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AI as Extraherics: Fostering Higher-order Thinking Skills in Human-AI Interaction

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arxiv 2409.09218 v2 pith:YOA272PM submitted 2024-09-13 cs.HC

Koji Yatani , Zefan Sramek , Chi-Lan Yang This is my paper · ORCID
classification cs.HC
keywords cognitiveinteractionusershumanhuman-aiskillsthinkingcritical
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As artificial intelligence (AI) technologies, including generative AI, continue to evolve, concerns have arisen about over-reliance on AI, which may lead to human deskilling and diminished cognitive engagement. Over-reliance on AI can also lead users to accept information given by AI without performing critical examinations, causing negative consequences, such as misleading users with hallucinated contents. This paper introduces extraheric AI, a human-AI interaction conceptual framework that fosters users' higher-order thinking skills, such as creativity, critical thinking, and problem-solving, during task completion. Unlike existing human-AI interaction designs, which replace or augment human cognition, extraheric AI fosters cognitive engagement by posing questions or providing alternative perspectives to users, rather than direct answers. We discuss interaction strategies, evaluation methods aligned with cognitive load theory and Bloom's taxonomy, and future research directions to ensure that human cognitive skills remain a crucial element in AI-integrated environments, promoting a balanced partnership between humans and AI.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 10 citations worldwide. Full citation record

  1. When Models Know More Than They Can Explain: Quantifying Knowledge Transfer in Human-AI Collaboration

    cs.AI 2025-06 conditional novelty 7.0 of 10

    Model benchmark performance only weakly predicts how well people learn from AI explanations, with notable outliers across code and math.

  2. Toward Resilient Human-AI Collaboration: A Lifecycle Taxonomy of Sociotechnical Risks and Cascading Failures

    cs.HC 2026-08 conditional novelty 5.0 of 10

    A literature synthesis maps human-AI collaboration failures into six interacting risk clusters arranged along a four-stage lifecycle.

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