Learn of ASU-tested practices for running multimedia, AI supported peer review that raises engagement, builds AI literacy, and lightens grading. We’ll unpack a graduate Psychology model where students record narrated slide presentations, choose peers to review based on interest, and then submit a short reflection on how both peer and AI feedback changed their work. You’ll see how a transparent AI-use tier policy (what tools are allowed, where, and why) plus required citations keeps expectations clear and productive.

We’ll translate those practices into concrete moves you can adopt: a two-artifact workflow (video for authenticity + slide deck for fast grading/rubric checks), pre-peer AI/rubric self-assessment to improve first drafts, a reviewer-quality rubric to upgrade comments (specificity, timestamps, tone, actionability). A brief, demo (shown in Harmonize) maps each best practice to features like time-stamped in-video notes and Rubric Coach for student self-checks but we’ll also discuss how to accomplish these steps without Harmonize.

Register: AI in Peer Review: Elevate Engagement, Feedback Quality, and Grading Efficiency