Alternative AI Philosophy: Daoism as Method for AI in Education

arXiv:2609.10842v1 Announce Type: new Abstract: As artificial intelligence (AI) rapidly iterates and transforms teaching, learning, and knowledge production, philosophical reflection has become increasingly indispensable to educational debates that remain predominantly shaped by Western intellectual traditions. This article proposes Daoism as an alternative philosophical framework for reimagining AI in education. Through philosophical analysis and textual interpretation of classical Daoist sources, brought into dialogue with contemporary scholarship on AI in education, it examines how the Daoist concepts of "Dao nature," "self-cultivation," and the "Zhenren" address fundamental questions concerning reality, the epistemic aims of education, and ethical action in the AI-mediated era. In doing so, the article diversifies the philosophical voices shaping inquiry into AI and education, enriching the field's conceptual resources for grappling with the philosophical questions AI raises for education and offering a genuinely pluralistic foundation for comparative philosophy of education in the AI era.

Proposes alternative philosophical framework

The article introduces Daoism as an alternative philosophical framework to enrich reflections on AI in education, potentially enhancing ethical and epistemic approaches. However, it does not claim direct societal changes or technological impacts. Its influence is intellectual and indirect, with uncertain overall effect on human flourishing.

This is a philosophical/conceptual essay, not an empirical claim about AI's societal effects. It offers a framework for interpreting AI in education rather than evidence of impact, positive or negative. Genuinely neutral in implication for human flourishing.

The text proposes an alternative philosophical framework for AI in education. It suggests that Daoism can address fundamental questions and diversify debates. The implications for human flourishing are uncertain.

SOURCE     arXiv cs.CY (Computers and Society) (tier 1)
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DOCUMENT   21f7234e-e552-493c-a698-a5bd45e094ca
           https://arxiv.org/abs/2609.10842
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EVIDENCE   1 extracted excerpt
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MODEL RUN  4 runs, methodology 1.0.1
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SCORE      +3  (Mixed or uncertain)
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CONFIDENCE 68%