Peer review has long been a favorite strategy for improving student writing, but it is only as good as the guidance students receive. When essays on When the Legends Die go through unstructured peer review, comments often amount to "good job" or "fix your grammar." Adding AI-generated feedback alongside peer comments can fill gaps and raise the quality of revision. The key is to design a process where each source of feedback has a clear purpose.

Start by deciding what peers should focus on. Students are generally better at spotting confusion and judging whether an argument is persuasive than at diagnosing technical problems. Prompt them to answer questions such as whether the thesis about Tom is clear and whether each paragraph supports it. These questions keep feedback focused on meaning.

AI feedback, in contrast, can handle systematic checks against the rubric, such as whether evidence is explained and whether the organization follows the thesis. Assigning these tasks to the tool lets peers concentrate on reader response. This division of labor mirrors how editors and reviewers work. It also respects the strengths of each kind of feedback.

Train Students to Give Useful Feedback

Students need explicit instruction in how to review a peer's draft. Demonstrate with a sample paragraph, showing the difference between a vague comment and a specific one. For example, "I got lost in the third paragraph because I could not tell which scene you meant" is more helpful than "this is confusing." Practice builds skill and confidence.

• Identify the thesis in one sentence and say whether it is clear
• Point to one piece of evidence that works well and explain why
• Mark one place where the explanation could be developed
• Note any moment where the organization felt confusing
• Suggest one concrete next step for revision

Sequence the Feedback Thoughtfully

The order of feedback matters. Some teachers begin with peer review so students receive human reactions first, then follow with AI feedback that addresses rubric criteria. Others reverse the order, using AI to catch structural issues before peers read the draft. Try both and see which produces better revisions in your classroom.

Whatever sequence you choose, require students to respond to the feedback in writing. A short revision plan listing the changes they will make and why shows that they have processed the comments. It also prevents the common problem of ignoring suggestions. Reviewing the plans gives you insight into their thinking.

Guard Against Over-Reliance

Students should understand that AI feedback is a tool, not an authority. Encourage them to evaluate suggestions critically and to decide which ones improve their argument. Class discussions about when feedback was helpful and when it missed the mark build judgment. This teaches an important skill for a world full of automated suggestions.

Likewise, remind students that peer comments reflect one reader's view. Encourage them to weigh multiple perspectives rather than accept every suggestion. A writer who can sort useful feedback from less useful feedback is a more independent thinker. Teachers can model this by thinking aloud while revising a sample paragraph.

Measure What Changes Between Drafts

To judge whether the process works, compare first drafts with revised versions. Look for improvements in the thesis, evidence use, and explanation, and note which type of feedback seemed to prompt each change. This analysis helps you refine the process for future units. It also gives you evidence to share with colleagues.

Collect student reflections on which feedback they found most helpful. Their answers often reveal insights, such as a preference for specific questions over general praise. Use these ideas to adjust the prompts and sequence. A feedback process that evolves with student input tends to become more effective over time.