dorsal/arxiv
View SchemaFrom Augmentation to Symbiosis: A Review of Human-AI Collaboration Frameworks, Performance, and Perils
| Authors | Richard Jiarui Tong |
|---|---|
| Categories | |
| ArXiv ID | 2601.06030vv1 |
| URL | https://arxiv.org/abs/2601.06030 |
| License | http://creativecommons.org/licenses/by/4.0/ |
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) -> 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.
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"abstract": "This paper offers a concise, 60-year synthesis of human-AI collaboration, from Licklider\u0027s ``man-computer symbiosis\" (AI as colleague) and Engelbart\u0027s ``augmenting human intellect\" (AI as tool) to contemporary poles: Human-Centered AI\u0027s ``supertool\" and Symbiotic Intelligence\u0027s 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.",
"arxiv_id": "2601.06030",
"authors": [
"Richard Jiarui Tong"
],
"categories": [
"cs.HC",
"cs.AI"
],
"license": "http://creativecommons.org/licenses/by/4.0/",
"title": "From Augmentation to Symbiosis: A Review of Human-AI Collaboration Frameworks, Performance, and Perils",
"url": "https://arxiv.org/abs/2601.06030",
"version": "v1"
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