The Memory Paradox: Why Our Brains Need Knowledge in an Age of AI

• URL: http://arxiv.org/abs/2506.11015v2
• Date: Thu, 19 Jun 2025 17:05:41 GMT
• Title: The Memory Paradox: Why Our Brains Need Knowledge in an Age of AI
• Authors: Barbara Oakley, Michael Johnston, Ken-Zen Chen, Eulho Jung, Terrence J. Sejnowski,
• Abstract summary: Drawing on neuroscience and cognitive psychology, this paper examines how heavy reliance on AI systems may impair the consolidation of declarative and procedural memory.<n>We highlight striking parallels between deep learning phenomena such as "grokking" and the neuroscience of overlearning and intuition.<n>The paper concludes with policy implications for education and workforce training in the age of large language models.
• Score: 4.508868068781058
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the age of generative AI and ubiquitous digital tools, human cognition faces a structural paradox: as external aids become more capable, internal memory systems risk atrophy. Drawing on neuroscience and cognitive psychology, this paper examines how heavy reliance on AI systems and discovery-based pedagogies may impair the consolidation of declarative and procedural memory -- systems essential for expertise, critical thinking, and long-term retention. We review how tools like ChatGPT and calculators can short-circuit the retrieval, error correction, and schema-building processes necessary for robust neural encoding. Notably, we highlight striking parallels between deep learning phenomena such as "grokking" and the neuroscience of overlearning and intuition. Empirical studies are discussed showing how premature reliance on AI during learning inhibits proceduralization and intuitive mastery. We argue that effective human-AI interaction depends on strong internal models -- biological "schemata" and neural manifolds -- that enable users to evaluate, refine, and guide AI output. The paper concludes with policy implications for education and workforce training in the age of large language models.

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