영어 쓰기에서 AI 피드백 학습활동이 대학생의 영어 성취도와 쓰기 동기에 미치는 영향
The effects of AI feedback learning activities in English writing on university students’ English achievement and writing motivation
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This study investigates how AI feedback learning activities in English writing classes affect university students’ English achievement and writing motivation. It examines both cognitive and affective outcomes in EFL contexts using generative AI tools such as ChatGPT. A total of 244 first-year university students completed four writing tasks incorporating AI feedback during the revision process. Pre- and post-tests were conducted along with the Oxford Placement Test (Syndicate, 2001) and a modified version of Payne’s (2012) Academic Writing Motivation Questionnaire. Quantitative analyses revealed significant improvement in English achievement and three motivational subcomponents—enjoyment, recognition, and effort—while instrumentality showed no significant change. Qualitative analysis of open-ended questionnaire responses indicated that students valued AI feedback for error detection, coherence, and self-directed learning, yet recognized limitations such as unnatural expressions, misinterpretations, and overreliance on AI. The findings highlight the importance of AI literacy and propose a hybrid feedback model integrating AI and teacher guidance to enhance learner autonomy and writing competence.
Abstract
This study investigates how AI feedback learning activities in English writing classes affect university students’ English achievement and writing motivation. It examines both cognitive and affective outcomes in EFL contexts using generative AI tools such as ChatGPT. A total of 244 first-year university students completed four writing tasks incorporating AI feedback during the revision process. Pre- and post-tests were conducted along with the Oxford Placement Test (Syndicate, 2001) and a modified version of Payne’s (2012) Academic Writing Motivation Questionnaire. Quantitative analyses revealed significant improvement in English achievement and three motivational subcomponents—enjoyment, recognition, and effort—while instrumentality showed no significant change. Qualitative analysis of open-ended questionnaire responses indicated that students valued AI feedback for error detection, coherence, and self-directed learning, yet recognized limitations such as unnatural expressions, misinterpretations, and overreliance on AI. The findings highlight the importance of AI literacy and propose a hybrid feedback model integrating AI and teacher guidance to enhance learner autonomy and writing competence.