Between these two poles, a new phenomenon has remained underexplored: what happens when an AI is not used in an isolated or sporadic way, but is instead integrated continuously into a healthy, stable,
Research gap analysis derived from 3 psychology papers in our local library.
The gap
Between these two poles, a new phenomenon has remained underexplored: what happens when an AI is not used in an isolated or sporadic way, but is instead integrated continuously into a healthy, stable, and meaningful human environment.
Consensus across the literature
Clustered from 3 gap mentions across 3 papers via embedding cosine ≥ 0.62.
Research trend
Established — well-defined area with open sub-problems.
Supporting evidence — 3 representative gaps
- AI, Cognition, and the Biosociotechnological Mesh: An Introductory Ontological and Strategic Framework for Understanding Postbiotic Cognition Systems, Their Impacts, and Systemic Risks (2026) · doi
Third, it remains unclear what replacement capacities AI-dependence creates in return. Whether AI-mediated cognition follows this pattern, or whether its coupling to biological reward systems and its speed of deployment introduce qualitatively different risks, remains an open empirical question. This leads to several predictable effects: • Ambiguity becomes aversive: the mind grows less tolerant of open questions, partial understanding, or slow-progress problem spaces.
Keywords: remains whether open third unclear replacement capacities dependence creates return mediated cognition follows pattern coupling - Relational Cognitive Consciousness in Human–AI Interaction (2026) · doi
Between these two poles, a new phenomenon has remained underexplored: what happens when an AI is not used in an isolated or sporadic way, but is instead integrated continuously into a healthy, stable, and meaningful human environment.
Keywords: poles phenomenon remained underexplored happens used isolated sporadic instead integrated continuously healthy stable meaningful human - Ironies of artificial intelligence (2023) · doi
Five ironies of AI are presented including difficulties with understanding AI and forming adaptations, opaqueness in AI limitations and biases that can drive human decision biases, and difficulties in understanding the AI reliability, despite the fact that AI remains insufficiently intelligent for many of its intended applications.
Keywords: difficulties understanding biases five ironies presented including forming adaptations opaqueness limitations drive human decision reliability
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