AI adoption of EFL instructors in a turkish tertiary bridging program: a mixed methods study

Abstract

This study investigates the adoption of artificial intelligence (AI) by English language instructors in a tertiary bridging program at a Turkish foundation university. It also aims to examine instructors’ perceptions across diverse demographic characteristics to determine whether these may influence AI adoption. Additionally, the study seeks to explore instructors’ in-depth perceptions and experiences of AI adoption in their teaching practices. Using an explanatory sequential mixed-methods design (QUAN → qual), the study first administered a validated online questionnaire, based on Chatterjee & Bhattacharjee’s (2020) AI adoption model, to 51 instructors. Descriptive analysis was used to examine overall trends, while inferential analysis was conducted to investigate whether demographic characteristics influenced AI adoption. This was followed by one semi-structured interview with each of eight participants to gain deeper insights. The most significant findings revealed that instructors were largely positive in their attitudes toward AI adoption, expressing a sense of cautious optimism regarding its use as a tool for their teaching purposes. Overall, demographic factors played a limited role; however, teaching experience and English teaching qualifications were found to significantly impact instructors’ Effort Expectancy. A significant difference was also found between age groups regarding the Facilitating Conditions for AI Use and between English teaching qualifications and Behavioral Intentions. When combined with interview insights, the findings suggest that demographic variables may still hold contextual relevance, possibly informing stakeholders in higher education in facilitating AI adoption in tertiary bridging programs. This could include facilitating resource access, clearly communicating AI policies, and providing focused English teaching.This study investigates the adoption of artificial intelligence (AI) by English language instructors in a tertiary bridging program at a Turkish foundation university. It also aims to examine instructors’ perceptions across diverse demographic characteristics to determine whether these may influence AI adoption. Additionally, the study seeks to explore instructors’ in-depth perceptions and experiences of AI adoption in their teaching practices. Using an explanatory sequential mixed-methods design (QUAN → qual), the study first administered a validated online questionnaire, based on Chatterjee & Bhattacharjee’s (2020) AI adoption model, to 51 instructors. Descriptive analysis was used to examine overall trends, while inferential analysis was conducted to investigate whether demographic characteristics influenced AI adoption. This was followed by one semi-structured interview with each of eight participants to gain deeper insights. The most significant findings revealed that instructors were largely positive in their attitudes toward AI adoption, expressing a sense of cautious optimism regarding its use as a tool for their teaching purposes. Overall, demographic factors played a limited role; however, teaching experience and English teaching qualifications were found to significantly impact instructors’ Effort Expectancy. A significant difference was also found between age groups regarding the Facilitating Conditions for AI Use and between English teaching qualifications and Behavioral Intentions. When combined with interview insights, the findings suggest that demographic variables may still hold contextual relevance, possibly informing stakeholders in higher education in facilitating AI adoption in tertiary bridging programs. This could include facilitating resource access, clearly communicating AI policies, and providing focused English teaching.