Sociodemographic predictors and usability perceptions explaining academic use intention of ChatGPT among university students in Ecuador - Caluña, Edgar Rolando Morales; Diaz, Dario Javier Cervantes; Caluña, Cristian Ismael Morales; Capelo, Fernando Xavier Altamirano (Frontiers in Education, 2025)
BackgroundThe rapid integration of artificial intelligence (AI) in higher education has transformed how students interact with academic content. ChatGPT, as a prominent AI-based language model, has been increasingly adopted by students to support learning tasks. However, the factors influencing its academic use intention remain underexplored in Latin American contexts.ObjectiveThis study aims to identify the sociodemographic and perceptual predictors that explain the academic use intention of ChatGPT among university students in Ecuador.MethodsA cross-sectional, analytical study was conducted with 210 students from seven Ecuadorian universities. Data were collected through a validated questionnaire encompassing six constructs: compatibility with students’ learning styles, efficiency, perceived ease of use, perceived usefulness, satisfaction, and intention of continued use. Descriptive statistics, exploratory factor analysis, binary logistic regression, and k-means clustering were performed using Python in Google Colab.ResultsThe logistic regression model revealed that perceived usefulness (OR = 2.37) and compatibility with learning style (OR = 1.87) were the most significant predictors of high academic use intention. Cluster analysis identified three user profiles: enthusiastic adopters, neutral users, and reluctant adopters. Sociodemographic factors showed limited predictive power.ConclusionStudents’ perceptions of the academic value and alignment of ChatGPT with their learning preferences are stronger predictors of usage intention than sociodemographic characteristics. These findings highlight the need for pedagogically aligned and inclusive AI integration strategies in higher education.