🤖 AI Summary
This study addresses a critical limitation in current autonomous agents—their frequent failure to appropriately determine when, why, and whether to intervene—by proposing an explanatory model that integrates scene context, situational factors, and human behavioral considerations. For the first time, this work bridges humanities and engineering perspectives to develop a user-meaning-centered framework for behavioral explanation. The model explicitly distinguishes between observable facts and the contextual meaning ascribed by users, leveraging this distinction to formulate timely and proportionate intervention strategies. Furthermore, the research articulates five design principles for behavior judgment in intelligent agents, establishing a theoretical model and corresponding design guidelines that enhance contextual sensitivity and behavioral reasoning. This approach significantly improves the appropriateness of AI interventions and user acceptance.
📝 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 foundation for designing agentic AI systems that act with contextual sensitivity and judgment in interactions.