Peer review is a familiar part of the writing process, but it often disappoints. Students exchange Fault in Our Stars drafts, write comments like "good job" or "fix the grammar," and hand the papers back with little change. Teachers who want peer review to matter need to structure it carefully. Pairing it with rubric-aligned AI feedback can raise the quality of the whole exchange.

The first requirement is a focused task. Instead of asking students to review a paper generally, give them one or two specific questions tied to the rubric, such as "does the thesis make an arguable claim about Hazel?" or "is each quotation explained?" Narrow tasks are easier to complete and produce more useful comments. They also keep reviewers from defaulting to surface corrections.

Training students to give feedback is equally important. A brief lesson in which the class reviews an anonymous sample essay together shows what helpful comments look like. Teachers can contrast vague remarks with specific ones and let students practice rewriting the vague ones. Students who have seen good models tend to give more thoughtful responses.

How AI Feedback Fits Into the Process

AI feedback can serve as a baseline before students exchange drafts. A student receives comments aligned with the rubric, revises based on them, and then shares the improved draft with a peer. The peer reviewer can focus on higher-level questions such as whether the argument is persuasive, since the basics have already been addressed. This sequence uses both types of feedback where they are strongest.

• Students receive rubric-based AI feedback on a first draft and revise it
• Peers exchange revised drafts and respond to two targeted questions
• Reviewers point to specific sentences rather than offering general praise
• Writers respond to feedback with a short note about what they will change
• The teacher reads the final draft with the feedback history in view

Keeping Human Voices at the Center

AI feedback should not replace the conversations that peer review creates. Students benefit from hearing how a classmate reads their argument, and that experience cannot be fully replicated by software. A peer might notice that a paragraph about Augustus feels unconvincing or that a joke in the introduction distracts from the thesis. These reactions come from a real reader and often carry weight that automated comments do not.

Teachers can preserve that value by keeping some parts of peer review free from technology. For instance, students might discuss their drafts aloud in pairs before writing any comments. This lets them explain their intentions and hear the reader's response in real time. The written comments that follow tend to be richer as a result.

Evaluating How Well It Works

It is worth checking whether the combined approach improves revision. Compare drafts and final versions for a sample of students, noting whether the changes address the feedback they received. Ask students which comments they found most helpful and why. These observations reveal whether the process supports real learning or merely adds steps.

Adjustments may be necessary. If students rely on the AI comments and skip peer discussion, the teacher can restructure the sequence. If peers repeat the software's observations, the review questions may need to be more distinct. Iterating on the design over several assignments leads to a process that fits the class.