Cycles of drafting and revising are crucial for students’
writing growth, and feedback on drafts has been shown to improve
outcomes (Graham et al 2015). Research demonstrates that AI feedback is
comparable to human feedback (Steiss et. al., 2024), suggesting AI can
be a valuable feedback tool. While AI offers support, however, it must
be used in a human-centered process. If we remove humans, we go against
research demonstrating students’ relationships with teachers and peers
impacts engagement, motivation, and belonging, which in turn correlate
with student success and retention (Kirby & Thomas 2021). Moreover,
as Anson (2023) points out, humans provide crucial practice for
understanding the social and rhetorical dimensions of writing, a key
concept associated with learning transfer (Downs & Robertson,
2015).
Busy faculty and TAs often lack time to
provide draft feedback. Fortunately, many studies demonstrate that
students benefit from providing criteria-based feedback to peers
(Lundstrom & Baker, 2015). Our evidence-based model, studied across
10 composition classes and 3 large STEM courses, combines AI feedback
with peer review. Since many students question the competence of peer
reviewers (Alnasser, 2018), AI can provide reassurance. Pedagogical
approaches should emphasize student self-assessment and reflection to
encourage AI literacy and increase learning transfer (Yancey, 2014).