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In a pre-post experiment (n = 41), we test the impact of an AI Coach'sexplanatory communications modeled after the instructions of human drivingexperts. Participants were divided into four (4) groups to assess two (2)dimensions of the AI coach's explanations: information type ('what' and'why'-type explanations) and presentation modality (auditory and visual). Wedirectly compare how AI Coaching sessions employing these techniques impactdriving performance, cognitive load, confidence, expertise, and trust in anobservation learning context. Through interviews, we delineate the learningprocess of our participants. Results show that an AI driving coach can beuseful for teaching performance driving skills to novices. Comparing betweengroups, we find the type and modality of information influences performanceoutcomes. We attribute differences to how information directed attention,mitigated uncertainty, and influenced overload experienced by participants.These, in turn, affected how successfully participants were able to learn.Results suggest efficient, modality-appropriate explanations should be optedfor when designing effective HMI communications that can instruct withoutoverwhelming. Further, they support the need to align communications with humanlearning and cognitive processes. Results are synthesized into eight designimplications for future autonomous vehicle HMI and AI coach design.

Learning Racing From an AI Coach: Effects of Multimodal Autonomous Driving Explanations on Driving Performance, Cognitive Load, Expertise, and Trust