Effects of generative AI feedback and hybrid feedback in functional adequacy development in L2 academic writing

The present study explores how generative GenAI feedback and hybrid feedback affect different dimensions of text quality, as measured by Functional Adequacy (FA), in three versions of an academic writing task. 73 Chinese EFL students produced (1) an initial draft written without GenAI assistance, (2) a revision based on GenAI feedback, and (3) a further revision incorporating both human feedback and prompt training. The study first compares students’ FA scores across the three versions with traditional statistics. Then we use unconditional individual growth curve models (IGCM) that allow for variation among learners to trace changes in the four FA subdimensions: content, task requirements, comprehensibility, and coherence and cohesion. Results show significant improvements in overall FA scores and across all four subdimensions, with rapid initial gains followed by relatively slower but sustained progress. GenAI feedback effectively facilitated FA development in all dimensions, extending beyond surface-level error correction. Hybrid feedback provided added value for continued enhancement in content, task requirements, and coherence/cohesion. These findings offer both theoretical and practical insights for L2 writing research and pedagogy.