Determinants of ChatGPT adoption for learning among students in Thailand’s private higher education sector: An integrated TAM–UGT approach

Abstract

Generative AI, particularly ChatGPT, has found its way into how students search for information, write, and revise their academic work. This research raised the question of what shapes students’ intention to use ChatGPT, based on two technology adoption theories: the Technology Acceptance Model and Uses and Gratifications Theory. Five factors, perceived value, attitude toward use, academic content creation, information seeking, and novelty, were assessed to predict behavioral intention. A sample of 500 undergraduate and graduate students at private universities in Thailand was surveyed. Content validity was assessed using the Item-Objective Congruence index, and the instrument was piloted with 50 respondents (Cronbach’s α > 0.80). Confirmatory factor analysis confirmed measurement quality, and structural equation modeling tested the hypotheses. Four of the five factors showed significant relationships: academic content creation had the strongest effect (β = 0.44, p < 0.001), followed by information seeking (β = 0.38, p < 0.001), perceived value (β = 0.20, p < 0.001), and novelty (β = 0.12, p < 0.05); attitude toward use did not show a significant relationship (β = 0.01, p > 0.05). In practical terms, students could achieve their academic goals through the use of ChatGPT, which highlighted their willingness to use it more than their feelings about its use. The study debates that higher education needed to provide guidance for the integration of learning and using Generative AI in a response of information literacy and efficient tools used rather than promoting the attitude in accordance with the motivation based on gratification model.