Scaffolding critical thinking with generative AI: Design principles for integrating large language models in higher education

作者：Mireia Vendrell, Samantha-Kaye Johnston · 发表于：Computers and Education: Artificial Intelligence · 年份：2026 · DOI：10.1016/j.caeai.2026.100572 · 被引用次数：30 · 研究领域：Computer Science

The rapid adoption of Large Language Models (LLMs) such as GPT-4 and DeepSeek R1 is transforming learning in higher education, yet unstructured use can weaken critical thinking by encouraging cognitive offloading, met-acognitive disengagement, and reduced epistemic agency. This paper presents a conceptual and normative analysis that synthesises research from cognitive psychology, educational theory, and AI ethics to develop a design-oriented pedagogical framework for integrating LLMs in ways that strengthen, rather than displace, higher-order thinking. Grounded in design-based research principles, the framework identifies six essential processes that underpin critical engagement: conceptual interpretation, inferential reasoning, evaluative judgement, metacognitive regulation, intellectual curiosity, and epistemic integrity. These processes are translated into eight actionable design principles, including preserving cognitive friction, positioning LLMs as pro-visional thinking partners, embedding evaluation throughout learning, and sequencing AI-mediated with AI-free phases. Two illustrative classroom scenarios showcase practical application. The framework offers educators a theoretically grounded and practically applicable model for cultivating critical thinking and epistemic responsibility in AI-rich learning environments, contributing to emerging new systems of learning in higher education.