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Revisiting Learning Styles in the Age of Generative AI: A Conceptual Framework for Regulation and Agency

Primary research

#1106

T1new
Topic
unassigned (set during synthesis)
First seen
2026-08-01 07:15:59
Last seen
2026-08-01 07:15:59

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  • Semantic Scholar2026-08-01 07:15:27
    Revisiting Learning Styles in the Age of Generative AI: A Conceptual Framework for Regulation and Agency

    This conceptual paper revisits the learning styles debate in the context of generative and adaptive artificial intelligence in higher education. It does not seek to rehabilitate classical learning style taxonomies, whose prescriptive claims have been widely challenged as a neuromyth. Instead, it argues that AI-mediated learning creates new conditions under which patterns of regulation, delegation, verification, iteration, epistemic control, and ethical responsibility may become more observable and pedagogically relevant. Drawing on research on learning styles, neuromyths, AI literacy, adaptive learning, assessment, self-regulated learning, metacognition, and epistemic agency, the paper proposes a conceptual framework for regulation and agency styles in AI-mediated learning. In this framework, style is not treated as a fixed psychological trait or as a category into which learners should be sorted, but as a situated and modifiable profile of decisions and actions distributed across learners, tasks, and intelligent systems. Three analytical dimensions are proposed: epistemic control and metacognitive orchestration, adaptive regulation and generative iteration, and socio-algorithmic agency and ethical governance. The paper concludes with implications for task design, assessment, teacher education, and equitable AI integration.