Promoting revision in L2 writing: A mixed-methods study on the role of specialized AI feedback agents
TL;DR
Writing Helper is developed, a multi-agent system that structures iterative revision through four specialized feedback agents (Grammar, Style, Structure, Content) and gamified mastery thresholds, highlighting the importance of pedagogical interface design in L2 writing classrooms.
Abstract
Standard generative AI interfaces allow L2 learners to bypass cognitively demanding writing processes by generating complete texts with minimal effort. This study examines whether embedding pedagogical constraints within AI interface design can reshape learner engagement with the revision process in L2 writing. We developed Writing Helper, a multi-agent system that structures iterative revision through four specialized feedback agents (Grammar, Style, Structure, Content) and gamified mastery thresholds. In a quasi-experimental study, 45 Korean university EFL learners completed a creative writing task using either standard AI chatbots (Control, n = 18) or the agentic AI system (Experimental, n = 27). Behavioral analysis revealed that control participants followed largely linear workflows (M = 2.1 iterations), while experimental participants engaged in significantly more revision cycles (M = 6 iterations), producing longer texts (M = 150 vs. 48 words) with higher ratings for narrative elaboration. Qualitative findings indicated that learners valued the structured revision process and expressed willingness to reuse the system, yet reported that higher-order feedback lacked specificity, and inconsistent scoring raised concerns about trust. These findings reveal both the promise and challenges of using constraint-based, agentic AI design in L2 writing classrooms, highlighting the importance of pedagogical interface design.