Making Lecture Videos Accessible for Students who are Blind or have Low Vision through AI-Assisted Navigation and Visual Question Answering

Designing accessible lectures and lecture materials is crucial to promote inclusive higher education. We conducted need-finding interviews with 12 students who are blind or have low vision to learn their perspectives on how lectures and lecture material could become more accessible through Artificial Intelligence (AI) technologies. Key insights from the interviews reveal that students envision AI to automatically customize lecture material, connect disparate information sources, for example, to better keep track of the current lecture slide, and enhance interaction and engagement with lecture material. Based on these insights, we developed the LectureAssistant prototype, employing an iterative design process with visually impaired users that features AI-assisted video navigation and chatbot interaction. In a final evaluation with seven students, the participants expressed enthusiasm for features such as AI-powered video search and the possibility of asking questions about visual content in the current video frame. They provided valuable suggestions for future improvements, including notifications for lecture slide transitions and the provision of a short overview
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function for a slide. Insights from the study indicate great potential of the prototype to improve accessibility of lecture videos for students with visual impairments, although they also point to crucial areas for improvement, such as more reliable and personalized image descriptions.