Students writing essays on Skellig increasingly have access to AI writing tools outside the classroom, and rather than treating this as purely a threat to academic integrity, many teachers are finding value in bringing structured, teacher-supervised AI feedback directly into the drafting process itself. Used well, an AI feedback tool can give students a fast first read on their draft's clarity, organization, and evidence use before a teacher ever sees it, which allows class time and teacher attention to focus on the more subjective, higher-value feedback that only a human reader can genuinely provide.

The key distinction that keeps this practice academically sound is that the AI tool reviews and comments on a student's own original draft rather than generating the essay's content or arguments for them, and the assignment structure needs to make this boundary unmistakably clear to students from the outset. A well-designed workflow might have students draft their entire essay independently first, then run it through an AI feedback tool for a structural and clarity check, then revise based on that feedback before final submission to the teacher, with each stage of the process left visible for the teacher to review.

For a text as interpretively rich as Skellig, AI feedback tools can be particularly useful at catching structural issues, such as a thesis that shifts mid-essay or a paragraph that drifts into summary, that students often struggle to notice in their own writing regardless of how carefully they proofread. This kind of structural feedback, delivered quickly and available for multiple rounds of revision before a final deadline, gives students more opportunities to strengthen their draft than a single round of teacher feedback delivered only after the essay is already considered finished.

Setting Clear Boundaries for Appropriate Use

Teachers introducing AI feedback tools into a Skellig essay unit should communicate specific, concrete expectations rather than a vague instruction like use AI responsibly, which leaves too much room for varied interpretation among students genuinely unsure of where the line falls. Concrete guidance might specify that AI tools may be used to identify unclear sentences, flag missing evidence, or suggest questions to consider, but may not be used to generate new sentences, paragraphs, or arguments that the student then simply inserts into their own draft without substantial rewriting in their own words.

• Using AI feedback to flag unclear thesis statements before final submission
• Identifying paragraphs that drift into summary rather than analysis
• Checking whether evidence is sufficiently explained rather than just quoted
• Reviewing organizational flow between paragraphs and transitions
• Never using AI tools to generate original arguments or full sentences

Building Transparency Into the Drafting Process

Requiring students to submit their AI feedback log alongside their final essay, showing what suggestions they received and how they responded to each one, creates useful transparency without requiring teachers to police every single revision decision a student makes during independent drafting time. This kind of visible process documentation also gives teachers insight into a student's revision thinking, which can be genuinely informative feedback in its own right, revealing whether a student engaged thoughtfully with suggestions or simply accepted every recommendation without any critical evaluation of whether it actually improved their essay.

This transparency requirement also tends to have a natural moderating effect on inappropriate use, since students asked to document and explain their revision choices are less likely to accept an AI-generated sentence wholesale without at least some genuine reflection on whether it accurately represents their own analysis and voice. Teachers reviewing these logs quickly, even just skimming for red flags rather than reading every entry in exhaustive detail, can catch most cases where a student has drifted from appropriate feedback use into inappropriate content generation.

Where the Teacher's Own Grading Fits In

Once a student's revised draft reaches the teacher for final grading, that grading should still rest on the teacher's own judgment and rubric, informed by the human relationship and classroom context the teacher has with that specific student, rather than deferring entirely to whatever score an automated tool might separately generate. AI-assisted grading on the teacher's side can still speed up this final step, generating rubric-aligned first-pass comments the teacher then reviews and personalizes, but the final evaluative judgment and the grade itself should remain clearly the teacher's own.

This layered approach, where students use AI feedback during drafting under clear, documented boundaries, and teachers use separate AI-assisted tools to support their own grading efficiency afterward, keeps the technology's benefits available at both stages of the writing process without blurring the line between student-generated work and teacher-evaluated work. Clarity about which tool is doing what, and for whose benefit at each stage, is what makes this kind of layered AI use sustainable and academically sound rather than a source of confusion or integrity concerns.