Computers & Education最新文献

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 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

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

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.

随着人工智能（AI）和生成式人工智能（GenAI）技术越来越多地融入日常生活，对衡量人们对人工智能知识的有效工具的需求也在增长。在这里，我们提出了一个理论驱动的、基于绩效的评估AI和GenAI知识的量表的开发和验证。天平在一个双轴框架中接地。一个轴捕获三种知识类型：内容知识（人工智能是什么以及在哪里遇到它），程序知识（人工智能系统如何运行和使用）和认知知识（人工智能输出的特征和构建过程）。另一个轴包含三个知识领域：与技术相关的知识（人工智能系统），与用户相关的知识（用户与人工智能的交互）和与社会相关的知识（人工智能的社会和伦理影响）。基于对来自[国家]的800名上网成年人的在线调查，采用验证性因子分析对26项量表进行评估，结果表明模型拟合可接受。通过两阶段结构方程建模和分组比较进一步验证。总体而言，该量表被发现既有效又具有实际洞察力：虽然它再现了与其他结构（例如，对GenAI的信任，对AI的态度）的预期关系以及人口群体之间的预期差异，但它也提供了对AI知识复杂性的细致入微的见解。例如，该量表表明，对GenAI的信任与对AI的了解之间的关系建立在认知知识和社会知识的基础上。因此，这个新工具可以更精确地研究人工智能知识的不同类型和领域如何与人工智能介导的世界中的感知、行为和决策相关。

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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 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

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

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.

基于游戏的学习环境通常支持探索，但很少将学习者代理与严格的嵌入式评估联系起来。本研究报告了ALIVE（沉浸式虚拟环境中的学习代理）的设计和试点实施，这是一个虚拟探究环境，将学习代理框架与以证据为中心的游戏设计相结合。九名初高中学生完成了一项生态调查，包括证据收集、假设检验和因果解释。数据包括大声思考协议、玩法日志和简短的反馈问题。三角分析捕捉到了自我报告和观察到的代理之间的趋同和分歧。学习者表现出有意的决策，策略转变，并在不确定的情况下选择性地委托给系统支持。这些发现显示了一致的能力、证据和任务模型如何使调查行动可见和可解释。该研究还提供了一种多源方法来检查引导数字查询中的代理表达。限制包括小样本和单会话设计。未来的工作应该检查更长期的模式、更广泛的实现和跨领域的转移。

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引用次数: 0

From automation to thinking: The role of AGI in discourse analysis of computer-supported collaborative learning based on computational grounded theory 从自动化到思考：基于计算基础理论的AGI在计算机支持的协同学习话语分析中的作用

IF 10.5 1区教育学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Education

Pub Date : 2026-07-01 Epub Date: 2026-01-24 DOI: 10.1016/j.compedu.2026.105579

Tien-Chih Chang , Alice R.P. Li , Chia-Yu Wang , John J.H. Lin

Analyzing the complex dialogue central to computer-supported collaborative learning is crucial for understanding learning processes, yet remains a significant challenge for educational researchers due to the labor-intensive nature of manual coding and the semantic limitations of traditional computational methods. Recent advancements have highlighted the potential of Large Language Models (LLMs) to move beyond mere automation, demonstrating an ability for inference without task-specific data that is characteristic of artificial general intelligence. To harness this potential, this study introduced and evaluated a human-AI collaborative framework (CGT-LLM) that integrates LLMs into computational grounded theory. Specifically, CGT-LLM focuses on learning analytics for rich discursive data. Applied to dialogue from a climate change collaborative simulation game, the framework was evaluated against a supervised bidirectional encoder representations from transformers (BERT) baseline. The performance of the framework approached human expert-level performance in categories related to explicit instructions, numerical data, or direct statements of intent crucial to game objectives, while also demonstrating promising capability in identifying more abstract and less obvious themes. The findings demonstrate that the researcher's role in computational grounded theory remains critical, particularly in exploring data diversity during the discovery phase, and making final interpretive judgments for abstract themes during the classification phase. This framework thus positions LLMs as a valuable assistant rather than as a replacement for human expertise, providing educators and researchers with a tool to gain deeper, more scalable insights into collaborative learning processes, and offering potential to inform the design of timely pedagogical interventions.

分析计算机支持的协作学习的复杂对话对于理解学习过程至关重要，但由于手工编码的劳动密集型性质和传统计算方法的语义限制，对教育研究人员来说仍然是一个重大挑战。最近的进展突出了大型语言模型（llm）超越单纯自动化的潜力，展示了不需要特定任务数据的推理能力，这是人工智能的特征。为了利用这一潜力，本研究引入并评估了一个人类-人工智能协作框架（CGT-LLM），该框架将llm整合到计算基础理论中。具体来说，CGT-LLM侧重于学习分析丰富的话语数据。将该框架应用于气候变化协作模拟游戏中的对话，并根据来自变压器（BERT）基线的监督双向编码器表示对其进行评估。在与明确指令、数字数据或对游戏目标至关重要的直接意图陈述相关的类别中，该框架的表现接近人类专家水平，同时在识别更抽象和不太明显的主题方面也显示出有希望的能力。研究结果表明，研究人员在计算基础理论中的作用仍然至关重要，特别是在发现阶段探索数据多样性，以及在分类阶段对抽象主题做出最终解释性判断。因此，该框架将法学硕士定位为有价值的助手，而不是人类专业知识的替代品，为教育工作者和研究人员提供了一种工具，以获得对协作学习过程更深入、更可扩展的见解，并为及时的教学干预设计提供了信息。

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引用次数: 0

Generative artificial intelligence augments social interactivity and learning outcomes: Advancing the framework of a scaffolded human–GenAI shared agency 生成式人工智能增强社会互动性和学习成果：推进搭建的人类-基因共享代理框架

IF 10.5 1区教育学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Education

Pub Date : 2026-07-01 Epub Date: 2026-01-08 DOI: 10.1016/j.compedu.2026.105564

Yi-Chen Juan , Yuan-Hsuan Lee , Jiun-Yu Wu

Generative Artificial Intelligence (GenAI) functions not merely as a tool but an active collaborator in human knowledge construction; however, the Human-GenAI interaction dynamics is still underexplored. This study investigates Human-GenAI interaction profiles, the network interactivity and profile differences within a statistics learning community, as well as the underlying mechanisms linking Human-GenAI interaction to learning performance. We designed the Human–GenAI Inquiry and Problem-Solving Scaffold to foster shared agency between twenty-eight graduate students and GenAI across seven homework assignments in a sixteen-week advanced statistics course. Analytical approaches included k-modes clustering, social network analysis, and Partial Least Squares Structural Equation Modeling, complemented by case studies of interaction profiles. Three distinct Human-GenAI interaction profiles were identified: Human-GenAI collaborators, Peer collaborators with GenAI assistance, and Individual learners with late GenAI adoption. The network interactivity becomes cohesive with GenAI occupying the central hub role within the learning community. The models then demonstrate unique pathways through which Human-GenAI interaction influences learning performance, via degree centrality (number of direct connections) and peer nomination as helpers. The case studies highlight GenAI’s capability to augment human roles, encouraging deeper inquiry, expanding the depth of peer discussion, or promoting the exploration of diverse problem-solving strategies. These findings add value to theory and practice by providing empirical evidence for the framework of a scaffolded Human-GenAI shared agency, offering pedagogical implications to foster active student participation and cultivate learner agency within the symbiotic Human–GenAI partnership.

生成式人工智能（GenAI）不仅是人类知识建构的工具，而且是人类知识建构的积极合作者；然而，人类与基因的互动动力学仍未得到充分探索。本研究调查了统计学习社区中人类与基因的交互概况、网络交互性和概况差异，以及将人类与基因的交互与学习绩效联系起来的潜在机制。在为期16周的高级统计学课程中，我们设计了人类-基因ai调查和问题解决支架，以促进28名研究生和基因ai在7项家庭作业中的共享代理。分析方法包括k模式聚类、社会网络分析和偏最小二乘结构方程模型，并辅以互动概况的案例研究。确定了三种不同的人类-GenAI交互概况：人类-GenAI合作者，GenAI协助下的同伴合作者，以及晚期采用GenAI的个人学习者。随着GenAI在学习社区中占据中心枢纽角色，网络交互性变得紧密。然后，这些模型通过度中心性（直接连接的数量）和同伴提名作为助手，展示了人类-基因- ai交互影响学习表现的独特途径。这些案例研究强调了GenAI增强人类角色的能力，鼓励更深入的探究，扩大同行讨论的深度，或促进对各种问题解决策略的探索。这些发现为搭建人类-基因共享代理的框架提供了经验证据，为促进学生的积极参与和利用基因ai的潜力培养学习者代理和人类-基因共生知识构建提供了教学启示，从而增加了理论和实践的价值。

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引用次数: 0

Virtual reality serious games for promoting environmental systems thinking and pro-environmental policy support 虚拟现实严肃游戏为促进环境系统思考和亲环境政策提供支持

IF 10.5 1区教育学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Education

Pub Date : 2026-07-01 Epub Date: 2026-01-14 DOI: 10.1016/j.compedu.2026.105575

Joseph G. Guerriero , Pejman Sajjadi , Janet K. Swim , Alexander Klippel , Jamie DeCoster , Mahda M. Bagher

Virtual reality (VR) serious games can expose people to environmental processes they would not otherwise experience. This can make topics in environmental science more concrete to learners, improving learning outcomes and downstream behaviors related to environmental sustainability. In a randomized experiment (N = 189), we examined the effectiveness of a VR serious game designed to teach people about a topic in environmental science—the Critical Zone—by comparing it to a non-VR version of the game and to a static presentation of the same information on a website. Although the VR serious game promoted greater spatial presence and feelings of awe (which, in turn, translated to feeling more connected with nature), these effects did not translate to improved learning outcomes and pro-environmental policy support as we hypothesized across two separate models. Yet, exploratory analyses revealed a very small but significant indirect pathway by which the VR serious game promoted systems thinking about the Food-Energy-Water (FEW) nexus and pro-environmental policy support: VR (vs other learning formats) led to increases in a sense of spatial presence, then to perceived learning effectiveness, then to FEW systems thinking, and, finally, to pro-environmental policy support. Our results shed light on the mixed effect of VR and spatial presence on learning outcomes discussed in the wider literature on VR in education. Although the original hypotheses were largely unsupported, by exploring and highlighting pathways from learning formats to outcomes, we demonstrate the potential of VR for promoting learning and pro-environmental policy support.

虚拟现实（VR）严肃游戏可以让人们接触到他们原本不会经历的环境过程。这可以使环境科学的主题对学习者来说更加具体，从而改善与环境可持续性相关的学习成果和下游行为。在一项随机实验（N = 189）中，我们通过将VR严肃游戏与非VR版本的游戏以及网站上相同信息的静态呈现进行比较，检验了VR严肃游戏的有效性，该游戏旨在教授人们有关环境科学的主题——关键区域。尽管VR严肃游戏提升了更大的空间存在感和敬畏感（这反过来又转化为与自然联系更紧密的感觉），但正如我们在两个独立模型中假设的那样，这些效果并没有转化为改善的学习成果和亲环境政策支持。然而，探索性分析揭示了一个非常小但重要的间接途径，即VR严肃游戏促进了对食物-能源-水（FEW）关系的系统思考和亲环境政策支持：VR（相对于其他学习形式）导致空间存在感的增加，然后是感知学习效率的增加，然后是FEW系统思考，最后是亲环境政策支持。我们的研究结果揭示了VR和空间存在对学习结果的混合影响，这些影响在更广泛的关于VR教育的文献中讨论过。虽然最初的假设在很大程度上没有得到支持，但通过探索和强调从学习形式到结果的途径，我们证明了虚拟现实在促进学习和亲环境政策支持方面的潜力。

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引用次数: 0

The effects of critical thinking intervention on reliance behaviors, problem-solving quality, and creativity during human-Generative AI collaborative learning 人类生成人工智能协作学习中批判性思维干预对依赖行为、问题解决质量和创造力的影响

IF 10.5 1区教育学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Education

Pub Date : 2026-07-01 Epub Date: 2026-01-18 DOI: 10.1016/j.compedu.2026.105576

Chenyu Hou , Gaoxia Zhu , Yanzhi Liu , Vidya Sudarshan , Josephine Leng Leng Chong , Fannie Yifan Zhang , Michael Yong Heng Tan , Yew Soon Ong

As Generative AI becomes increasingly used in various educational contexts, understanding how students engage with these tools during collaborative problem-solving is critical. While prior research suggests that critical thinking is essential in human-AI problem-solving, few studies have examined how instructional interventions, targeting critical thinking, might shape their reliance behaviors and collaborative outcomes. This study investigates the effects of a critical thinking intervention embedded in a problem-based learning (PBL) environment where students are engaged with Generative AI. The intervention combined strategies that foster critical thinking, including authentic instruction, structured dialogue, and AI-supported peer mentoring, aiming to promote students' thoughtful engagement and improve problem-solving performance. Participants (N = 226) were assigned to experimental (with critical thinking interventions) or comparison (without critical thinking interventions) conditions. We used pre- and post-surveys to measure participants' trust, critical thinking, and AI reliance behaviors, and group reports and chat histories to assess their problem-solving quality and creativity. Results revealed that the intervention did not produce significant improvement in self-reported critical thinking, possibly due to the short intervention duration. However, the intervention led to a marginal reduction in students' thoughtless use of Generative AI and significantly reduced the direct adoption of AI-generated content. Notably, students in the intervention condition produced more creative solutions, demonstrating higher levels of originality and idea density in their group reports. These findings suggest that how students use Generative AI is critical, especially when it is almost impossible to control whether they use it or not. The study highlights the importance of designing interventions that cultivate students’ critical thinking to support creative human-AI problem-solving.

随着生成式人工智能越来越多地应用于各种教育环境，了解学生在协作解决问题过程中如何使用这些工具至关重要。虽然之前的研究表明，批判性思维在人类-人工智能问题解决中至关重要，但很少有研究调查针对批判性思维的教学干预如何影响他们的依赖行为和合作结果。本研究探讨了在学生参与生成式人工智能的基于问题的学习（PBL）环境中嵌入批判性思维干预的效果。干预措施结合了培养批判性思维的策略，包括真实的教学、结构化的对话和人工智能支持的同伴指导，旨在促进学生的深思熟虑参与，提高解决问题的能力。参与者（N = 226）被分配到实验（有批判性思维干预）或比较（没有批判性思维干预）条件。我们使用前后调查来衡量参与者的信任、批判性思维和人工智能依赖行为，并使用小组报告和聊天记录来评估他们解决问题的质量和创造力。结果显示，干预并没有显著改善自我报告的批判性思维，可能是由于干预时间短。然而，干预导致学生对生成式人工智能的轻率使用略有减少，并显著减少了对人工智能生成内容的直接采用。值得注意的是，在干预条件下的学生提出了更多创造性的解决方案，在他们的小组报告中表现出更高的原创性和想法密度。这些发现表明，学生如何使用生成式人工智能是至关重要的，尤其是在几乎不可能控制他们是否使用它的情况下。该研究强调了设计干预措施的重要性，这些干预措施可以培养学生的批判性思维，以支持创造性的人类-人工智能问题解决。

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引用次数: 0

Generative AI: A double-edged sword for creative thinking learning — Evidence from facial expressions and fNIRS 生成式人工智能：创造性思维学习的双刃剑——来自面部表情和近红外光谱的证据

IF 10.5 1区教育学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Education

Pub Date : 2026-07-01 Epub Date: 2026-01-19 DOI: 10.1016/j.compedu.2026.105578

Xinheng Song , Yue Zhang , Zhaolin Lu , Linci Xu , Hengheng Shen

With the widespread integration of generative AI tools into educational contexts, understanding their influence on learners’ cognitive and emotional processes has become increasingly critical. While AI holds potential for enhancing creativity, its double-edged impact on neurocognitive and emotional processes still requires further investigation. This study investigates the impact of generative AI-based learning tools on the creative thinking learning process. Participants were divided into two groups: a generative AI design group and a traditional design group. They completed tasks employing the divergent brainstorming creative method and the structured innovation TRIZ method. During these tasks, both facial expressions and functional near-infrared spectroscopy (fNIRS) data were collected to explore the effects of generative AI-assisted creative thinking education on students’ facial emotional changes and prefrontal cortex (PFC) activation patterns. Expert evaluations were conducted to assess the outcomes of creative thinking. The results indicated that generative AI significantly enhanced creative thinking performance. Facial emotion analysis revealed that, with generative AI assistance, the brainstorming process generated more fear emotions, while the Theory of Inventive Problem Solving (TRIZ) design process produced more happiness emotions. fNIRS data showed that, with generative AI support, the brainstorming process facilitated activation in the right DLPFC, while the TRIZ design process activated both the left and right DLPFC areas. Machine learning classifiers indicated that facial emotion and fNIRS data could serve as effective indicators for assessing creative thinking performance. The CatBoost classifier achieved an accuracy rate of 91.40 %/89.06 % in the two groups. This study focuses on learners’ facial emotions and PFC activity, revealing that while generative AI enhances creative thinking performance, it may also increase negative emotions. The findings call for caution in using generative AI in creativity education to avoid potential negative psychological effects on students, despite its benefits in promoting creative thinking.

随着生成式人工智能工具在教育环境中的广泛整合，了解它们对学习者认知和情感过程的影响变得越来越重要。虽然人工智能具有增强创造力的潜力，但它对神经认知和情感过程的双刃剑影响仍需要进一步研究。本研究探讨了基于生成人工智能的学习工具对创造性思维学习过程的影响。参与者被分为两组：生成式人工智能设计组和传统设计组。他们采用发散性头脑风暴创造性方法和结构化创新TRIZ方法完成任务。在这些任务中，收集面部表情和功能近红外光谱（fNIRS）数据，以探索生成式人工智能辅助创造性思维教育对学生面部情绪变化和前额叶皮层（PFC）激活模式的影响。进行了专家评估，以评估创造性思维的结果。结果表明，生成式人工智能显著提高了创造性思维的表现。面部情绪分析显示，在生成人工智能的帮助下，头脑风暴过程产生了更多的恐惧情绪，而创造性问题解决理论（TRIZ）设计过程产生了更多的快乐情绪。fNIRS数据显示，在生成式人工智能支持下，头脑风暴过程促进了右侧DLPFC的激活，而TRIZ设计过程同时激活了左侧和右侧DLPFC区域。机器学习分类器表明，面部情绪和fNIRS数据可以作为评估创造性思维表现的有效指标。CatBoost分类器在两组中的准确率分别为91.40% / 89.06%。本研究主要关注学习者的面部情绪和PFC活动，揭示了生成式人工智能在提高创造性思维表现的同时，也可能增加消极情绪。研究结果呼吁在创造力教育中使用生成式人工智能时要谨慎，以避免对学生产生潜在的负面心理影响，尽管它在促进创造性思维方面有好处。

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引用次数: 0

How explanatory features of AI and time frame reshape adolescents’ decision-making 人工智能和时间框架的解释性特征如何重塑青少年的决策

IF 10.5 1区教育学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Education

Pub Date : 2026-07-01 Epub Date: 2026-01-08 DOI: 10.1016/j.compedu.2026.105563

Zhuo Shen , Yinghe Chen , Jingyi Zhang , Hengrun Chen

As AI technologies permeate daily life, adolescents' distinctive cognitive profiles make their decision-making highly sensitive to AI explanation features. The study aimed to examine the underlying mechanisms by which AI's explanatory features and time frame impact adolescents' decision-making. We created an online platform where adolescents interacted with an explainable AI. A preliminary survey identified 10 mathematics-related factors. Experiment 1 involved 158 students (M_age = 13.7) and used a 3 (explanation type: prediction, causal, counterfactual) × 2 (perceived control: high, low) × 2 (perceived reliability: reliable, unreliable) mixed design. Experiment 2 recruited 225 students (M_age = 13.7) and employed a 3 (explanation type) × 2 (time frame: short-term, long-term) mixed design. Decision-making and expectation (expected impact of each factor on math achievement) were the outcomes in both experiments. In Experiment 1, perceived unreliable counterfactual explanations for low-control factors produced the lowest expectation and decision-making probability, whereas predictions and causal explanations did not differ. For high-control factors, perceived reliable counterfactual explanations similarly reduced decision-making probability, although expectation remained constant across explanations. In Experiment 2, predictions and causal explanations led to higher decision-making probability for short-term events than long-term ones, while counterfactuals reversed this pattern. While counterfactual explanations help restore trust and motivate change in distant, uncertain contexts, they can trigger reactance and reduce action when events feel controllable or imminent. Although adolescents cognitively understand causality and time frames, they still struggle to effectively regulate their decisions. AI model explanations should therefore account for the developmental characteristics of adolescents and recognize the dual effects inherent in counterfactual explanations.

随着人工智能技术渗透到日常生活中，青少年独特的认知特征使得他们的决策对人工智能的解释特征高度敏感。本研究旨在探讨人工智能的解释特征和时间框架影响青少年决策的潜在机制。我们创建了一个在线平台，让青少年与一个可解释的人工智能互动。一项初步调查确定了10个与数学有关的因素。实验1涉及158名学生（Mage = 13.7），采用3（解释类型：预测、因果、反事实）× 2（感知控制：高、低）× 2（感知信度：可靠、不可靠）混合设计。实验2共招募225名学生（Mage = 13.7），采用3（解释类型）× 2（时间框架：短期、长期）混合设计。决策和期望（每个因素对数学成绩的预期影响）是两个实验的结果。在实验1中，低控制因素的感知不可靠反事实解释产生的期望和决策概率最低，而预测和因果解释没有差异。对于高控制因素，感知可靠的反事实解释同样降低了决策概率，尽管期望在解释之间保持不变。在实验2中，预测和因果解释导致短期事件的决策概率高于长期事件，而反事实则逆转了这一模式。虽然反事实的解释有助于在遥远、不确定的环境中恢复信任和激励变革，但当事件感觉可控或迫在眉睫时，它们可能引发抗拒，减少行动。虽然青少年在认知上理解因果关系和时间框架，但他们仍然难以有效地调节自己的决定。因此，人工智能模型解释应该考虑到青少年的发展特征，并认识到反事实解释固有的双重效应。

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引用次数: 0

A systematic review of multimodal learning analytics in computer-supported collaborative learning 计算机支持的协作学习中多模态学习分析的系统综述

IF 10.5 1区教育学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Education

Pub Date : 2026-07-01 Epub Date: 2026-01-13 DOI: 10.1016/j.compedu.2026.105574

Fan Ouyang , Xianping Bai

Multimodal learning analytics (MMLA) has provided new perspectives for computer-supported collaborative learning (CSCL) by capturing multimodal data to explore behavior, social interaction, cognition, regulation, and emotion in CSCL process. However, there are critical challenges in handling multimodal data in CSCL context, such as multimodal data preprocessing methods, selecting suitable analysis methods and tools, and integrating multi-source, multimodal data to represent learning indicators in CSCL process. To fill these gaps, this systematic review constructed a conceptual framework of MMLA in CSCL and provided an overview of the contexts, multimodal data, indicators, data preprocessing methods, analysis methods, and tools, and effects of MMLA applications in CSCL from 2012 to 2024. One hundred fourteen studies articles were included for the final synthesis. Results found that: (1) existing studies primarily focused on groups’ social interactions in CSCL; (2) visual data was commonly adopted in CSCL; (3) the relationships between multimodal data and learning indicators in CSCL included four types, namely One-to-One, Many-to-One, One-to-Many, and Many-to-Many, with particular emphasis on Many-to-One relationships; (4) the most frequently used data preprocessing method was manual coding and extraction, and the utilization of traditional analysis methods (e.g., statistical analysis) had gradually decreased in CSCL, while advanced analysis techniques (e.g., AI algorithms) were gradually gaining traction but were not yet widely adopted; and (5) the application of MMLA in CSCL had positive effects on both learners and instructors, which primarily help instructors comprehensively understanding the CSCL process. Based on the results, this research proposed theoretical, technological, and practical implications to guide future research in the application of MMLA within CSCL contexts.

多模态学习分析（MMLA）通过捕获多模态数据来探索计算机支持的协同学习过程中的行为、社会互动、认知、调节和情绪，为计算机支持的协同学习（CSCL）提供了新的视角。然而，在CSCL环境下处理多模态数据存在着严峻的挑战，如多模态数据预处理方法、选择合适的分析方法和工具、整合多源、多模态数据来表示CSCL过程中的学习指标等。为了填补这些空白，本文构建了MMLA在CSCL中的概念框架，并概述了2012 - 2024年MMLA在CSCL中的应用背景、多模态数据、指标、数据预处理方法、分析方法和工具，以及MMLA在CSCL中的应用效果。最终合成纳入了114篇研究论文。结果发现：(1)现有的研究主要集中在CSCL群体的社会互动方面；(2) CSCL多采用视觉数据；(3) CSCL中多模态数据与学习指标的关系包括一对一、多对一、一对多和多对多四种类型，其中多对一关系尤为突出；(4) CSCL中最常用的数据预处理方法是人工编码和提取，传统的分析方法（如统计分析）的使用逐渐减少，而先进的分析技术（如AI算法）逐渐受到关注，但尚未广泛采用；(5) MMLA在CSCL教学中的应用对学习者和教师都有积极的影响，主要是帮助教师全面理解CSCL教学过程。在此基础上，本研究提出了理论、技术和实践意义，以指导未来在CSCL背景下MMLA应用的研究。

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引用次数: 0

Timing matters! Using delayed signaling to improve experiential learning in procedural VR training 时间问题!应用延迟信号改善程序性VR训练中的体验学习

IF 10.5 1区教育学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Education

Pub Date : 2026-06-01 Epub Date: 2026-01-14 DOI: 10.1016/j.compedu.2025.105553

Jonas De Bruyne , Charlotte Larmuseau , Lieven De Marez , Durk Talsma , Klaas Bombeke

‘Learning by doing’, or experiential learning, is increasingly implemented through immersive media such as virtual reality (VR) across domains like education and professional training. Immersive technologies enable dynamic instruction and guidance, but this potential remains underexplored. To support learning, cognitive load theory promotes signaling to reduce cognitive load by guiding attention to essential content, while discovery learning encourages minimal guidance to foster exploration. While the temporal aspect of the signaling principle is underrepresented in literature, this study suggests that striking a balance between the theoretical approaches is possible by delaying additional guidance. This work therefore investigates the impact of delayed signaling on experiential learning in VR, using a VR training module on electrofusion welding that is currently used in industry. When comparing performance after training either with immediate or delayed signaling, the data suggested improved procedural learning when signaling was delayed, with an average improvement of 8% in task completion time (

p

<

.05, d = .76). Furthermore, the method with delayed signaling did not increase cognitive load, as measured by self-reports, suggesting that discovery learning in combination with (delayed) guidance does not place undue cognitive demand on participants. The findings stress the – currently underexposed – importance of timing of visual aids through signaling and how they can be used to optimize training effectiveness. The results are interpreted in light of existing learning literature with future directions for adaptive training systems highlighted.

“边做边学”或体验式学习越来越多地通过虚拟现实（VR）等沉浸式媒体在教育和专业培训等领域实现。沉浸式技术可以实现动态教学和指导，但这种潜力仍未得到充分开发。为了支持学习，认知负荷理论提倡通过引导注意力到基本内容来减少认知负荷，而发现学习则鼓励最少的指导来促进探索。虽然信号传导原理的时间方面在文献中代表性不足，但本研究表明，通过延迟额外的指导，可以在理论方法之间取得平衡。因此，本研究利用目前在工业中使用的电熔焊接VR培训模块，研究延迟信号对VR体验学习的影响。当比较即时或延迟信号训练后的表现时，数据表明延迟信号时程序学习得到改善，任务完成时间平均提高8% （p < 0.05, d = 0.76）。此外，根据自我报告的测量，延迟信号的方法并没有增加认知负荷，这表明发现学习与（延迟）指导相结合不会对参与者产生不适当的认知需求。研究结果强调了——目前尚未充分暴露的——通过信号提供视觉辅助的时机的重要性，以及如何利用它们来优化训练效果。根据现有的学习文献对结果进行了解释，并强调了自适应训练系统的未来方向。

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引用次数: 0