Affordances and Constraints of AI-Generated Writing Feedback
TL;DR
The findings suggest that AI-generated feedback supported targeted revision when it is accessible, interpretable, and aligned with classroom assessment criteria.
Abstract
This case study examines how middle school students in a rural, Indigenous-serving school engaged with artificial intelligence (AI)-generated writing feedback during a classroom journal-writing activity. Drawing on think-aloud sessions with four focal students, supplemented by researcher observation notes and brief post-activity student and teacher surveys, the study focuses on observable patterns in how students engaged with, responded to, and used automated feedback under real classroom conditions. Analysis identified four interrelated dimensions: (1) AI feedback as actionable guidance for revision, (2) rubric-aligned feedback as a structure for focused revision, (3) usability and access constraints as central conditions shaping engagement, and (4) variation in students’ use of interactive AI features and in their experiences of feedback readability. The findings suggest that AI-generated feedback supported targeted revision when it is accessible, interpretable, and aligned with classroom assessment criteria. At the same time, technical disruptions, feedback length, linguistic complexity, and local infrastructural conditions significantly shaped students’ uptake of the tool. By focusing on classroom implementation rather than performance outcomes, this study contributes empirically grounded insights into the situated affordances and limitations of AI-supported writing feedback in an underrepresented K-12 context.