From teachers to chatbots: scaffolded corrective feedback and student trust in online L2 English classrooms

Teacher corrective feedback (TCF) plays a vital role in second language (L2) learning. Recent studies have examined feedback provided by both human teachers and large language models (LLMs). However, little is known about how students' trust differs toward scaffolded corrective feedback (SCF)—that is, feedback that incrementally progresses from indirect to direct during interaction—when it is provided by an LLM such as ChatGPT versus a language teacher. To address this gap, this study compared the effects of SCF, delivered by language teachers and ChatGPT, on L2 learning outcomes and student trust. Using a mixed-methods design, 40 lower-intermediate Iranian learners of English as a foreign language were randomly assigned to two conditions to receive scaffolded CF on English article usage from either a teacher or ChatGPT across four sessions. Learning gains obtained from immediate and delayed post-tests were analyzed using ANOVA and paired-sample t -tests, while semi-structured interviews and feedback interaction logs were examined using thematic analysis. Results showed that students in the teacher-delivered feedback group significantly outperformed those in the ChatGPT-delivered feedback group on both post- and delayed post-tests. Qualitative analyses suggested that this advantage stemmed from higher trust in the teacher, driven by the teacher's personalized emotional and technical support. The findings highlight that while ChatGPT can serve as a feedback tool in L2 instruction, its effectiveness depends on teacher mediation that attends to learners' individual differences and affective needs.