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 J. 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.