Feedback is where course teams lose the most hours. It is also where learners feel the delay most: a comment that arrives two weeks after the assignment is feedback on a version of themselves that no longer exists.
That makes assignment feedback a good first agentic workflow. The task is well defined, there is already a rubric, and a mentor can review everything before it reaches the learner.
Step 1: Write down the process you already run
Before touching a model, map the current process. For most teams it looks something like this:
- A submission lands in the LMS.
- A mentor opens it next to the rubric.
- They score each criterion and write comments.
- Someone checks for consistency across mentors.
- The feedback is released to the learner.
The agent takes over the slow middle — steps 2 and 3 — as a draft. Steps 4 and 5 stay human.
Step 2: Give the agent tools, not just a prompt
A workflow becomes agentic when the model can act. Here, that means a short list of narrowly scoped tools:
- Fetch submission — read one learner's submission.
- Get rubric — read the rubric and exemplar answers for that assignment.
- Search course material — find the lesson a comment should point back to.
- Save draft feedback — store scores and comments for review.
Notice what is missing: there is no tool to publish grades. Leaving out the dangerous tool is the most reliable guardrail you have.
Step 3: Build an evaluation set before you trust it
Take 30 to 50 past submissions that mentors have already graded. Run the agent on them and compare: where does it agree with the mentors, where does it drift, and does it drift in the same direction every time? Keep this set and re-run it after every prompt or model change. Without it, you are guessing.
Step 4: Put the review step where it hurts least
The mentor now opens a queue of drafts instead of a pile of raw submissions. For each one they accept, edit or reject. Track the edit rate per rubric criterion — it tells you exactly where the agent needs work, and when it is ready to take on more.
Measure the agent by how much a mentor has to change, not by how impressive its comments sound.
What it costs, roughly
Model costs per submission are usually small next to mentor time, but they add up at cohort scale. Estimate with your real numbers: submissions per week, average length, and how many tool calls an average run takes. Cache the rubric and course material where your provider allows it, since they are the same for every learner.
What to automate next
Once the feedback loop is stable, the same pattern — clear goal, narrow tools, evaluation set, human review — carries over to other work: drafting weekly progress notes, preparing office-hour agendas from common mistakes, or checking new course material against the syllabus.
Frequently Asked Questions
Will an AI feedback agent replace mentors?
No. In this design the agent drafts and the mentor decides. The point is to move mentor time from typing repetitive comments to reviewing, correcting and adding the judgement only they can add.
How do we stop the agent from being too generous or too harsh?
Keep an evaluation set of already-graded submissions and compare the agent's scores with your mentors' on every change. If it drifts consistently in one direction on a criterion, tighten that criterion's rubric language or add exemplars.
Which LMS does this work with?
Any LMS that lets you read submissions and write draft feedback through an API or an export. The agent's tools are thin wrappers around those calls, so the pattern stays the same across platforms.
