A real snippet: 11 p.m., revising the thirty-seventh course assignment. The same formatting error, and tonight I’d already written the same comment for the eleventh time; in the comment box for the next one, my hand was already uncontrollably typing "pay attention to standard citation format"—exactly the same as the previous ten.
At that moment I realized that most of my grading time wasn’t spent giving "feedback" at all; it was spent "copying."
First, let’s do the math on grading time
Break down grading a batch of 50 assignments: the part that truly requires a teacher’s professional judgment—whether the argument holds, whether the reasoning is rigorous, whether the solution is feasible—is actually only a portion of it. The rest is mostly: writing comments over and over for the same formatting issues, manually aggregating common problems scattered across assignments, and the decline in comment quality caused by late nights.
What repeats isn’t the feedback itself; it’s the carrier of the feedback. Realizing this was the starting point for restructuring the process. muchsci’s role in this process also became clear—it only touches the repetitive parts and does not touch the judgment parts at all.
The restructured grading process
Step one: set the standards. First, write the grading criteria clearly—how many dimensions this assignment is divided into and what weight each dimension carries. The standards are the teacher’s judgment and cannot be outsourced.
Step two: let the machine run the initial screening. Give the grading criteria and the students’ assignment texts to muchsci together: AI runs through them and produces two things—a draft comment for each assignment and a summary of common problems. Observations that span the whole class, such as "thirty assignments all have inconsistent citation formats," are exactly what human eyes are most likely to miss when flipping through them one by one.
Step three: the teacher reviews and finalizes. Go through the draft comments one by one: approve those you agree with directly, rewrite those you don’t, and rewrite again those involving individual students’ special circumstances. AI drafts, the teacher finalizes—this order cannot be reversed.
After restructuring the process, where is the boundary?
What the tool eats up is repetitive labor, not the boundary of the teacher’s responsibility. I draw this line very clearly:
What cannot be handed to AI: final evaluations and scoring judgments of students, individual feedback that requires an educational context, and determinations involving academic integrity—these three are the teacher’s core responsibilities, and not a single word of them is outsourced.
What can safely be handed to tools: clustering and summarizing common problems, draft comments for each assignment (used after teacher review), and unified labeling of formatting issues.
After that day
For the same 50 assignments, grading time was visibly shortened, and the quality of comments was actually more stable than handwriting late at night—because I had reviewed every draft comment while clear-headed, and there was even an extra list of common problems that could be addressed centrally in the next class.
Tools eat up repetitive labor, and teachers preserve judgment. The grading efficiency dividend brought by tools like muchsci is real, provided you first think clearly: what are the things only you can do?