Revision is where most of the learning happens in writing, yet it is often the step that gets squeezed out when teachers have large classes. Peer review can fill part of the gap, and AI feedback can fill another part, but neither works well when used carelessly. For a Maze Runner essay, a thoughtful combination of the two can give students several rounds of input before the teacher reads the final draft. The key is giving each source of feedback a specific job.

Peer review is most effective when students have a clear task. Instead of asking classmates to give general feedback, provide a checklist tied to the rubric: Is the claim arguable? Does each paragraph include a specific scene from the novel? Does the writer explain how the evidence supports the claim? These questions direct reviewers to what matters and help them give usable comments. Reviewers also learn by applying the criteria to someone else's work.

AI feedback can serve as an additional layer by identifying patterns the peer reviewers might miss, such as missing counterclaims or weak explanations. Because it responds quickly, students can get input on an early draft and revise before meeting with peers. This sequencing means that peer conversations can focus on higher-level ideas instead of basic problems. The teacher can decide which tasks belong to which source.

Designing the Review Sequence

A workable sequence might start with a student drafting the essay and receiving automated feedback on structure and evidence. They revise and then trade drafts with a partner for a focused peer review on one or two criteria. After a second revision, they submit the essay to the teacher, who can concentrate on the highest-level feedback. Each stage reduces the number of basic problems remaining for the final read.

• First draft written with the rubric in hand
• Automated feedback on claim clarity, evidence, and structure
• Student revision based on the feedback received
• Peer review focused on one or two rubric criteria
• Final revision and submission for teacher grading

Teaching Students to Evaluate AI Feedback

Students should not accept AI comments blindly. Teach them to treat suggestions as hypotheses, checking each one against the text and the rubric. If a comment suggests adding evidence from the maze's layout, the student should ask whether that evidence actually supports the claim. This habit develops critical thinking and prevents the passive acceptance of any feedback, human or machine.

It also helps to have students record which suggestions they adopted and why. A short revision log, with entries like "Added a second scene about Minho because the comment pointed out only one example," makes the thinking visible. Teachers can review these logs to see how students are using feedback. They also provide a natural place to note when a student disagreed with a suggestion and explain their reasoning.

Keeping Peer Review Productive

Peer review can fall flat when students are reluctant to criticize classmates or unsure of what to say. Provide sentence starters, such as "One place where I wanted more evidence was..." and model a sample review in front of the class. Pair students thoughtfully, and keep the focus on the writing rather than the writer. Setting a norm that every review must include one specific strength and one specific suggestion keeps the tone constructive.

Monitor the process and step in when reviews are vague. A quick scan of the feedback forms can reveal which pairs need guidance. Short check-ins during the activity are usually enough to keep students on track. Over time, they become more skilled at giving and receiving feedback, which benefits all of their writing.

Where the Teacher Fits In

With peers and AI handling early feedback, the teacher can focus on the final draft and on students who need more support. Tools like GraideMind can assist during the drafting phase by applying the teacher's rubric and generating comments students can act on, while the teacher retains control of the final grade. This division of labor spreads feedback across more sources without sacrificing quality. It also shortens the time needed to grade the final essays, since many basic issues have already been addressed.

After the unit, ask students which forms of feedback helped most. Their answers will guide adjustments for the next novel, whether that means more peer review time, a different rubric focus, or clearer instructions for using automated suggestions. Continual refinement keeps the process effective and responsive to real classroom needs. Students appreciate being part of shaping how they learn.