Advances in autonomous driving provide an opportunity for AI-assisted driving instruction that directly addresses the critical need for human driving improvement. How should an AI instructor convey information to promote learning? In a pre-post experiment (n = 41), we tested the impact of an AI Coach's explanatory communications modeled after performance driving expert instructions. 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). We compare how different explanatory techniques impact driving performance, cognitive load, confidence, expertise, and trust via observational learning. Through interview, we delineate participant learning processes. Results show AI coaching can effectively teach performance driving skills to novices. We find the type and modality of information influences performance outcomes. Differences in how successfully participants learned are attributed to how information directs attention, mitigates uncertainty, and influences overload experienced by participants. Results suggest efficient, modality-appropriate explanations should be opted for when designing effective HMI communications that can instruct without overwhelming. Further, results support the need to align communications with human learning and cognitive processes. We provide eight design implications for future autonomous vehicle HMI and AI coach design.

翻译：自动驾驶技术的进步为人工智能辅助驾驶教学提供了契机，直接应对了提升人类驾驶技能的迫切需求。人工智能教练应如何传递信息以促进学习？在一项前测-后测实验中（n = 41），我们测试了模拟高性能驾驶专家指导方式的人工智能教练解释性沟通的影响。参与者被分为四组，以评估人工智能教练解释的两个维度：信息类型（“是什么”与“为什么”类解释）和呈现模态（听觉与视觉）。我们通过观察学习比较了不同解释技术如何影响驾驶性能、认知负荷、信心、专业技能和信任。通过访谈，我们描绘了参与者的学习过程。结果表明，人工智能教练能有效向新手传授高性能驾驶技能。我们发现信息的类型和模态会影响绩效结果。参与者学习成效的差异归因于信息如何引导注意力、缓解不确定性以及影响参与者所经历的认知超负荷。研究结果表明，在设计能够有效指导而不致信息过载的人机交互通信时，应选择高效且模态适配的解释方式。此外，结果支持了沟通设计需与人类学习及认知过程相契合的必要性。我们为未来自动驾驶车辆人机交互及人工智能教练设计提出了八项设计启示。