{"ID":2845054,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2601.06030","arxiv_id":"2601.06030","title":"From Augmentation to Symbiosis: A Review of Human-AI Collaboration Frameworks, Performance, and Perils","abstract":"This paper offers a concise, 60-year synthesis of human-AI collaboration, from Licklider's ``man-computer symbiosis\" (AI as colleague) and Engelbart's ``augmenting human intellect\" (AI as tool) to contemporary poles: Human-Centered AI's ``supertool\" and Symbiotic Intelligence's mutual-adaptation model. We formalize the mechanism for effective teaming as a causal chain: Explainable AI (XAI) -\u003e co-adaptation -\u003e shared mental models (SMMs). A meta-analytic ``performance paradox\" is then examined: human-AI teams tend to show negative synergy in judgment/decision tasks (underperforming AI alone) but positive synergy in content creation and problem formulation. We trace failures to the algorithm-in-the-loop dynamic, aversion/bias asymmetries, and cumulative cognitive deskilling. We conclude with a unifying framework--combining extended-self and dual-process theories--arguing that durable gains arise when AI functions as an internalized cognitive component, yielding a unitary human-XAI symbiotic agency. This resolves the paradox and delineates a forward agenda for research and practice.","short_abstract":"This paper offers a concise, 60-year synthesis of human-AI collaboration, from Licklider's ``man-computer symbiosis\" (AI as colleague) and Engelbart's ``augmenting human intellect\" (AI as tool) to contemporary poles: Human-Centered AI's ``supertool\" and Symbiotic Intelligence's mutual-adaptation model. We formalize the...","url_abs":"https://arxiv.org/abs/2601.06030","url_pdf":"https://arxiv.org/pdf/2601.06030v1","authors":"[\"Richard Jiarui Tong\"]","published":"2025-11-07T19:11:33Z","proceeding":"cs.HC","tasks":"[\"cs.HC\",\"cs.AI\"]","methods":"[]","has_code":false}