Behavioral Intention Analysis of AI Use in Academic Writing: Implementing the UTAUT Model among English Education Students in Jambi

Abstract

This study examines English as a Foreign Language (EFL) students’ behavioral intention to adopt Artificial Intelligence (AI) applications in academic writing using the Unified Theory of Acceptance and Use of Technology (UTAUT). Using Partial Least Squares-Structural Equation Modeling (PLS-SEM), the results show that the model explains 51.6% of the variance in Behavioral Intention and 66.4% of the variance in Use Behavior. Social Influence (β = 0.23; T = 3.614; p < 0.001) and Facilitating Conditions (β = 0.505; T = 7.507; p < 0.001) significantly predict students’ intention, while Performance Expectancy (β = 0.069; T = 0.778; p > 0.05) and Effort Expectancy (β = 0.011; T = 0.156; p > 0.05) do not show significant influence. Behavioral Intention strongly predicts actual AI use (β = 0.65; T = 7.985; p < 0.001), supported by adequate technological access (β = 0.217; T = 2.611; p = 0.005). Students tend to rely on AI for idea generation and language refinement but remain concerned about ethical issues that may affect academic integrity, indicating the need for institutional guidance and responsible AI literacy in higher education.

Keywords: Generative AI ; Technology Acceptance ;  Social Influence ; Higher Education ; AI Literacy ; Ethical Academic Practice