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.

翻译：人们通过使用线索、启发式策略及其他方法在复杂环境中导航，这些策略在稳定情境下通常是适应性的。然而，随着人工智能日益渗透社会的信息环境，这些环境变得更具适应性和演化性：基于大语言模型的聊天机器人参与扩展性互动，维护对话历史，模仿社交线索，并能够超个性化定制回应，从而不仅影响信息获取的方式，还影响问题的框架、证据的解读以及行动时机的判断。本文提出一个可持续人机交互框架，该框架基于人类认知与人机交互的不变特征，并聚焦于三个相互关联的现象：用户与人工智能系统之间的纠缠、重复互动中认知与行为漂移的出现，以及元认知在觉察和调节这些动态中的作用。由于对话代理提供的线索（如流畅性、连贯性、响应性）被人类视为信息性信号，主观信心和行动准备度可能在缺乏相应认知可靠性提升的情况下增加，使得漂移难以察觉和纠正。我们从微观、中观和宏观层面描述这些动态。该框架识别出四个元认知干预点及心理学启发的干预措施，这些措施提供元认知支架（助推与自我助推）。最后，我们概述了一个面向科学前瞻的长期研究议程。