Research on AI Hybrid Teaching Framework for Principles of Operating Systems in Computing Majors

Abstract

Aiming at the prevalent teaching pain points of the Principles of Operating Systems course in contemporary computing majors—such as highly abstract and difficult theoretical concepts, a persistent disconnection between theoretical knowledge and practical application, a lack of personalized teaching methodologies, and the superficial application of modern educational technologies—this study systematically integrates artificial intelligence with hybrid teaching models. This integration is fundamentally based on the urgent talent cultivation demands of the modern digital economy and the strategic need for the independent controllability of domestic operating systems. Using a comprehensive mixed-methods approach that includes extensive literature research, detailed questionnaire surveys, and rigorous experimental comparisons, a robust four-layer integrated AI hybrid teaching framework is meticulously constructed. This innovative framework consists of an objective layer, a core layer, a support layer, and an evaluation layer, each featuring clear design principles, specialized core modules, and streamlined implementation processes. Verified through extensive teaching practice and empirical evaluation in higher education institutions, this framework successfully achieves the deep, meaningful integration of advanced AI technologies and traditional course teaching. The empirical results demonstrate that this approach can effectively improve students' overall academic performance, stimulate intrinsic learning interest, enhance engineering practical abilities, and foster comprehensive systematic thinking. Ultimately, this research provides highly replicable and promotable practical schemes, alongside robust theoretical references, for the ongoing teaching reform of core foundational courses in computing majors globally.