A Model for Evaluating Computer-Related Curricula in Higher Education Using an AI-Converged Platform

As the primary vehicle for cultivating talent in computer science disciplines, the teaching evaluation system of computer-related curricula has long been plagued by persistent challenges: delayed feedback mechanisms, data uniformity constraints and static interaction paradigms. Aiming at this problem, this study proposes a three-phase technical framework encompassing platform convergence, model innovation and empirical validation. By integrating artificial intelligence (AI) with big data, we developed an integrated A-T-M (assessment, testing, measurement) model of teaching evaluation based on an AI-converged platform, and verified its effect in the Computer Network course. The empirical results demonstrate that our AI-enabled model of teaching evaluation has advantages in providing real-time behavioral feedback for instructors and learners and multidimensional observation of the teaching process. While acknowledging current limitations in insufficient transparency of evaluation indexes and personalized recommendation bias, our ongoing research aims to extend this framework into a long-cycle, multi-course generalized study in the future to promote teaching evaluation from result-oriented to process-oriented.