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.

Questo articolo esplora i giri e le loro implicazioni.

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