Cognitive and Technological Factors Shaping Students’ Sustained Use of ChatGPT in Higher Education

Abstract

This study examines the cognitive and technological factors shaping students' sustained use of ChatGPT in Indonesian higher education. Despite the rapid adoption of generative Artificial Intelligence (AI) in education, a clear understanding of the factors sustaining continued engagement with such systems remains limited. While continuance intention has been widely examined, the application of the Expectation–Confirmation Model (ECM) in generative AI contexts remains underexplored. This gap is especially evident when considering the role of AI-specific system attributes in shaping post-adoption evaluations. Although ECM has been extended with various constructs in prior studies, the specific integration of AI characteristics, particularly perceived intelligence and anthropomorphism, has not been explored in generative AI use in education, especially within Indonesian higher education. To address this gap, a multi-theoretic framework integrating ECM and AI characteristics was developed. Data from 322 Indonesian students were analyzed using Partial Least Squares-Structural Equation Modeling. All ten hypotheses were supported, and the model explains 43.3% of the variance in continuance intention (R² = 0.433). Perceived Intelligence strongly influences Perceived Anthropomorphism with a path coefficient of 0.591, representing the strongest relationship in the model, while other paths demonstrate moderate or modest effects. The findings confirm ECM's robustness in generative AI settings and highlight the pivotal role of AI characteristics in shaping post-adoption evaluations and sustained use. These results contribute to the growing body of research on generative AI adoption in education by demonstrating how system intelligence and human-like interaction jointly influence continuance intention. The findings also offer practical guidance for AI developers to enhance system intelligence and natural interaction. Future research could explore how students experience AI over time and what shapes their sustained use using different research methods.