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/.

翻译：在人类与人工智能的协作中，AI助手通常通过行为反馈（例如辅助驾驶中的警报或方向盘提示）来纠正次优的人类行动。此类干预可缓解即时错误，但长期改进需要解决导致重复失误的根本性误解。我们提出SENSEI框架，该框架从交互行为推断用户误解，并提供针对性、最小且充分的建议以纠正这些误解。我们的方法超越了行动或轨迹层面的干预，通过结构化知识表征来定位并纠正错误行为的根源。在包含多样误解及相应行为的三个长周期任务中，SENSEI展现出零样本组合泛化能力，尽管仅基于单误解案例训练，仍能解耦多个重叠误解。用户研究进一步表明，我们的方法能识别真实的人类误解，并提供有效指导以提升长周期任务表现，成功纠正了90%的学生误解。代码及项目页面见 https://misoshiruseijin.github.io/SENSEI/。