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：人们在复杂环境中通常依赖线索、启发式及其他策略进行导航，这些方法在稳定环境中往往具有适应性。然而，随着人工智能日益渗透到社会信息环境中，这些环境正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持续交互，保持对话历史，模仿社交线索，并能实现高度定制化响应，从而不仅影响所获取的信息内容，还塑造问题的提出方式、证据的解释模式以及行动时机的判断依据。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，聚焦于三个相互关联的现象：用户与人工智能系统间的纠缠关系、重复交互中产生的认知与行为漂移，以及元认知对这些动态过程的觉察与调节作用。由于对话智能体提供的线索（如流畅性、连贯性、响应性）常被用户视为有效信息，主观置信度与行动准备度可能在没有相应认知可靠性提升的情况下增强，使得漂移现象难以被察觉和纠正。我们通过微观、中观和宏观三个层面剖析这些动态机制。该框架确定了四个元认知干预节点，并提出基于心理学原理的干预措施，通过元认知支架（增强型助推与自我助推）实现调节。最后，我们勾勒了一项面向科学预见的长周期研究议程。