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

学徒制 计算机科学 认知 认知学徒 数学教育 心理学 知识管理 认知心理学 认知风格 体验式学习 人机交互 电子学习 人工智能 背景(考古学) 任务分析 教育技术 计算机辅助教学 认知科学 认知负荷 认知系统 主动学习(机器学习) 应用心理学
作者
Yihua Zhong,Jili Chen,Changqin Huang,Xizhe Wang,Yixuan Chen,Tao He
出处
期刊:IEEE Transactions on Learning Technologies [Institute of Electrical and Electronics Engineers]
卷期号:19: 548-565
标识
DOI:10.1109/tlt.2026.3696968
摘要

With the rapid development of generative 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 (1) exhibited enhanced metacognitive regulation, (2) demonstrated superior performance in both problem-solving and critical thinking, and (3) 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.
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