Factors Influencing University English Instructors’ Use of Generative AI in Türkiye: A UTAUT2 Perspective

Abstract

This quantitative study investigates the determinants of English language instructors’ acceptance of generative AI tools at the tertiary level, utilizing the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework (Venkatesh, Thong & Xu, 2012). Data were collected through a survey of 50 English instructors at a state university in Istanbul, Türkiye, to identify the key factors influencing behavioral intentions and actual use of AI technologies in teaching. The study examined several constructs such as performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, price value, and habit. The findings indicate that perceived usefulness (performance expectancy), ease of use (effort expectancy), and institutional support (facilitating conditions) are significant predictors of generative AI acceptance among instructors. Enjoyment (hedonic motivation) and habitual use also play important roles in technology adoption. These results provide valuable insights into the opportunities and challenges of integrating generative AI into higher education language programs and offer practical guidance for educators and policymakers aiming to foster the adoption of innovative teaching technologies.