Peer review is one of the most practical ways to deepen learning and to keep assessment meaningful in the age of AI. This guide explains what it involves, how peer review, peer assessment, and peer feedback differ, and what to look for in a tool that runs the whole thing inside your LMS.

Peer review is the practice of students assessing and giving feedback on each other's work against criteria you set. A good Peer Review tool is what makes it work across a whole cohort, by handling the allocation, anonymity, reminders, and grading that would otherwise take hours by hand. It is the difference between peer review as a good idea you try once and peer review as a normal, dependable part of how a course runs. This page explains what peer review involves, how peer review, peer assessment, peer feedback, and peer evaluation differ, what to look for in a tool, and where to go deeper on each part of the practice.
At its simplest, a Peer Review tool takes the work students submit and routes it to other students for review against your criteria or rubric. Done well, it does four jobs that are painful by hand. It distributes submissions fairly, so no student is left without a reviewer and no one reviews their own group. It keeps the criteria in front of the reviewer, so feedback stays anchored to what matters. It manages anonymity, reminders, and the gathering of scores, so the admin disappears. And the better tools now coach students toward more specific comments while they write, which lifts quality at the moment feedback is created. The point of all of this is simple, to scale feedback quality without scaling workload.
Because it runs inside your LMS, you design the activity once and the system handles the rest, which is what makes structured peer feedback realistic at scale rather than a logistical headache.
These terms get used interchangeably, and that causes most of the confusion when teams design an activity.
Peer feedback is the qualitative part, where students comment to help work improve before a final version. Peer assessment adds evaluation, where students apply criteria and reach a rating or a score. Peer evaluation is the broad umbrella that covers both and often includes group members assessing each other's contribution. Peer review is the reciprocal process of giving and receiving all of the above.
A simple rule keeps it straight. If the output is comments, you are running peer review as feedback. If the output is a score against criteria, you are running peer assessment. Most courses are best served by leading with feedback, then introducing scored assessment once students trust the process.
The instinct is to worry that students are not expert enough to assess each other. That misreads where the value sits. The student who gains the most is often the one doing the reviewing, because to judge a peer's work against criteria they have to understand the standard from the inside. This is also why peer review has become one of the most practical answers to assessment in the age of AI, since evaluating a specific peer's specific work is something a student cannot hand to a model. It is authentic assessment in its most usable form.
The evidence is strong. In a meta-analysis of 54 studies, Double, McGrane and Hopfenbeck (2020) found peer assessment improved academic performance and, with the right supports, reached a reliability comparable to teacher marking. David Carless and David Boud (2018) call the underlying capability feedback literacy, and name peer feedback as one of the most effective ways to build it.
A few things separate a tool that genuinely helps from one that just moves the admin around. It should distribute work fairly and automatically. It should support rubrics and AI rubrics so criteria are quick to build and easy for students to apply. It should offer anonymous peer review when you want more honest comments. It should use AI responsibly, to coach students toward better feedback rather than to grade for them, which is exactly what our AI peer review coaching does. And it should be pedagogy-first and integrated in the systems you already use, so there is no new platform and no adoption barrier.
The FeedbackFruits Peer Review tool runs natively inside Canvas, Moodle, Blackboard, and Brightspace, so students never leave the environment they know and grades flow back automatically. If you are moving across from another platform, our peer review tool makes that transition straightforward.
To design the activity itself, read our guide to peer assessment, which covers the methods, a ready to use form, and worked examples. And when you need to show students what good comments sound like, peer feedback examples for students gives you the language and templates to share.
Peer Review is part of the Peer Assessment bundle, which brings peer review, self-assessment, and group member evaluation together in one pedagogy-first solution inside your LMS. You can start small and prove value through the Peer Assessment get started bundle.
For the thinking behind it, our ebook A guide to authentic peer assessment in higher education pairs thirty years of research with a semester one roadmap, and our webinar Assessment Rebooted: peer review and authentic learning in a post-AI classroom shows what good looks like before the new intake.