Journal Paper Published

Abstract

Drawing from the established human-AI collaboration framework (Mosqueira-Rey et al., 2023), this study investigated how L2 teachers at a research-intensive university collaborated with Generative AI chatbots to provide feedback on tertiary-level written assignments over 9 weeks. Data from thirteen teachers included 278 interactions with GenAI, 876 feedback instances, and reflective accounts of their experiences. Findings revealed three Teacher-GenAI collaboration modes—embedding (human-led), copilot (interactive), and agent (GenAI-led)—with a MANOVA indicating that rubric dimensions significantly influenced their frequency of use. Post hoc comparisons indicated that teachers used the agent and copilot modes more frequently in the “language use” dimension. Conversely, the embedding mode was used most frequently in the “content” and “overall evaluation” dimensions. Although the agent mode reduced teachers' workload in providing feedback on mistakes at the micro level, the embedding mode demanded more effort from teachers to give feedback at the macro level of students' assignments. Guided by Ertmer's framework (1999) on the barriers to educational technology integration, we found that teachers tried to claim more control over human-AI collaboration by adopting embedding and copilot modes for four concerns: GenAI agents' misalignment with task requirements, inconsistency with external expectations, inadequacy in addressing students' emotional needs, and cautious attitudes toward technology.

Keywords

collaborative strategies, GenAI chatbot, human-AI collaboration, LLM Chatbot, teacher feedback, written corrective feedback (WCF)