Fix the Mind, Not the Move: Interpretable AI Assistance via Knowledge-Gap Localization
AI assistants in human-AI collaboration often correct suboptimal human actions through behavioral feedback (e.g., alerts or steering-wheel nudges in assistive driving). Such interventions can mitigate immediate errors, but long-term improvement requires addressing the underlying misconceptions that cause repeated mistakes. We introduce SENSEI, a framework that infers user misconceptions from interaction behavior and provides targeted, minimal yet sufficient suggestions to correct them. Our approach departs from action- or trajectory-level interventions by operating over a structured knowledge representation to localize and correct the sources of erroneous behavior. Across three long-horiz...
| Authors | Ayano Hiranaka, Ya-Chuan Hsu, Stefanos Nikolaidis et al. |
|---|---|
| Primary category | cs.AI |
| Published | 4 June 2026 |
| Last updated at source | 4 June 2026 |
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