Speaker
Description
Feedback plays a critical role in education by providing learners with information about their current performance and guiding them toward improvement. With the rapid advancement of deep learning and natural language processing (NLP) technologies, artificial intelligence (AI) can now offer personalized and immediate feedback in writing education. This capability not only alleviates instructors’ workload but also enhances accessibility and timeliness of feedback. However, the effectiveness of AI-generated feedback depends not solely on its technical sophistication, but on how learners perceive, interpret, and utilize it. Cognitive readiness, emotional receptivity, and self-efficacy are key factors that influence feedback utilization. Grounded in constructivist learning theory, which emphasizes learners as active agents in interpreting and applying feedback, this study investigates how learners engage with AI feedback in English writing tasks. Specifically, it examines learners’ initial writing proficiency, self-efficacy, and trust in AI feedback affect their feedback usage behavior. The research involved approximately 20 university students with prior experience using AI-based feedback systems for at least two writing essay assignments. The findings highlight that AI feedback alone does not guarantee improvement; rather, learner perception, motivation, and contextual guidance from instructors are crucial. This study provides practical implications for AI feedback system design, including the need for emotionally sensitive language, contextual personalization, and educator-facilitated feedback interpretation. Future research should explore longitudinal effects and diverse text genres to further validate AI feedback’s pedagogical impact.
Biography
An associate professor in the department of business English at Daejeon University. Main research interests are English teaching methodology and corpus based learning.
