The proliferation of educational videos on the Internet has changed the educational landscape by enabling students to learn complex concepts at their own pace. Our work outlines the vision of an automated tutor – a multimodal question answering (QA) system to answer questions from students watching a video. This can make doubt resolution faster and further improve learning experience. In this work, we take first steps towards building such a QA system. We curate and release a dataset named EduVidQA, with 3,158 videos and 18,474 QA-pairs. However, building and evaluating an educational QA system is challenging because (1) existing evaluation metrics do not correlate with human judgments, and (2) a student question could be answered in many different ways, training on a single gold answer could confuse the model and make it worse. We conclude with important research questions to develop this research area further.