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Updated: Jun 11, 2025

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
Published on: June 30, 2020
AI-induced hyper-learning in humans
1Affective Brain Lab, Department of Experimental Psychology, University College London, London, UK; Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, UK.
Abstract:
Humans evolved to learn from one another. Today, however, learning opportunities often emerge from interactions with AI systems. Here, we argue that learning from AI systems resembles learning from other humans, but may be faster and more efficient. Such 'hyper learning' can occur because AI: (i) provides a high signal-to-noise ratio that facilitates learning, (ii) has greater data processing ability, enabling it to generate persuasive arguments, and (iii) is perceived (in some domains) to have superior knowledge compared to humans. As a result, humans more quickly adopt biases from AI, are often more easily persuaded by AI, and exhibit novel problem-solving strategies after interacting with AI. Greater awareness of AI's influences is needed to mitigate the potential negative outcomes of human-AI interactions.
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