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.

本研究解决了在人工智能增强推理中干预措施干扰认知流状态的问题。提出了一种情境感知的认知增强框架，通过根据类型、时机和规模三大关键情境因素动态调整干预措施，确保支持者保持深度参与。研究成果显示，该框架能够个性化、适应性强且对认知沉浸的干扰最小，显著提升复杂决策和推理中的认知流状态。

认知流状态的导航：用于有效推理支持的情境感知人工智能干预