cluster analysis to group them into teaching approaches. Four main approaches of teaching with digital technology emerged: (i) Practicing, where teachers used various features of the tool to make students practice the learning content, (ii) Presenting, where teachers used the tool predominantly to present the learning content but did not utilize many features, (iii) Motivating Students, where the tool and especially its game-like features were used as a reward for students, and (iv) Low-Guidance, where the teacher did not provide guidance for students how to use the tool. Teaching practices varied between these approaches in several dimensions, for example in how teachers explained the learning content, how they implemented rules, and which learning materials they selected. The findings highlight large variation in integrating even one and the same digital tool in the mathematics classroom. We identified teaching approaches for technology-enhanced learning settings that can inform future research investigating how technology implementation influences student learning. These approaches may also guide the design of professional development programs that support teachers in developing effective strategies for integrating digital technology into their instruction.

数字技术有潜力改善学生的学习，特别是在数学方面。然而，学生从技术中受益的程度在很大程度上取决于它如何融入教学。然而，关于教师如何在课堂上实施数字技术，人们知之甚少。本研究考察了14名在职教师如何使用数字工具在15-20节课中教授分数。我们使用课堂观察来识别教学实践，并使用聚类分析将其归类为教学方法。利用数字技术进行教学的主要方法有四种：(i)练习，教师使用工具的各种功能让学生练习学习内容；（ii）呈现，教师主要使用工具来展示学习内容，但没有使用很多功能；（iii）激励学生，工具，特别是它的游戏式功能被用作对学生的奖励；（iv）低指导，教师没有为学生如何使用工具提供指导。这些方法的教学实践在几个方面有所不同，例如教师如何解释学习内容，他们如何实施规则，以及他们选择哪些学习材料。研究结果强调了在数学课堂上整合同一种数字工具的巨大差异。我们确定了技术增强学习环境的教学方法，可以为未来调查技术实施如何影响学生学习的研究提供信息。这些方法也可以指导专业发展计划的设计，支持教师制定有效的策略，将数字技术融入他们的教学。

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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 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

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 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

Learning in hybrid times: Comparing student experiences in traditional and GenAI-supported instruction 混合时代的学习：比较学生在传统和人工智能支持教学中的经验

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2025-12-01 DOI: 10.1016/j.caeo.2025.100313

Meital Amzalag , Dizza Beimel , Rina Zviel-Girshin

The emergence of generative artificial intelligence (GenAI) tools, such as ChatGPT, is reshaping the landscape of higher education by introducing new opportunities for learner autonomy, flexibility, and engagement. While extensive research has explored GenAI’s technical capabilities and ethical implications, limited attention has been paid to students’ subjective learning experiences with AI-supported instruction. This study investigates how undergraduate students perceive and experience two instructional modes in a database management course: traditional lecturer-led instruction and GenAI-supported self-regulated learning. Sixty-eight second-year engineering students participated in the study, providing qualitative insights through open-ended survey responses. The findings reveal key cognitive, emotional, and strategic differences between the two approaches: traditional instruction fostered structure, immediate feedback, and emotional reassurance, while GenAI-supported learning promoted autonomy and exploration but raised concerns regarding reliability and critical thinking. Importantly, students did not view these modes as mutually exclusive, but rather as complementary components of a broader “learning puzzle,” balancing the stability of face-to-face instruction with the adaptability of GenAI to support diverse learning needs. This study contributes to a deeper understanding of hybrid learning environments and underscores the importance of nuanced, learner-centered integration of AI technologies in higher education.

ChatGPT等生成式人工智能（GenAI）工具的出现，通过为学习者的自主性、灵活性和参与度带来新的机会，正在重塑高等教育的格局。虽然广泛的研究探索了GenAI的技术能力和伦理影响，但对学生在人工智能支持教学中的主观学习体验的关注有限。本研究调查了本科生如何感知和体验数据库管理课程的两种教学模式：传统的讲师主导教学和genai支持的自主学习。68名二年级工程专业的学生参与了这项研究，通过开放式的调查回答提供定性的见解。研究结果揭示了两种方法在认知、情感和策略上的关键差异：传统教学促进结构、即时反馈和情感安慰，而基因人工智能支持的学习促进自主和探索，但增加了对可靠性和批判性思维的担忧。重要的是，学生们并不认为这些模式是相互排斥的，而是将其视为更广泛的“学习难题”的互补组成部分，在面对面教学的稳定性和GenAI的适应性之间取得平衡，以支持不同的学习需求。这项研究有助于更深入地理解混合学习环境，并强调了在高等教育中细致入微、以学习者为中心的人工智能技术集成的重要性。

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引用次数: 0

AI literacy, educational level, and parenting self-efficacy of children’s education among parents of primary school students 小学生家长人工智能素养、受教育程度与子女教育的父母自我效能感

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2025-12-01 DOI: 10.1016/j.caeo.2025.100318

Jiaqi Guo , Tammy Sheung-Ting Law , Shen Qiao , Susanna Siu-sze Yeung

Parenting self-efficacy, representing parents’ confidence in their capability to effectively handle parenting responsibilities and obstacles, is an important determinant of parenting quality. Parents’ educational level is known to be an important predictor of parenting self-efficacy. Nowadays, children are exposed to Artificial Intelligence (AI) from an early age, especially in their learning process. Parents’ AI literacy emerges as an important factor that could contribute to parenting self-efficacy of children’s education. However, no previous studies have investigated how parents’ AI literacy is related to parenting self-efficacy. The quantitative study examined the associations among parents’ AI literacy, educational level, and parenting self-efficacy of children’s education. Data were collected from 160 parents of primary school students in Hong Kong through online surveys. Results showed significant relationships among parents’ AI literacy, educational level, and parenting self-efficacy of children’s education. The association between parents’ educational level and parenting self-efficacy was partially mediated by the subfactors of AI literacy. The study extended the literature by investigating AI literacy from the parents’ side and highlighted the importance of parents’ AI literacy in parenting regarding children’s education. It had implications for practitioners and policymakers to develop intervention programs to help parents improve AI literacy.

育儿自我效能感是衡量育儿质量的一个重要决定因素，它代表父母对自己有效处理育儿责任和障碍的能力的信心。众所周知，父母的教育水平是父母自我效能感的重要预测指标。如今，孩子们从小就接触到人工智能（AI），尤其是在学习过程中。父母的人工智能素养成为影响子女教育父母自我效能感的重要因素。然而，之前没有研究调查过父母的人工智能素养与父母自我效能感之间的关系。定量研究考察了父母的人工智能素养、教育水平和子女教育的父母自我效能感之间的关系。通过网上调查，收集了160名香港小学生家长的数据。结果显示，父母的人工智能素养、受教育程度和子女教育的父母自我效能感之间存在显著的相关关系。父母受教育水平与父母自我效能感之间的关系被人工智能素养的子因素部分中介。该研究扩展了文献，从父母的角度调查了人工智能素养，并强调了父母的人工智能素养在养育子女教育中的重要性。这对从业者和政策制定者制定干预计划以帮助父母提高人工智能素养具有重要意义。

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引用次数: 0

Enhancing enthusiasm for STEM education with AI: Domain-specific chatbot as personalized learning assistant 用人工智能提高STEM教育的热情：特定领域的聊天机器人作为个性化学习助手

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2025-12-01 DOI: 10.1016/j.caeo.2025.100315

Cilia Ricarda Rücker, Sebastian Becker-Genschow

Generative AI has been increasingly integrated into educational settings for its potential to facilitate personalized learning. However, evidence-based implementation concepts remain essential to realize its pedagogical value. Therefore, this study investigates the educational efficacy of ADA, a domain-specific AI chatbot customized for secondary mathematics education, in enhancing learning outcomes and student engagement. A cluster-randomized controlled study was conducted with 195 ninth-grade students from German secondary schools in authentic classroom settings. The objective of the study was to compare ADA's personalized support against conventional differentiation materials. The intervention targeted the Heron method for estimating square roots in a single-lesson intervention with a pre-post assessment. As one of the few cluster-randomized controlled studies on custom AI chatbots in secondary mathematics education, this investigation assessed both affective and cognitive aspects of learning simultaneously to provide a comprehensive research picture. The findings indicated a high degree of student acceptance across the dimensions of the Technology Acceptance Model. Additionally, situational interest significantly improved in the chatbot condition. The study found that trends in performance and emotional responses were favorable, while cognitive load increased slightly. The findings emphasize the evident potential of customized AI chatbots to supplement differentiated instruction in the domain of mathematics education. They provide a critical foundation for future research investigating the potential of AI chatbots to enhance student learning through careful integration and customization.

生成式人工智能因其促进个性化学习的潜力而越来越多地融入教育环境。然而，基于证据的实施概念仍然是实现其教学价值的必要条件。因此，本研究调查了ADA（一种为中学数学教育定制的特定领域的AI聊天机器人）在提高学习成果和学生参与度方面的教育功效。本研究以195名德国中学九年级学生为研究对象，在真实的课堂环境中进行整群随机对照研究。该研究的目的是比较ADA的个性化支持与传统分化材料。干预的目标是在单课干预中估计平方根的Heron方法，并进行前后评估。作为为数不多的针对中学数学教育中定制AI聊天机器人的集群随机对照研究之一，本研究同时评估了学习的情感和认知方面，以提供全面的研究图景。研究结果表明，在技术接受模型的各个维度上，学生的接受程度都很高。此外，在聊天机器人条件下，情境兴趣显著提高。研究发现，表现和情绪反应的趋势是有利的，而认知负荷略有增加。研究结果强调了定制人工智能聊天机器人在数学教育领域补充差异化教学方面的明显潜力。它们为未来研究人工智能聊天机器人的潜力提供了重要的基础，通过仔细整合和定制来增强学生的学习。

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引用次数: 0