User acceptance is essential for successfully introducing automated vehicles (AVs). Understanding the technology is necessary to overcome skepticism and achieve acceptance. This could be achieved by visualizing (uncertainties of) AV's internal processes, including situation perception, prediction, and trajectory planning. At the same time, relevant scenarios for communicating the functionalities are unclear. Therefore, we developed EduLicitto concurrently elicit relevant scenarios and evaluate the effects of visualizing AV's internal processes. A website capable of showing annotated videos enabled this methodology. With it, we replicated the results of a previous online study (N=76) using pre-recorded real-world videos. Additionally, in a second online study (N=22), participants uploaded scenarios they deemed challenging for AVs using our website. Most scenarios included large intersections and/or multiple vulnerable road users. Our work helps assess scenarios perceived as challenging for AVs by the public and, simultaneously, can help educate the public about visualizations of the functionalities of current AVs. •EduLicit–a method to elicit user-chosen driving scenarios and educate on automated vehicle functionalities and challenges.•Applying neural networks to scenario videos to visualize vehicles' detection, prediction, and trajectory planning functionalities.•Website implementation of EduLicit, where users upload in-the-wild driving videos for automated visualization and education.•User-chosen driving scenarios primarily include large intersections and/or multiple vulnerable road users.•Users perceive vulnerable road users as more unreliable, unpredictable, and harder to detect by sensors than vehicles.