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This chapter identifies and explores a paradox of AI-driven personalisation in education. Personalisation promises to support learning by adapting, predicting, and optimising learning pathways, but in doing so it risks undermining the very cognitive, psychological, and epistemic conditions that facilitate meaningful learning. Through a postdigital reading of education and technology, AI is understood not to function as an additional tool or object that can be introduced to and through the process of learning. Rather, it is treated as an active participant in shaping the very conditions under which cognition, learner identity, and knowledge practices are developed. Dominant design logics, centred on datafication, prediction, and performance optimisation, are argued to contribute to the development of what we term the “cluttered mind”. Characterised by information overload, fragmentation, superficial processing, algorithmic dependence, and a static view of learner ability, this is contrasted with what we term the “wise mind”, characterised by integration, reflection, epistemic activity, and learner growth. This chapter presents three conceptual contributions for exploring the complex, multidimensional phenomenon of learning with AI. First, we introduce the “cluttered mind” and “wise mind” as new perspectives on learning with AI that go beyond concerns with measurable learning outcomes. Second, drawing on these perspectives, we develop a multilevel framework for studying AI-supported learning that traverses cognitive, psychological, and epistemic levels of analysis. Third, we use this framework to generate four interconnected design principles for integrated and reflective learning with AI, metacognitive visibility, productive struggle, epistemic multiplicity, and algorithmic transparency, that illuminate how AI is transforming the processes and conditions that foster learning. Thus, the chapter moves beyond the question of whether AI can enhance learning outcomes and explores in detail how AI is reshaping the way learners process information, form judgements about their capacities, and interact with knowledge.

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From Cluttered Minds to Wise Minds: Rethinking AI-driven Personalisation and Learning Design in the Postdigital Condition

  • 章节

  • 发表日期：

    2026年

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  • Laid Bouakaz,
  • Dennis Beach

摘要

This chapter identifies and explores a paradox of AI-driven personalisation in education. Personalisation promises to support learning by adapting, predicting, and optimising learning pathways, but in doing so it risks undermining the very cognitive, psychological, and epistemic conditions that facilitate meaningful learning. Through a postdigital reading of education and technology, AI is understood not to function as an additional tool or object that can be introduced to and through the process of learning. Rather, it is treated as an active participant in shaping the very conditions under which cognition, learner identity, and knowledge practices are developed. Dominant design logics, centred on datafication, prediction, and performance optimisation, are argued to contribute to the development of what we term the “cluttered mind”. Characterised by information overload, fragmentation, superficial processing, algorithmic dependence, and a static view of learner ability, this is contrasted with what we term the “wise mind”, characterised by integration, reflection, epistemic activity, and learner growth. This chapter presents three conceptual contributions for exploring the complex, multidimensional phenomenon of learning with AI. First, we introduce the “cluttered mind” and “wise mind” as new perspectives on learning with AI that go beyond concerns with measurable learning outcomes. Second, drawing on these perspectives, we develop a multilevel framework for studying AI-supported learning that traverses cognitive, psychological, and epistemic levels of analysis. Third, we use this framework to generate four interconnected design principles for integrated and reflective learning with AI, metacognitive visibility, productive struggle, epistemic multiplicity, and algorithmic transparency, that illuminate how AI is transforming the processes and conditions that foster learning. Thus, the chapter moves beyond the question of whether AI can enhance learning outcomes and explores in detail how AI is reshaping the way learners process information, form judgements about their capacities, and interact with knowledge.