A writing workshop built around Tartuffe gives students the chance to draft, receive feedback, and revise before a final grade is assigned. Peer review has long been part of such workshops, and AI feedback tools now offer a complementary source of response. When the two are combined thoughtfully, students receive more varied input than a single teacher could provide.

Peer review offers something that no automated tool can replicate: the experience of reading a classmate's argument and thinking critically about it. A student who reads a peer's essay on Orgon's blindness may notice weaknesses in their own draft that they had not recognized. This kind of reciprocal reading builds analytical skills that benefit both writer and reader.

AI feedback contributes speed and consistency. It can respond to a draft within minutes, apply the rubric criteria, and highlight patterns such as missing evidence or an unclear thesis. Students receive an immediate first response, which they can use to improve their draft before it reaches a peer or the teacher.

Sequencing the Workshop

The order in which feedback arrives affects how students use it. A sensible sequence starts with the student's own reflection on their draft, followed by AI feedback on structure and evidence, then peer review focused on argument and clarity, and finally teacher comments on the most important issues. This layering allows each source to contribute what it does best.

• Students complete a brief self-assessment against the rubric
• AI feedback addresses thesis, structure, and use of evidence
• Peer reviewers respond to the argument and ask questions as readers
• The teacher reviews selected drafts and gives targeted guidance
• Students write a revision plan explaining what they will change and why

Preparing Students to Give Peer Feedback

Students need explicit training to give useful peer feedback, since vague comments such as "good job" or "needs more detail" do not help. Modeling the process by reviewing an anonymous sample essay as a class, and demonstrating how to ask questions rather than simply pronounce judgments, sets a productive standard. Providing a short set of guiding questions keeps reviewers focused on the aspects of writing that matter most.

It also helps to assign specific roles. One reviewer might focus on the thesis, another on the use of evidence from the play, and a third on the flow of ideas between paragraphs. Dividing the task in this way reduces the pressure on any single reviewer and yields a more complete picture of the draft.

Teaching Students to Evaluate AI Feedback

AI feedback is a tool for thinking, not an authority, and students should learn to evaluate it critically. If the tool suggests that a paragraph lacks evidence, the student should check whether that is accurate and decide how to respond. This habit of questioning feedback, rather than accepting it automatically, is part of developing as a writer.

Teachers can reinforce this by asking students to record which suggestions they accepted, which they rejected, and why. The reasoning behind those decisions reveals a great deal about the student's understanding of their own argument. It also creates a record that supports honest discussion of how the writing process unfolded.

Evaluating the Workshop's Impact

To judge whether the workshop is working, compare first drafts with final versions. Look for improvements in thesis clarity, evidence use, and analysis rather than only surface corrections. If students are making meaningful revisions, the feedback is being used effectively.

Student reflections after the workshop can provide further insight into what helped most. Some students may find peer feedback more motivating, while others appreciate the speed and consistency of automated responses. Adjusting the balance in future units based on this information ensures that the workshop continues to serve students well.