Generative artificial intelligence (Generative AI), together with AI-enabled tutoring and learning analytics tools, is rapidly reshaping learning practices in higher education; nevertheless, the mere availability of tools does not automatically translate into effective learning. Grounded in the Technology Acceptance Model (TAM) and the Stimulus–Organism–Response (SOR) framework, this paper constructs an explanatory chain linking perceived usefulness (PU) and perceived ease of use (PEOU) to attitudes, behavioral intentions, actual use, and subsequent learning behaviors. To enhance contextual sensitivity, the model further considers situational factors salient to women's universities, including gendered expectations, institutional culture, and technology accessibility. The study contributes (a) a structural integration of TAM and SOR, (b) a reusable conceptual scaffold—together with operationalizable constructs and an analysis pathway—appropriate for AI-in-education research, and (c) theoretical support for developing student-centered, ethically responsible strategies for integrating Generative AI into higher education.
Wei et al. (Thu,) studied this question.