First-year composition courses often use a single shared text, and All the Light We Cannot See is a common choice because it is accessible, emotionally engaging, and rich enough for sustained analysis. A professor teaching three sections of twenty-five students can easily end up with seventy-five essays on the same novel in the same week. Grading them all with care is a real challenge, particularly when the course also includes drafts and revisions.

The workload problem is amplified for adjuncts and graduate instructors, who often teach several courses at different institutions. Time spent on repeated feedback comes directly out of time available for office hours, course preparation, and their own research. A workflow that reduces the repetitive portion of grading without lowering standards can make a substantial difference to sustainability.

College-level essays also demand more nuanced feedback than a high school assignment typically does. Professors are expected to comment on argument structure, use of sources, disciplinary conventions, and the sophistication of the interpretation. A rubric that captures these dimensions is the foundation of any efficient grading process.

Designing a Workflow for Multiple Sections

A workable approach begins with a shared rubric across all sections, even if only one instructor teaches them. Consistent criteria make grades easier to defend and allow the professor to compare patterns across classes. Once the rubric is set, essays can be processed in batches, with an AI grading tool drafting initial feedback aligned to each criterion.

• Write the rubric before the assignment goes out so that students see the same criteria that will be used to grade
• Process a small calibration batch first to confirm the feedback matches your standards
• Review AI-generated comments and adjust scores for context, originality, and effort
• Add a personal note to each essay that addresses the student's specific argument
• Track recurring issues across the class and address them in a short lecture or handout

Maintaining Academic Standards

Some professors worry that AI-assisted grading will lower the quality of feedback. The concern is reasonable, and the answer is to keep the instructor in charge of final evaluation. The tool handles the first draft of comments based on the rubric, but the professor reads the essay, confirms the assessment, and adds discipline-specific observations that only an expert can provide.

Transparency with students is also important. Explaining in the syllabus how feedback is produced and reviewed builds trust and heads off confusion. Students generally accept technology-assisted grading when they understand that a human is accountable for the final result.

Supporting Revision-Based Courses

Many composition programs are built around revision, which multiplies the grading load. A student may submit a draft, receive feedback, and turn in a revised version, doubling the number of papers to read. Faster first-round feedback makes the revision cycle more realistic, since students can start revising within days instead of waiting two weeks.

For the second round, professors can compare the revision against the earlier feedback to see whether the student addressed the main issues. That comparison is where much of the learning occurs, and it deserves careful human attention. Automating the first pass gives instructors the energy to do that work well.

Making the Case to a Writing Program

Writing program directors evaluating new tools should consider consistency, transparency, and time savings. Tools that allow shared rubrics across sections help ensure that students in different classrooms are assessed against the same standards. A pilot with a small group of instructors is often the best way to gather evidence before a broader rollout.

Metrics worth tracking include the time spent grading per essay, the turnaround time for feedback, and student satisfaction with the comments they receive. These measures give the program concrete data to support a decision. A thoughtful pilot also surfaces concerns from instructors early, which makes adoption smoother.