Peer review has a mixed reputation. When it works, students learn to read critically and revise thoughtfully, but when it fails, they exchange vague praise and leave the draft unchanged. Adding AI-generated feedback to the process can make peer review more productive if it is sequenced well. A unit on The Woman's Hour offers a good setting to try it.

The central idea is that each type of feedback has strengths. Peers can tell a writer when something is confusing, because they experience the essay as a real reader. AI feedback can quickly identify structural issues against the rubric, such as a missing counterargument or unsupported claim. Teachers can then focus on judgment calls and the largest conceptual problems.

A useful sequence begins with the student's own self-check against the rubric. Next comes AI-generated feedback on the rubric criteria, followed by peer review focused on reader experience and clarity. Finally, the teacher reads the revised draft. Each stage narrows the remaining issues, so the teacher's attention goes to where it matters most.

Structuring peer review so it works

Give reviewers specific tasks instead of asking for general comments. They might underline the thesis, mark where evidence from the book appears, and write one question the essay left unanswered. Specific tasks produce usable feedback and reduce the temptation to say it was good. Peer review sheets with three or four targeted prompts take little time to set up.

• Self-check against the rubric before any feedback is requested
• AI feedback that names rubric gaps and suggests next steps
• Peer review focused on clarity, questions, and reader reaction
• A short revision plan written by the student after reading both
• Teacher review of the revised draft for final judgment and grading

Teaching students to evaluate AI feedback

Students should not accept AI comments without thinking. Part of the assignment can ask them to mark which suggestions they will follow, which they will reject, and why. This builds judgment and discourages passive copying. It also gives the teacher insight into how students interpret feedback.

Make clear that the AI does not write the essay. The tool identifies issues and suggests directions, but the student remains responsible for the work. Setting this expectation upfront preserves academic integrity and keeps the focus on learning.

Protecting the human element

Some teachers worry that automated feedback will reduce the human connection in writing instruction. The risk is real if the tool replaces conversation, but it is manageable when the tool takes on repetitive work and the teacher uses the saved time for conferences. Talking with students about their ideas is more valuable than correcting the same structural error repeatedly. The best systems amplify that kind of teaching.

Evaluating the workflow

After the first cycle, ask students which stage helped most and why. Compare first and final drafts for improvements in the rubric areas emphasized. If peer review contributes little, adjust the prompts, and if AI comments are too generic, refine the rubric language. Iteration makes the process better each time.

Document the workflow so other teachers in the department can try it. A short guide with sample prompts, timelines, and student handouts reduces setup time and encourages consistency.