Governing Reflective Human-AI Collaboration: A Framework for Epistemic Scaffolding and Traceable Reasoning

Large language models have advanced rapidly, from pattern recognition to emerging forms of reasoning, yet they remain confined to linguistic simulation rather than grounded understanding. They can produce fluent outputs that resemble reflection, but lack temporal continuity, causal feedback, and anchoring in real-world interaction. This paper proposes a complementary approach in which reasoning is treated as a relational process distributed between human and model rather than an internal capabil

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ethics.ai (16 April 2026), “Governing Reflective Human-AI Collaboration: A Framework for Epistemic Scaffolding and Traceable Reasoning,” evidence record 5783, https://ethics.ai/record/5783 (originally published by arXiv).

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