Applying the Technology Acceptance Model to Generative AI Usage in EFL Academic Writing Classes at a Korean University
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This study applied the principles of the technology acceptance model (TAM) to the use of generative AI in EFL academic writing courses at a Korean university. Quantitative data was collected from 111 participants through the use of a questionnaire. In addition, qualitative data was gathered through the use of a limited number of short answers questions which were included as part of the participants' homework assignments. Quantitative data was analyzed through the use of both inferential and descriptive statistics, and qualitative responses were analyzed using a qualitative thematic review. Both sets of data were examined holistically to identify patterns and trends in relation to the TAM. The results indicate participants use generative AI frequently, and they feel it is both useful and easy to use with a moderately strong correlation between both constructs. However, this is often dependent on certain societal elements (peer usage; validation from instructors; trust). Furthermore, certain emotive factors (guilt; pleasure) can also be influential. Finally, the desire for generative AI to replace these types of courses in the future remains unclear despite an overall perception that this technology is going play an ever-increasing role in this educational context. Administrational and educational implications are also discussed.
Abstract
This study applied the principles of the technology acceptance model (TAM) to the use of generative AI in EFL academic writing courses at a Korean university. Quantitative data was collected from 111 participants through the use of a questionnaire. In addition, qualitative data was gathered through the use of a limited number of short answers questions which were included as part of the participants' homework assignments. Quantitative data was analyzed through the use of both inferential and descriptive statistics, and qualitative responses were analyzed using a qualitative thematic review. Both sets of data were examined holistically to identify patterns and trends in relation to the TAM. The results indicate participants use generative AI frequently, and they feel it is both useful and easy to use with a moderately strong correlation between both constructs. However, this is often dependent on certain societal elements (peer usage; validation from instructors; trust). Furthermore, certain emotive factors (guilt; pleasure) can also be influential. Finally, the desire for generative AI to replace these types of courses in the future remains unclear despite an overall perception that this technology is going play an ever-increasing role in this educational context. Administrational and educational implications are also discussed.