A PBL-Based Mini Course Module for Teaching Computer Science Students to Utilize Generative AI for Enhanced Learning

This research-to-practice paper introduces a mini-course module designed to teach computer science students how to interact more efficiently with Generative AI(GAI). The rapid rise of GAI is transforming education by providing students with easy access to knowledge and answers to their questions, acting as a personal tutor. Particularly in the field of computer science, where GAI can easily generate code based on specific requirements, many instructors struggle to prevent students from using tools like ChatGPT for completing assigned programming assignments and homeworks. However, we argue that 1) the use of GAI is inevitable, necessitating a redesign of courses so that students cannot merely rely on GAI without actual learning; and 2) students' learning can be enhanced if they learn to use GAI more effectively. In this paper, we demonstrate how we integrate Project-Based Learning to design the course module in a concise yet effective manner, which not only facilitates students' learning of GAI but also enriches their learning in relation to the host course where this mini-course module is embedded. In particular, the goal of this module is to teach CS students: 1) the basic principles and workflow of GAI; 2) Prompt Engineering: how to craft questions to interact more effectively with GAI; and 3) Extending GAI: how to create interactive tools by training customized GAI models. Designed to be completed within two weeks, the mini-course module can easily be incorporated into host courses. This mini-course module was integrated into a graduate-level Artificial Intelligence course with 42 students in Winter 2024. To assess the module's impact on student learning and engagement, we conducted pre- and post-course surveys as well as student interviews. The results from the surveys and interviews highlighted key areas for improving the design of educational modules to better teach essential GAI skills. These insights focused on enhancing student engagement and learning efficiency within a concise time frame.