Flipped Learning with AI-Enabled Support for CFD Fire Modelling Education: A Student Survey Case Study

Abstract

With the rapid development of AI technologies, digital software, and teaching methods, flipped-learning approaches are becoming increasingly popular for academic education. Next-generation teaching modules will potentially enhance the learning experiences of students. In this study, the impact and enhancement of students’ end-user experience in learning computational fluid dynamics (CFD) fire models have been evaluated based on course evaluation surveys. This paper will explore three key objectives of the proposed flipped-learning approach, including (i) the engagement and enhanced willingness of students’ self-adaptive learning; (ii) the effectiveness of pre-class study and practice towards students’ total understanding of the learning materials; and (iii) the overall quality of the delivery mode towards the learning outcomes. Additionally, the AI-enabled technology to convert legacy teaching materials to online-adaptive tutorials with visual-aided components will be discussed in this paper. Survey responses show generally positive perceptions of the flipped-learning format and AI-supported resources. The students’ feedback on new functions and their effectiveness in software skills acquisition will also be investigated.