Large lecture courses built around an anthology like Wain's volume present a real logistical challenge. A professor may cover two centuries of poetry in a single term, and each major unit can generate a round of essays. When enrollment reaches into the hundreds, even brief written responses add up to a daunting amount of reading.

Most professors respond to this pressure by relying on teaching assistants, shortening assignments, or shifting to multiple-choice exams. Each of these choices has costs. Teaching assistants vary in experience, short assignments limit what students can demonstrate, and multiple-choice tests do not measure the interpretive skills that poetry courses are meant to develop.

A more sustainable approach keeps written analysis at the center but changes how feedback is produced. By combining clear rubrics, structured workflows, and AI-assisted first-pass feedback, professors can preserve meaningful writing assignments without being buried by them. The aim is to protect the learning value of the assignment while managing the workload.

Design Assignments With Grading in Mind

The way an assignment is written strongly affects how hard it is to grade. Prompts that require a specific claim and a limited number of quoted passages produce tighter essays that are quicker to assess. Open-ended prompts tend to generate sprawling responses that take longer to evaluate and are harder to score consistently.

• Limit the length so each essay stays focused on one argument
• Require a specific number of textual references to anchor the analysis
• Provide the rubric with the assignment so students know the standard
• Offer a small set of poems to choose from rather than the whole volume
• Build in a short revision step to reward response to feedback

Structure the Grading Workflow

A clear workflow reduces both time and inconsistency. Many professors find it helpful to grade one criterion at a time across a batch of essays rather than reading each paper start to finish. This approach keeps the standard fresh in mind and makes it easier to notice patterns, though it requires organization to execute well.

AI grading tools can handle much of this structuring by scoring every essay against each criterion and returning organized results. The professor or teaching assistants then review the output, focusing on essays where the score seems questionable or where an individual note would help. This turns grading from a marathon of reading into a process of targeted review.

Keep Feedback Specific Even at Scale

Students in large courses often complain that feedback is generic, and they are frequently right. A comment such as "good analysis" appears on many papers and teaches nothing. Specific comments that reference the student's own sentences and suggest a next step are far more effective, and tools that draft them can make this realistic.

Professors should also consider sharing aggregate feedback with the whole class. A short summary of the most common strengths and weaknesses across essays helps students place their own work in context. It also reduces the number of individual questions that arrive after grades are posted.

Support Teaching Assistants

Teaching assistants carry much of the grading burden in large courses and often have limited training. A shared rubric, sample essays, and AI-assisted drafts of comments give them a reliable starting point. This improves the quality of their feedback and reduces the time they spend second-guessing scores.

Regular check-ins to review difficult cases help maintain consistency throughout the term. When assistants know that unusual essays will be discussed rather than left to individual judgment, they tend to grade with more confidence. The result is a course where students receive fair and informative feedback regardless of which grader reads their work.