Teaching clever heuristics is a promising approach to improvedecision-making. We can leverage machine learning to dis-cover clever strategies automatically. Current methods requirean accurate model of the decision problems people face inreal life. But most models are misspecified because of lim-ited information and cognitive biases. To address this prob-lem we develop strategy discovery methods that are robustto model misspecification. Robustness is achieved by model-ing model-misspecification and handling uncertainty about thereal-world according to Bayesian inference. We translate ourmethods into an intelligent tutor that automatically discoversand teaches robust planning strategies. Our robust cognitivetutor significantly improved human decision-making when themodel was so biased that conventional cognitive tutors were nolonger effective. These findings highlight that our robust strat-egy discovery methods are a significant step towards leverag-ing artificial intelligence to improve human decision-makingin the real world.