Boosting metacognition in entangled human-AI interaction to navigate cognitive-behavioral drift
🤖 AI Summary
This study addresses the subtle yet consequential cognitive-behavioral drift that emerges in users through prolonged interaction with highly adaptive AI systems, which undermines their judgment reliability. To tackle this issue, the authors propose an integrated “Entanglement–Drift–Metacognition” framework that synthesizes theories from cognitive psychology and human-computer interaction. By analyzing longitudinal behavioral data, the framework identifies entangled user–AI relationships and dynamic drift patterns, pinpointing four critical metacognitive intervention points. Building on this foundation, the work introduces psychologically grounded intervention mechanisms—such as metacognitive nudges and self-prompting strategies—to enhance users’ awareness of and capacity to regulate AI-induced cognitive shifts. This research provides both a theoretical foundation and actionable pathways for preserving cognitive reliability in human-AI collaboration.
Application Category
📝 Abstract
People navigate complex environments using cues, heuristics, and other strategies, which are often adaptive in stable settings. However, as AI increasingly permeates society's information environments, those become more adaptive and evolving: LLM-based chatbots participate in extended interaction, maintain conversational histories, mirror social cues, and can hypercustomize responses, thereby shaping not only what information is accessed but how questions are framed, how evidence is interpreted, and when action feels warranted. Here we propose a framework for sustained human-AI interaction that rests on invariant features of human cognition and human--AI interaction and centers on three interlinked phenomena: entanglement between users and AI systems, the emergence of cognitive and behavioral drift over repeated interactions, and the role of metacognition in the awareness and regulation of these dynamics. As conversational agents provide cues (e.g., fluency, coherence, responsiveness) that people treat as informative, subjective confidence and action readiness may increase without corresponding gains in epistemic reliability, making drift difficult to detect and correct. We describe these dynamics across micro-, meso-, and macro-levels. The framework identifies four metacognitive intervention points and psychologically informed interventions that provide metacognitive scaffolding (boosting and self-nudging). Finally, we outline a long-horizon research agenda for scientific foresight.
Problem
Research questions and friction points this paper is trying to address.
metacognition
cognitive-behavioral drift
human-AI interaction
entanglement
epistemic reliability
Innovation
Methods, ideas, or system contributions that make the work stand out.
metacognition
cognitive-behavioral drift
human-AI entanglement
metacognitive scaffolding
AI-mediated interaction
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