![[2602.22814] When Should an AI Act? A Human-Centered Model of Scene, Context, and Behavior for Agentic AI Design](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.22814] When Should an AI Act? A Human-Centered Model of Scene, Context, and Behavior for Agentic AI Design
Summary
This article presents a human-centered model for agentic AI design, focusing on when AI should act based on contextual understanding and user behavior.
Why It Matters
As AI systems become more proactive, understanding the context in which they operate is crucial for effective and ethical interventions. This model provides a framework for designing AI that respects user agency and enhances interaction quality, addressing a significant gap in current AI design practices.
Key Takeaways
- The proposed model integrates scene, context, and human behavior factors to guide AI actions.
- Five design principles are outlined to ensure AI interventions are contextually sensitive and appropriate.
- The model emphasizes the importance of user agency and motivational calibration in AI interactions.
- Understanding user-constructed meaning is essential for effective AI behavior.
- This framework can help mitigate risks associated with AI acting without proper judgment.
Computer Science > Artificial Intelligence arXiv:2602.22814 (cs) [Submitted on 26 Feb 2026] Title:When Should an AI Act? A Human-Centered Model of Scene, Context, and Behavior for Agentic AI Design Authors:Soyoung Jung, Daehoo Yoon, Sung Gyu Koh, Young Hwan Kim, Yehan Ahn, Sung Park View a PDF of the paper titled When Should an AI Act? A Human-Centered Model of Scene, Context, and Behavior for Agentic AI Design, by Soyoung Jung and 5 other authors View PDF Abstract:Agentic AI increasingly intervenes proactively by inferring users' situations from contextual data yet often fails for lack of principled judgment about when, why, and whether to act. We address this gap by proposing a conceptual model that reframes behavior as an interpretive outcome integrating Scene (observable situation), Context (user-constructed meaning), and Human Behavior Factors (determinants shaping behavioral likelihood). Grounded in multidisciplinary perspectives across the humanities, social sciences, HCI, and engineering, the model separates what is observable from what is meaningful to the user and explains how the same scene can yield different behavioral meanings and outcomes. To translate this lens into design action, we derive five agent design principles (behavioral alignment, contextual sensitivity, temporal appropriateness, motivational calibration, and agency preservation) that guide intervention depth, timing, intensity, and restraint. Together, the model and principles provide a foundati...
