Comparative literature seminars are small, discussion-driven, and heavily dependent on writing. A seminar that includes Ljudi govore may ask students for reading responses, a midterm essay, and a longer research paper, which adds up to a surprising amount of grading. Professors are understandably cautious about using AI in such a setting, since interpretation and originality are what the course values.

The most sensible approach is to use AI for the parts of grading that are consistent and rule-based, while keeping interpretive judgment with the professor. A tool can check whether a thesis is present, whether evidence is cited, and whether the structure follows the assignment guidelines. It cannot, and should not, decide whether an original reading of the book is persuasive.

This division of labor is especially useful for responses and drafts, where students need quick feedback to keep their thinking moving. Receiving comments within a day or two, rather than a week or more, makes a real difference in a seminar's momentum. Professors can then devote their reading time to the final papers, where their expertise matters most.

Match the Tool to the Task

Different assignments call for different levels of automation. Short reading responses are well suited to a quick rubric-based first pass, while a research paper deserves a full human read. Deciding in advance which tasks get which treatment keeps expectations clear for both students and instructors.

• Reading responses: AI-assisted first-pass feedback with spot checks
• Midterm essays: AI draft comments reviewed and edited by the professor
• Research papers: full professor read with optional AI support for structure
• Discussion contributions: assessed by the professor directly
• Final reflections: read in full and graded by the professor

Protect Originality and Nuance

The risk in comparative seminars is that automated feedback pushes students toward conventional readings. A rubric that explicitly rewards originality, risk-taking, and engagement with ambiguity helps prevent this. Professors should also review AI comments for any suggestions that might discourage unusual but defensible interpretations.

Seminars thrive on disagreement and unexpected connections between texts. Make clear to students that feedback is a starting point for revision, not a verdict on what the book means. Encourage them to bring questions raised by comments into discussion.

Be Open About the Process

Students deserve to know how their work is being evaluated. State in the syllabus which assignments receive AI-assisted feedback and which are graded entirely by the professor. Transparency builds trust and gives students the chance to raise concerns.

Offer a way for students to request a full human review of any piece of feedback they find unconvincing. This safeguard reassures students and keeps the professor accountable for final grades. In practice, few students use it, but its availability matters.

Keep Teaching at the Center

Grading is part of teaching, and the goal is to improve student writing, not just to produce scores. Use the patterns revealed by feedback to adjust seminar discussions, address common weaknesses, and suggest readings. A professor who sees that many students struggle to compare structure across texts can devote a session to the topic.

Used thoughtfully, AI becomes a way to give students more feedback, sooner, while freeing the professor to engage deeply with the ideas that make seminars worthwhile. The technology supports the teacher's role rather than replacing it. That is the balance most departments will want to pursue.