Introductory humanities courses frequently assign the Book of Job as a bridge between ancient literature and philosophy, and enrollments in these courses can run into the hundreds. A single essay assignment can produce more than a hundred hours of grading when comments are detailed. Professors and teaching assistants need a system that preserves quality while keeping the workload realistic.

The first step is deciding what level of feedback each assignment deserves. A low-stakes response paper may need only a score and two sentences, while a major essay warrants fuller comments. Being intentional about this tiering prevents the common trap of spending the same effort on every submission and burning out before the most important work arrives.

Second, design the assignment to be gradable at scale. Clear prompts, strict length limits, and a required structure, such as a thesis in the first paragraph and at least three cited passages, make essays more predictable and therefore faster to evaluate. Predictability does not mean uniformity of thought, since students can still argue very different positions within the same format.

Calibrate Teaching Assistants Early

In courses with multiple graders, inconsistency between sections is one of the biggest sources of student complaints. Hold a calibration session before grading begins, where all graders score the same three or four sample essays independently and then discuss any differences. This exercise surfaces assumptions about what counts as strong analysis of a text like Job.

• Distribute a detailed rubric with written descriptors for each performance level.
• Choose sample essays that represent high, middle, and low quality, plus one borderline case.
• Have every grader score the samples independently before comparing results.
• Discuss each disagreement until the group agrees on how to apply the rubric.
• Spot-check a few essays from each grader midway through to catch drift in standards.

Build a Comment Bank

Most essays on Job share a handful of recurring weaknesses, such as thesis statements that merely describe, quotations dropped without explanation, and conclusions that restate the introduction. A comment bank lets graders paste a well-written explanation of each issue and then add a sentence tailored to the student's paper. This keeps comments substantive while cutting down on repetitive typing.

Keep the bank updated as you grade, adding new comments when you notice patterns. Students appreciate feedback that is clear and thoughtful even if it has been used before, as long as the personalized sentence shows that the paper was read. Over time, the bank becomes a reusable asset that can be shared with future teaching assistants.

Use Technology Where It Makes Sense

AI-assisted grading tools can handle the structural layer of feedback for large courses, applying a professor's rubric consistently across hundreds of essays. They can flag missing thesis statements, quotations without analysis, and weak organization, and draft comments for the grader to approve. This frees human attention for the interpretive aspects of the work.

Professors should still review a representative sample of the results and adjust the rubric if the output seems off. Transparency with students about how feedback is generated and who makes the final decision preserves trust. Used this way, technology reduces the burden of volume without removing the instructor's authority over grades.

Protect Time for High-Value Interaction

The goal of any efficiency strategy is to free time for the interactions that matter most, such as office hours and discussion of drafts. Students who receive quick, consistent feedback on routine issues can spend conversations with the professor on the interpretive questions that Job raises. That is where a professor's expertise is most valuable.

Consider tracking how much time each stage of grading takes, so you can see where the system saves effort and where it still bogs down. Adjust the assignment design, rubric, or comment bank accordingly. A well-tuned process makes it possible to assign meaningful writing in large classes without sacrificing the quality of feedback.