Understanding the complexities and potential of artificial intelligence (AI) in autonomous driving presents unique educational challenges, particularly for newcomers to the field. Building on the success of our initial workshop, we introduce an expanded version, designed to address both educational and research objectives, through an enhanced, proposed architecture. This proposed architecture provides participants with immersive, hands-on experience in supervised learning specifically tailored for autonomous driving (AD). It supports continuous, high-quality data collection in diverse driving scenarios—key for training and advancing behaviour-based End-to-End (E2E) learning models. Initially built around a static, onsite model vehicle track, the workshop now incorporates an innovative online simulator, allowing dynamic driving conditions and configurable track designs. This transition facilitates comprehensive, data-driven exploration of varied driving behaviours, addressing core training needs in this domain. Held multiple times with resounding success, the workshop has received overwhelmingly positive feedback and significant interest at major AI exhibitions. Through this proposed architecture, we address education and training challenges in AD, equipping participants with critical skills in E2E learning models and dynamic data collection methods essential for advancing scientific research and practical application in the field.