From Augmentation to Symbiosis: A Review of Human-AI Collaboration Frameworks, Performance, and Perils

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) -> co-adaptation -> 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.