Learning on the move: Designing Adaptive Education for Automated Vehicles

Automated vehicles (AVs) could transform commute time into learning opportunities by freeing passengers from driving tasks. We conducted semi-structured interviews with 31 regular commuters, including educators and automotive experts, to identify design challenges and opportunities for AV-based learning. Using general inductive thematic analysis, we identified three key challenges: managing cognitive load and distraction, technology integration and privacy, and content design and interdisciplinary alignment. We also identified four autonomous vehicle-specific opportunities: journey-linked contextual learning, productive time utilization, controlled sensory environments, and automation-aware personalization. Our findings indicate that AV affordances, including real-time environmental data, private space, and extended uninterrupted time, create design requirements distinct from general mobile learning. We contribute design recommendations for adaptive focus management, context-aware content delivery, multi-modal interaction, and privacy-preserving personalization to guide future AV learning systems.