Intelligent Tutoring in a Driving Simulator: Enhancing Driving Proficiency With AI-Driven Skill Assessment and Personalized Coaching Generation

This paper presents DriveCoach, an intelligent driving assistance and coaching system designed to strengthen safe driving skills through structured, learning-oriented intervention. The system combines risk assessment and adaptive assistance with a coaching-centered improvement cycle in which risk driving skills are diagnosed, addressed through real-time feedback, and reinforced via tailored post-drive coaching. Using the CARLA driving simulator, we conducted a mixed-method user study to evaluate DriveCoach across four representative driving skills: maintaining safe distance, responding to oncoming vehicles, handling adjacent vehicles, and negotiating intersections. Quantitative analyses demonstrated significant reductions in risk-related events when drivers received real-time assistance and notable improvements in post-coaching performance, indicating short-term skill retention and transfer. Complementary qualitative results revealed strong user acceptance and positive perceptions of the system's usability and coaching effectiveness. These findings highlight DriveCoach as a human-centered AI system that fosters safer, more reflective driving, contributing to the design of co-adaptive driver support systems that integrate behavioral assessment with personalized coaching.