Fix the Mind, Not the Move: Interpretable AI Assistance via Knowledge-Gap Localization

• URL: http://arxiv.org/abs/2606.05602v1
• Date: Thu, 04 Jun 2026 02:25:19 GMT
• Title: Fix the Mind, Not the Move: Interpretable AI Assistance via Knowledge-Gap Localization
• Abstract summary: We introduce SENSEI, a framework that infers user misconceptions from interaction behavior.<n>Our approach departs from action- or trajectory-level interventions by operating over a structured knowledge representation.<n>A user study shows that our method identifies real human misconceptions and provides effective guidance.
• Score: 11.210709234919237
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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-horizon tasks with diverse misconceptions and corresponding behaviors, SENSEI demonstrates zero-shot compositional generalization, disentangling multiple overlapping misconceptions despite training only on single-misconception cases. A user study further shows that our method identifies real human misconceptions and provides effective guidance that improves long-horizon task performance, successfully correcting $90\%$ of student misconceptions. Code and project page are available at https://misoshiruseijin.github.io/SENSEI/.

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