BLENDED CORRECTIVE FEEDBACK: THE IMPACT OF AI TOOL USAGE ON LEARNING MOTIVATION AND EFL WRITING PERFORMANCE

Abstract

As information and communication technology (ICT) advances at a rapid pace in the twenty-first century, the use of AI writing assistant tool presents a promising solution to address deficiencies in EFL students’ writing ability. A blended corrective feedback (BCF) mode has thus been developed to enhance English as a foreign language (EFL) students’ writing outcomes by integrating automated corrective feedback (ACF) and face-to-face feedback (FFF). However, some researchers question the efficacy of the BCF learning mode. For example, the ACF system does not address the basic literary constructs such as content, coherence, or structure. Furthermore, traditional face-to-face collaborative learning does not direct students/peers to think, to use knowledge, or to solve problems effectively. Given the lack of agreement on the potential efficacy of BCF application, the purpose of this study was to develop a BCF mode that combined the elements of ACF and FFF in the EFL writing process, as well as to investigate whether the BCF intervention could improve students' English writing performance and whether EFL students were more satisfied in the BCF learning environment. The pretest/posttest experimental design was used in this study, which was conducted in two freshman writing classes in Taiwan. The results revealed that implementing the BCF intervention significantly improved EFL students’ writing scores; however, no significant differences in word length, sentence length, or readability score were discovered. ACF and FFF learning were found to have significant relationships with BCF learning motivation. Surprisingly, no significant relationships were discovered between ACF, FFF, and BCF learning motivation and students' writing performance. Based on the findings of the investigation, pedagogical implications, limitations, and future research directions were identified and discussed.