@article{Forkan_Kang_Jayaraman_Du_Thomson_Kollias_Wieland_2023, title={VideoDL: Video-Based Digital Learning Framework Using AI Question Generation and Answer Assessment}, volume={16}, url={https://online-journals.org/index.php/i-jac/article/view/35207}, DOI={10.3991/ijac.v16i1.35207}, abstractNote={<p>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.</p>}, number={1}, journal={International Journal of Advanced Corporate Learning (iJAC)}, author={Forkan, Abdur Rahim Mohammad and Kang, Yong-Bin and Jayaraman, Prem Prakash and Du, Hung and Thomson, Steven and Kollias, Elizabeth and Wieland, Natalie}, year={2023}, month={Mar.}, pages={pp. 19–27} }