College survey courses on modern drama often enroll seventy to two hundred students, yet instructors still want to assign analytical writing about plays like The Wild Duck. The tension is obvious, since meaningful writing requires meaningful feedback, and meaningful feedback takes time that large courses rarely provide. Professors and teaching assistants end up choosing between fewer assignments and thinner comments.

Ibsen is a particularly useful author for this problem because essays on his work follow recognizable patterns. Many students will write about the life-lie, the attic as a symbolic space, or Gregers's idealism, which means a shared rubric can cover most submissions. Predictable categories make it easier to apply consistent standards, even when the roster is large.

AI feedback tools fit into this setting as a way to handle the first pass of grading. The tool reads each essay against the rubric, drafts comments for each criterion, and suggests a score, which the instructor or teaching assistant reviews. This approach preserves human judgment where it matters while reducing the repetitive work that consumes so many hours.

Setting Up a Workflow for a Large Course

Begin by writing a rubric that fits the course learning outcomes, such as the ability to analyze dramatic form, situate a play in its historical context, and construct a text-based argument. Provide the rubric to students with the assignment so they know what is expected. Then run a small pilot on ten or fifteen essays to see how the AI feedback compares with your own judgment.

• Write rubric criteria that reflect course outcomes, including attention to realist technique and historical context
• Pilot the process on a small set of essays and compare AI scores with instructor scores
• Train teaching assistants to review and edit AI-generated comments rather than writing from scratch
• Set a policy for how students can request a human re-read of their feedback
• Collect class-wide patterns to inform lectures and discussion sections

Maintaining Rigor and Fairness

Rigor depends on clear standards and consistent application. When teaching assistants grade different batches of essays, differences in strictness often appear, and students notice. A common rubric combined with AI-assisted first-pass scoring reduces variation between graders, since everyone starts from the same baseline.

Fairness also involves transparency with students. Explaining how feedback is generated and reviewed, and offering a route to ask questions, builds trust and reduces grade disputes. Professors who are open about the process generally find that students engage more with the comments they receive.

Using Class-Level Patterns in Teaching

One of the less obvious benefits of structured feedback is the data it produces. If sixty percent of essays on The Wild Duck cite only the opening act, a professor can address that in the next lecture by walking through how the later acts complicate the early ones. Patterns invisible in a single paper become obvious across a class set.

This information also helps discussion sections. Teaching assistants can focus on the concepts students found hardest, such as distinguishing the life-lie from ordinary deception, rather than reviewing material most students already understand. Writing feedback and classroom instruction begin to reinforce one another.

What Instructors Should Still Do Themselves

Some judgments should remain with the instructor, including final scores on borderline essays, comments on unusually original interpretations, and responses to students who are struggling. An automated tool can identify missing evidence, but it cannot fully appreciate a student's emerging voice. Reserving time for these cases keeps the course humane.

Used this way, AI feedback becomes a way to sustain more writing in a large course rather than a replacement for teaching. Students write more, receive faster responses, and still encounter a professor who reads their best work closely. That combination is hard to achieve by hand at scale.