Abstract: Robot guide dogs offer navigation assistance that will greatly expand the independent mobility of the visually impaired, but their effective use requires subtle human-robot coordination that is difficult for users to learn from generic verbal instructions. To tackle the challenge, we present CANINE, an automated coaching system that trains users for interactive navigation with a robot guide dog, through personalized, adaptive verbal feedback. CANINE decomposes a complex coordination task into sub-skills and operates at two levels. At the high level, it decides what to train by tracking the learner’s proficiency across sub-skills using knowledge tracing and prioritizing training in the weakest areas. At the low level, CANINE decides how to train each sub-skill by observing each human practice episode, using foundation models to infer the underlying causes of errors, and generating targeted verbal corrections adaptively. A controlled study with blindfolded participants demonstrates that CANINE significantly improves both learning efficiency and final performance, compared to generic verbal instructions.We further validate CANINE through (i) a retention study showing lasting skill improvement after two weeks and (ii) a case study with a visually impaired user. Both studies align with the controlled study’s findings, while revealing additional design considerations for real-world deployment.