Artificial intelligence in higher education: reconfiguring metacognition in unequal contexts
Artificial intelligence (AI) is increasingly reshaping higher education, influencing how students access knowledge, engage with learning tasks, and regulate their learning processes. While existing research has largely focused on efficiency and performance, comparatively limited attention has been given to how AI affects metacognitive processes such as planning, monitoring, and evaluation. This study addresses this gap through a qualitative systematic literature review with thematic synthesis of peer-reviewed studies published between 2018 and 2026, supplemented by foundational theoretical work on metacognition. The findings indicate that AI does not exert a uniform effect on metacognition. Instead, its impact is dynamic, context-dependent, and shaped by the interaction between technological, pedagogical, and socio-economic conditions. AI can enhance metacognition by externalising cognitive processes and supporting feedback, but it can also enable cognitive offloading, reducing learners’ engagement in regulation. A third mode, hybrid regulation, emerges in which metacognitive processes are co-constructed between learners and AI systems. These findings are particularly relevant to AI-supported and technology-rich learning environments, including STEM education contexts where intelligent tutoring systems and generative AI tools increasingly mediate learning processes. The study proposes a conceptual framework that positions AI along a continuum from support to offloading, shaped by contextual conditions. The findings reframe AI as a system that reorganises, rather than simply enhances or erodes, metacognition in higher education.