Wei Baoyi1, Leow Min Hui2
1.New Era University College; 2.Universiti Teknologi MARA
Abstract：
Generative artificial intelligence (Generative AI) is rapidly entering university learning through tools for drafting, searching, coding, translation, and study planning. Alongside gains in efficiency and personalization, universities face governance risks: academic integrity erosion, uncritical cognitive offloading, privacy and data leakage, and widening digital inequities. This paper integrates the Technology Acceptance Model (TAM) and the Stimulus–Organism–Response (SOR) model to propose a closed-loop teaching-management approach that connects institutional stimuli, learner psychological states, and observable learning behaviors. We argue that universities can shape perceived usefulness (PU) and perceived ease of use (PEOU), trust, and risk perception by designing actionable stimuli (rules, assessment, teacher guidance, and platform safeguards), thereby shifting Generative AI from “output substitution” to a “learning scaffold” centered on verification, reflection, disclosure, and process evidence. Drawing on typical conditions in Chinese higher education and the national education digitalization agenda, we present an implementation scheme spanning governance rules, course assessment redesign, literacy development, compliant platforms, integrity and risk control, equity safeguards, and continuous evaluation.
Key Words：
TAM; SOR; generative Artificial Intelligence; AI-assisted learning
