VideoDL: Video-Based Digital Learning Framework Using AI Question Generation and Answer Assessment

Abdur Rahim Mohammad Forkan, Yong-Bin Kang, Prem Prakash Jayaraman, Hung Du, Steven Thomson, Elizabeth Kollias, Natalie Wieland

Abstract: Assessing learners’ understanding and competency in video-based digital learning is time-consuming and very difficult for educators, as it requires the generation of accurate and valid questions from pre-recorded learning videos. This paper demonstrates VideoDL, a video-based learning framework powered by Artificial Intelligence (AI) that supports automatic question generation and answer assessment from videos. VideoDL comprises of various AI algorithms, and an interactive web-based user interface (UI) developed using the principles of human-centred design. Our empirical evaluation using real-world videos from multiple domains demonstrates the effectiveness of VideoDL.