Factors Influencing Satisfaction and Continued Use Intention of ChatGPT in the Academic Context: Analysis Using Structural Equation Modeling

Artificial intelligence tools like ChatGPT have transformed higher education by facilitating academic tasks and improving autonomous learning. However, their acceptance and continued use depend on factors such as compatibility, efficiency, satisfaction, and intention to use. This study applies Structural Equation Modeling (SEM) to evaluate these relationships. To analyze how these factors influence user satisfaction and continued use intentions of ChatGPT among university students. Study involved 210 students from Ecuadorian universities. Validated surveys were used to assess six constructs: compatibility, efficiency, perceived ease of use, perceived usefulness, satisfaction, and continued use intention. Data were analyzed using Exploratory and Confirmatory Factor Analysis, followed by SEM for model adjustment. The findings identified a four-factor structure explaining 63% of the variance. Fit indices were acceptable (CFI = 0.876, SRMR = 0.064), with significant factor loadings (p<0.001). However, high correlations among factors suggested conceptual redundancy. ChatGPT is perceived as a useful, satisfying tool aligned with students' learning styles, promoting its continued adoption. Nonetheless, refining the factor structure could improve the model.