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This paper explores how artificial intelligence (AI) can enhance learning transfer, emphasizing metacognitive processes and the need for a comprehensive taxonomy.

May 21, 2026Open Access

Transfer of Learning mediated by artificial intelligence

Key Points

This paper explores how artificial intelligence (AI) can enhance learning transfer, emphasizing metacognitive processes and the need for a comprehensive taxonomy.
Analysis of key literature on learning transfer and artificial intelligence.
Discussion of AI-mediated scaffolding and feedback mechanisms.
Proposes new research directions related to AI in educational contexts.
Identifies how AI can assist learning transfer through personalized strategies.
Examines the balance between student support and autonomy, highlighting the assistance dilemma.
Calls for the establishment of a far transfer taxonomy to better categorize AI applications.

Abstract

This conceptual paper examines the role of artificial intelligence (AI) in facilitating learning transfer, with a focus on metacognitive processes, the assistance dilemma, and the need for a taxonomy of far transfer. It reviews key literature (Perkins Lehn, 2011; Koedinger Barnett & Ceci, 2002) and analyzes how AI-mediated scaffolding, feedback granularity, and personalization can influence near and far transfer. The paper also discusses the interaction plateau, the balance between support and autonomy, and proposes new research lines for AI in education. Originally presented at EDUEMER 2025 (Seville, Spain). Keywords: learning transfer, artificial intelligence in education, metacognition, assistance dilemma, far transfer taxonomy, intelligent tutoring systems

Transfer of Learning mediated by artificial intelligence

Key Points

Abstract

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