An Explicit-Thinking LLM-Based Cognitive Apprenticeship Approach to Promote Students’ Metacognition, Higher Order Thinking Skills, and Learning Performance

IF 7.4 3区 教育学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
IEEE Transactions on Learning Technologies Pub Date : 2026-01-01 Epub Date: 2026-03-26 DOI:10.1109/TLT.2026.3696968
Yihua Zhong;Jili Chen;Changqin Huang;Xizhe Wang;Yixuan Chen;Tao He
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Abstract

With the rapid development of generative artificial intelligence (AI), large language models (LLMs) have been increasingly applied in education. Although LLMs demonstrate considerable potential in supporting student learning, conventional LLMs tend to provide direct answers without explicitly articulating their reasoning processes. This hinders students’ ability to comprehend the underlying logic of problem solving, thereby limiting opportunities for metacognitive engagement and the development of higher order thinking skills. To address this issue, this study proposes a cognitive apprenticeship approach based on an explicit-thinking LLM that provides structured thinking support during LLM-based learning activities. By employing the modeling, coaching, and reflection mechanisms of cognitive apprenticeship, the approach guides students to internalize the LLM’s reasoning processes, thereby fostering their higher order thinking skills. A quasi-experimental design was adopted, involving 68 college students divided into experimental and control groups. Participants completed a writing-based learning task using either the explicit-thinking LLM or a conventional LLM, and the data were analyzed using a mixed-methods approach that included eye tracking. The results showed that, compared to the control group, the experimental group exhibited enhanced metacognitive regulation, demonstrated superior performance in both problem solving and critical thinking, and achieved better learning outcomes when they devoted greater attention to the reasoning process generated by the explicit-thinking LLM. This study suggests that the explicit-thinking LLM enhances students’ learning performance and higher order thinking by making reasoning processes more transparent. It further emphasizes the essential role of cultivating students’ critical and creative engagement with AI to effectively support the development of higher order thinking skills.
基于外显思维法学硕士的认知学徒模式促进学生元认知、高阶思维技能和学习绩效
随着生成式人工智能(AI)的快速发展,大型语言模型(llm)在教育中的应用越来越广泛。尽管法学硕士在支持学生学习方面表现出相当大的潜力,但传统的法学硕士倾向于提供直接的答案,而没有明确阐明其推理过程。这阻碍了学生理解解决问题的潜在逻辑的能力,从而限制了元认知参与和高阶思维技能发展的机会。为了解决这一问题,本研究提出了一种基于显式思维法学硕士的认知学徒方法,该方法在法学硕士学习活动中提供结构化思维支持。通过采用认知学徒的建模、指导和反思机制,该方法引导学生内化法学硕士的推理过程,从而培养他们的高阶思维技能。采用准实验设计,将68名大学生分为实验组和对照组。参与者使用显性思维法学硕士或传统法学硕士完成了一项以写作为基础的学习任务,并使用包括眼动追踪在内的混合方法分析数据。结果表明,与对照组相比,实验组的元认知调节能力增强,在解决问题和批判性思维方面表现优异,并且当他们更多地关注外显思维LLM产生的推理过程时,他们的学习效果更好。本研究表明,外显思维LLM通过使推理过程更加透明来提高学生的学习绩效和高阶思维。它进一步强调了培养学生对人工智能的批判性和创造性参与的重要作用,以有效地支持高阶思维技能的发展。
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来源期刊
IEEE Transactions on Learning Technologies
IEEE Transactions on Learning Technologies COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-
CiteScore
7.50
自引率
5.40%
发文量
82
审稿时长
>12 weeks
期刊介绍: The IEEE Transactions on Learning Technologies covers all advances in learning technologies and their applications, including but not limited to the following topics: innovative online learning systems; intelligent tutors; educational games; simulation systems for education and training; collaborative learning tools; learning with mobile devices; wearable devices and interfaces for learning; personalized and adaptive learning systems; tools for formative and summative assessment; tools for learning analytics and educational data mining; ontologies for learning systems; standards and web services that support learning; authoring tools for learning materials; computer support for peer tutoring; learning via computer-mediated inquiry, field, and lab work; social learning techniques; social networks and infrastructures for learning and knowledge sharing; and creation and management of learning objects.
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