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
Yihua Zhong;Jili Chen;Changqin Huang;Xizhe Wang;Yixuan Chen;Tao He
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引用次数: 0
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.
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.