Integrating Trust and Perceived Performance into the Expectation-Confirmation Model: A Mixed-Methods Study on Generative AI Persistence
), Anisa Yunita Sari(2)
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Country:
(1) Department of Information Systems, Universitas Negeri Surabaya, Indonesia
(2) Department of Early Childhood Teacher Education, Universitas Narotama, Indonesia
Corresponding Author
The rapid adoption of Generative AI in tertiary education has changed how students obtain, process, and assess learning information, but little is known about how satisfied they will be in the long term and the persistence intention towards such technologies. This research paper discusses why students were satisfied and wanted to continue using AI-based learning tools, according to the Expectation Confirmation Theory (ECT). A sequential mixed-methods design was used to collect quantitative data from 106 university students across Education, Engineering Computer Science, and Health Sciences majors. Quantitative data were analyzed using PLS-SEM, and an eventual semi-structured interview of eight subjects was used to validate the quantitative data. The findings suggest that all the variables of expectation, perceived performance, confirmation, and satisfaction are important predictors of continuance intention. However, perceived performance is the most effective predictor. There is a statistically significant but weak relationship between expectations and confirmation, and students' confirmation is likely influenced more by their experience with AI performance than by their initial expectations. These findings are also supported by qualitative evidence indicating that the reliability, contextual relevance, and trustworthiness of AI systems strongly impact student satisfaction and confidence in AI-based learning. The study highlights the significance of perceived performance and trust as key factors in maintaining the use of AI in education. In theory, it uses the Expectation Confirmation Theory, incorporating ethical awareness and reliability as contextual factors that affect satisfaction and continuance intention. In practice, this means that AI developers and teachers need to be more transparent about their algorithms, accurate, and ethically literate to build trust and foster meaningful interaction with AI in higher education.
Keywords: expectation confirmation theory, gen ai, student satisfaction, continuance intention, higher education.
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