Professors who teach large survey courses on European literature or the Enlightenment face a familiar trade-off: assign meaningful essays on works like Minna von Barnhelm and drown in grading, or fall back on multiple choice and lose the writing practice that students need. With two hundred or more students, even a short response paper means hundreds of pages of reading. Many instructors respond by assigning fewer essays than they believe students should write.

Teaching assistants are the usual solution, but they bring their own challenges. Graders vary in experience, turnover is high, and consistency across a team of six or eight is difficult to maintain. A professor may spend more time training and reviewing assistants than grading personally, and students still complain that their score depended on which assistant they happened to get.

AI-assisted grading offers a different division of labor. The system applies the professor's rubric to every essay, producing draft scores and comments tied to specific criteria, and humans review the results rather than starting from scratch. This approach does not remove the need for judgment, but it shifts effort from repetitive first-pass marking to the decisions that really require expertise.

Designing assignments that scale

Assignments in large courses should be short, focused, and clearly specified. A seven hundred word response on a single question about Minna's testing of Tellheim is easier to grade consistently than an open-ended five page paper. A tightly defined prompt also reduces the variation that makes essays hard to compare, and it helps students understand exactly what is expected of them.

• Keep prompts narrow enough for a short, focused response
• Publish the full rubric alongside the assignment
• Provide a model paragraph that shows the expected analysis
• Specify the edition and citation format students must use
• Plan a revision opportunity for students scoring below target

Keeping humans in the loop

A responsible workflow reviews a meaningful sample of AI-scored essays before releasing grades. Choose a random selection plus any essays near grade boundaries, and compare the system's comments against your own judgment. If you find patterns of disagreement, such as consistent underrating of unconventional arguments, adjust the rubric language and rerun the batch before students see anything.

Make it easy for students to request a human review. A simple form that lets a student ask for a second read, with a short explanation of why, builds trust in the process and catches the occasional misjudgment. Most students never use it, but knowing it exists makes them more comfortable with the overall system.

Giving feedback students will actually use

Large courses rarely deliver personalized feedback, which is a loss because feedback is where most writing improvement happens. AI-generated comments tied to rubric categories give each student specific information about where the essay succeeded and what to change, even in a course of three hundred. The comments are not a replacement for a teacher's voice, but they are far better than a bare number.

To get the most from feedback, build in a revision step. Allow students to resubmit an improved version for partial credit, and use the same rubric for the second round. Students who revise show measurable gains, and the second round of scoring is often faster because the system and the professor have already seen the first draft.

Addressing concerns from students and colleagues

Expect questions about fairness, privacy, and academic integrity. Explain clearly how scoring works, what role the instructor plays, and how student work is handled. Transparency does more to build confidence than any technical assurance, and it invites students to treat the process as a collaboration rather than something done to them.

Colleagues may worry that automation cheapens teaching. The best response is to point to what the technology makes possible: more writing assignments, faster feedback, and more office hours spent on ideas rather than marking. When assessment becomes sustainable, the ideal of a writing-intensive survey course stops being aspirational and becomes routine.