AI根据用户状态动态调整辅助方式，避免干扰专注力。
Navigating the State of Cognitive Flow: Context-Aware AI Interventions for Effective Reasoning Support
- 通过眼动、打字停顿等行为数据判断用户状态
- 实现个性化、低干扰的实时认知支持
- 适合需要深度思考的复杂决策场景
心流理论描述了当任务难度与个人技能水平匹配时，个体所体验到的深度专注与内在动机状态。在人工智能增强的推理过程中，不当的干预可能破坏这种心流状态，反而损害决策质量。本文提出一种情境感知的认知增强框架，依据类型、时机和规模三个关键情境因素动态调整干预策略。通过分析多模态行为线索（如注视行为、打字停顿、交互速度），AI可实时调节认知支持以维持或恢复心流。我们引入‘认知心流’概念，作为人工智能增强推理中对心流理论的扩展，强调干预应个性化、自适应且最小侵入性。通过将静态干预转变为情境感知的增强机制，该方法确保人工智能系统在不打断认知沉浸的前提下，支持复杂决策与推理中的深度参与。
原文摘要 · Abstract (English)
Flow theory describes an optimal cognitive state where individuals experience deep focus and intrinsic motivation when a task's difficulty aligns with their skill level. In AI-augmented reasoning, interventions that disrupt the state of cognitive flow can hinder rather than enhance decision-making. This paper proposes a context-aware cognitive augmentation framework that adapts interventions based on three key contextual factors: type, timing, and scale. By leveraging multimodal behavioral cues (e.g., gaze behavior, typing hesitation, interaction speed), AI can dynamically adjust cognitive support to maintain or restore flow. We introduce the concept of cognitive flow, an extension of flow theory in AI-augmented reasoning, where interventions are personalized, adaptive, and minimally intrusive. By shifting from static interventions to context-aware augmentation, our approach ensures that AI systems support deep engagement in complex decision-making and reasoning without disrupting cognitive immersion.
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