Formative feedback is the comment a student receives while there is still time to improve an essay, and research on writing instruction consistently points to it as one of the most powerful teaching tools. Unfortunately, it is also one of the most expensive in terms of teacher time. A teacher with 150 students cannot realistically write detailed comments on every draft of a Bartleby essay.

This is where AI-assisted feedback tools can be helpful, provided they are used thoughtfully. A tool that reads a draft against a teacher-defined rubric can point out where a thesis is vague, where a quotation lacks commentary, or where the argument stops developing. Those are exactly the observations that teachers make in margin notes, only delivered faster.

The key is that the tool supports the revision process rather than replacing the teacher's judgment. A student who receives a targeted question such as "What does the narrator's decision to move offices suggest about his sense of responsibility?" is prompted to think, not simply to accept a correction. Feedback framed as questions keeps the student responsible for the writing.

A revision workflow that keeps students thinking

One effective approach is to run a formative round after the first full draft, with feedback focused on only two or three criteria. Students respond by writing a short revision plan describing what they will change and why, which helps them use the feedback rather than skim it. The teacher can then review the plans and intervene with students who misunderstand the comments.

• Limit formative feedback to the two or three criteria the lesson targets
• Have students write a short response explaining how they plan to revise
• Review AI-drafted comments before they reach students when stakes are high
• Keep grades out of the first feedback round to encourage risk-taking
• Compare first and final drafts to see whether the feedback was used

Keeping the teacher in the loop

Teachers should decide which comments go out, especially for a text as interpretively open as this one. An automated comment might suggest that a student needs more evidence when the real problem is a misreading of the narrator's tone. Reviewing the drafts, even quickly, catches such cases and lets you replace them with better guidance.

GraideMind is designed around this teacher-in-control model, allowing educators to set the rubric, review generated comments, and edit anything before students see it. That structure keeps the tool aligned with a teacher's standards and voice. It also means students hear from their teacher, not from a black box.

Setting clear expectations with students

Students should know how feedback is produced, what it is meant to do, and that the final judgment belongs to a human instructor. Saying so plainly reduces suspicion and prevents students from treating machine comments as final answers. A short explanation in the assignment sheet is usually enough.

You might also encourage students to disagree with a comment and explain why, treating feedback as the start of a conversation. That habit builds the same critical skills the story itself demands, since the narrator's confident explanations deserve scrutiny too. Students who question feedback thoughtfully often write stronger revisions.