Peer review is a cornerstone of authentic assessment in Digital Media education, yet its role in self-regulated learning remains underexplored, particularly as generative AI reshapes assessment. This study analyses structured peer review practices across three undergraduate units in an Australian Bachelor of Digital Media program, using artefacts created before generative AI became widespread. Guided by Zimmerman’s model of self-regulated learning and Self-Determination Theory, the analysis examines how students engage with assessment. It identifies three recurring mechanisms: rubric calibration, justified critique, and revision reflection. These mechanisms align with the forethought, performance, and self-reflection phases of learning. Together, they show how human-mediated assessment supports students’ capacity to judge quality, use feedback, and make revision decisions. The paper proposes a three-tier hybrid assessment model as a design-oriented implication rather than an empirically tested AI intervention.
Luan et al. (Fri,) studied this question.