Enhancing Operating System Education With a Generative AI‐Supported Boppps Model: An Empirical Study

ABSTRACT As generative artificial intelligence (GAI) continues to advance, its integration into engineering education offers new opportunities to enhance student's engagement and comprehension in complex subjects. This study proposes a GAI‐supported BOPPPS instructional model (G‐BOPPPS), which embeds GAI tools into six stages of the traditional BOPPPS to foster active learning. The model incorporates adaptive goal setting, automated assessment generation, interactive learning activities, and data‐driven feedback mechanisms. A quasi‐experimental study was conducted in an undergraduate Operating System course involving cohorts from 2020 to 2022. The results indicate that the G‐BOPPPS model significantly improved the academic performance of the students. A post‐course survey revealed that more than 90% of the students perceived GAI to be beneficial for enhancing participation, conceptual understanding, and self‐directed learning. The follow‐up interviews further demonstrated that GAI integration facilitated the shift of the learning mechanism from passive knowledge reception to active knowledge construction. This study provides an empirical framework for embedding GAI within student‐centered instructional models, which is applicable to the education of computer science and other engineering disciplines.