Sociodemographic predictors and usability perceptions explaining academic use intention of ChatGPT among university students in Ecuador

Bibliographic Data

ID22167662
AuthorsEdgar Rolando Morales Caluña (0000-0001-9545-1282, Universidad Estatal de Milagro, corresponding author), Dario Javier Cervantes Diaz (0009-0003-2791-4005, Universidad Estatal de Milagro), Cristian Ismael Morales Caluña (Universidad Técnica de Ambato), Fernando Xavier Altamirano Capelo (0000-0001-5138-6538, Escuela Superior Politécnica del Chimborazo)
Year2025
Volume10
Publication date2025-08-19
Open AccessYes
TypeARTICLE
VenueFrontiers in Education (JOURNAL)
Journal identifiersISSN: 2504-284X • E-ISSN: 2504-284X
PublisherFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/feduc.2025.1649034
OpenAlexW4413327806
LanguageEN
Citations received1
References cited32

Background The 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. Objective This study aims to identify the sociodemographic and perceptual predictors that explain the academic use intention of ChatGPT among university students in Ecuador. Methods A 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. Results The 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. Conclusion Students’ 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

Human–computer interaction · Medical education · Perception · Technology Acceptance Model · Usability · AI in Service Interactions · Applied Psychology · Computer Science · E-Learning and Knowledge Management · Medicine · Online Learning and Analytics · Psychology · Social Psychology

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