The core of the case is simple. Frequent feedback matters. Research in learning sciences confirms this. Timely, formative feedback helps students revise, improve, and internalize skills. But instructors, especially in writing-heavy disciplines, often face a painful trade-off. They can offer in-depth comments to a few students, or cursory ones to all. AI disrupts that trade-off. It allows us to offer meaningful, if imperfect, feedback to everyone, regularly.
Some might object that AI-generated comments lack nuance. That is true. Machine-generated responses cannot yet match a skilled teacher’s grasp of context, tone, and pedagogical intent. But that is not the right comparison. The real alternative is not “AI feedback vs. perfect human feedback.” It is “AI-assisted feedback vs. no feedback at all.” Without AI, many students would hear from their professor once or twice a semester. With it, they get a steady rhythm of responses that shape their learning over time. Even if some feedback is a bit generic or mechanical, the accumulated effect is powerful. Volume matters.
There is also a difference between delegation and abdication. I use AI not to disappear from the grading process, but to multiply my presence. I still read samples. I still scan the flow. I constantly correct AI, and ask it to rewrite feedback. I calibrate and recalibrate responses. I add my voice where it matters. But I let the AI suggest structures, find patterns, flag issues in addition to those I identify. It catches what I might miss at the end of a long day. And I catch things it misses. It handles the repetition that otherwise numbs the teacher’s eye. In other words, AI is a junior grader, not a substitute professor.
Why not go further and let students self-assess with AI? Why not skip the instructor altogether?
That path sounds tempting. But it misunderstands the purpose of assessment. Good assessment is not just a score. It is a conversation between teacher and student, mediated by evidence. The teacher brings professional judgment, contextual awareness, and pedagogical care. AI cannot do that. At least not yet. It can mark dangling modifiers or check for thesis clarity. But it cannot weigh a struggling student’s growth over time. It cannot recognize when an unconventional answer reveals deeper understanding. That requires human supervision.
In fact, removing human oversight from assessment is not liberating. It is neglect. There are risks to over-automating grading. Biases can creep in. Misalignments between prompt and rubric can go unnoticed. Students can game systems or misunderstand their feedback. Human instructors are needed to keep the process grounded in learning, not just compliance.
The right model, then, is hybrid. AI expands what teachers can do, not what they avoid. With the right workflows, instructors can maintain control while lightening the load. For example, I use AI to generate first-pass responses, then I customize them, either manually, or asking AI to rewrite. Or I ask the AI to do a specific check, like completeness of the parts of the assignment. The trick is to know when to lean on automation and when to intervene.
There is also an emotional dimension. When students get feedback weekly, they feel seen. They know someone is paying attention. That builds motivation, trust, and engagement. AI does not create that feeling. But it supports the practice that does. It keeps the feedback loop open even when human time is short. In this way, AI is not replacing the human touch. It is sustaining it.
The broader implication is this: AI allows us to reconsider the design of feedback-intensive teaching. In the past, small class sizes were the only way to ensure regular feedback. That is no longer true. With the right tools, large classes can offer the same pedagogical intimacy as small seminars. Not always, and not in every way. But more than we once thought possible.
Of course, AI grading will not solve all instructional problems. It will not fix flawed assignments or compensate for unclear rubrics. (It can help plan instruction, but that's for another blog). It will not restore joy to a disillusioned teacher. But it will make one part of the job lighter, faster, and more consistent. That is not trivial. Teaching is cumulative labor. Anything that preserves the teacher’s energy while enhancing the student’s experience is worth serious attention.
We do not need to romanticize feedback. We just need to produce more of it, more often, and with less exhaustion. AI grading helps us do that. It is not perfect. But it is good enough to be a breakthrough.
