Metacognitive Regulation of L2 Writers in Response to AI and Human Genre-feedback
Dissertation Proposal Title: Metacognitive Regulation of L2 Writers in Response to AI and Human Genre-feedback
Dissertation Committee: Dr. Chris Tardy (Chair), Dr. Sandiway Fong, Dr. Janet Nicol, Dr. Jon Reinhardt, Dr. Raffaella Negretti (Special External Member, Chalmers University of Technology)
Abstract: The recent breakthroughs in artificial intelligence, in the form of Large Language Models (LLMs), pose new opportunities and challenges in second language (L2) instruction. This study aims to explore the effectiveness of LLMs, such as ChatGPT, as a pedagogical tool in L2 writing classrooms, where instructors have mixed perspectives due to the novelty of the technology and the scarce research into its implementation. By integrating the frameworks of metacognition (Flavell, 1979) and genre knowledge (Tardy et al, 2020), this study delves into how undergraduate L2 writers self-regulate their learning, when provided with formal and rhetorical feedback both from ChatGPT and from their human instructor.
In adopting a mixed-methods approach, this study will analyse students’ drafts, questionnaires, annotations, and reflections to not only assess the effectiveness of the feedback, but also understand students’ decision-making processes as they incorporate and prioritize feedback from both sources. This research is significant for at least two reasons. First, the findings can provide actionable insights and guidelines for L2 instructors who wish to supplement their feedback practices with AI tools. Second, and most important, in juxtaposing formal and rhetorical knowledge—two domains generally associated with machine and human expertise, respectively—this study pushes our understanding of the limits of AI and human cognition, and calls for more research into humans’ unique traits, such as metacognition. In doing so, it makes the case for the enduring value of human contribution in a world dominated by AI.
