AI in Grading Isn’t the Risk. Misunderstanding It Is.

At a Glance

• Faculty concern is real. In recent faculty survey data, 95% of respondents said generative AI will increase student overreliance, 90% said it may diminish critical thinking, 83% anticipated decreased attention spans, and 78% reported increased cheating since generative AI became widespread.

• Faculty are using AI anyway. The contradiction is that faculty concern and faculty adoption are happening at the same time. One source cited in your session materials reports that 77% of faculty have used AI professionally, while 74% reported students are using AI to write.

• The real issue is AI literacy, not AI itself. Large language models are good at structure, language, and pattern recognition, but weak in judgment, verification, and context. That is why human oversight in grading is essential.

Artificial intelligence is already moving into academic work. That part is no longer hypothetical.

In the March 2026 edition of the 3rd Thursday with AI series, we covered these topics at length. Faculty are using it to draft rubrics, generate feedback, summarize discussion posts, and support students outside the classroom. Learning management systems are beginning to build AI directly into their workflows. Productivity platforms are doing the same. In practical terms, AI is no longer something sitting off to the side. It is starting to show up where faculty already teach, write, grade, and communicate.

That shift has created understandable anxiety.

As a nursing informatics educator and a data analytics consultant, I hear the same concern over and over again in slightly different language. People want to know whether AI is making grading less fair, less human, or less defensible. They worry that feedback will become generic, that students will rely on the system too much, and that faculty judgment will slowly get pushed out of the process.

Those concerns are legitimate. But I do not think AI in grading is the main risk.

The bigger problem is that many people are using these systems without a clear mental model of what they are actually doing.

That matters, because when you misunderstand the tool, you either trust it too much or reject it too quickly. Neither response is especially helpful.

The Current Faculty Reality

Faculty are not imagining the pressure.

The March 2026 session materials for this presentation cited survey data showing widespread concern about generative AI in higher education. According to those materials, 95% of faculty feared increased student overreliance, 90% believed AI may diminish critical thinking, 83% anticipated decreased attention spans, and 78% reported increased cheating since generative AI became widespread.

That is a striking level of concern, and it tells me that this conversation is no longer about novelty. We are past the phase where AI just feels interesting. It now feels consequential.

At the same time, the same slide deck highlights an uncomfortable reality. Faculty are experimenting with AI too. One cited source in the research summary reported that 77% of faculty had used AI professionally, while 74% said students were using it to write essays or papers.

That tension is the story.

We are worried about AI in education while also folding it into our own workflows. That does not make faculty hypocritical. It makes the moment more honest. People are trying to solve real workload problems while also trying to protect academic standards.

The Contradiction Is the Point

This is where I think the conversation usually goes sideways.

People often frame the issue as if there are only two positions available. Either you embrace AI because it saves time, or you reject it because it threatens rigor.

That is too simple.

The real contradiction is that faculty see value in the technology while also recognizing that something important can be lost if it is used carelessly. In the transcript from your session, you made that point directly. You described the tension between speed and trust, and argued that the real work is not marveling at what AI can produce but interrogating it and reclaiming design authority.

That framing is exactly right.

The danger is not that AI always produces terrible work. The danger is that it often produces work that looks polished enough to escape scrutiny.

And in grading, polished is not the same thing as accurate, fair, or educationally sound.

What AI Is Actually Doing

If faculty are going to use AI well, they need to understand the basic mechanism.

Large language models do not evaluate student work the way a faculty member does. They do not understand a paper, a clinical reflection, or a discussion post in the human sense. They process prompts as tokens, identify patterns learned from large text datasets, and predict the next most likely word or sequence of words. The result can be remarkably fluent and well structured, but it is still probability-driven output, not professional judgment.

That distinction is not just technical trivia. It changes how these tools should be used.

If a model is strong at structure and language, then it makes sense to use it for structured drafting tasks. If it is weak at verification and context, then it should not be trusted as the final judge of student performance. Your handout states this plainly: AI is strong in structure, language, and pattern recognition, but weak in judgment, verification, and context.

That is a much more useful framework than the question, “Can AI grade?”

The better question is, “Which parts of grading are actually structured language tasks, and which parts require human evaluation?”

A More Useful 3-Lane Framework

This is the model I find most practical.

The first lane is rubric development. This is a good entry point because rubrics are highly structured. AI can generate an initial rubric draft quickly, especially when the assignment is clearly defined. That does not make the rubric correct. It means the model can produce a usable starting structure that faculty then refine for rigor, fairness, and competency alignment.

The second lane is feedback generation. AI can help draft consistent formative feedback linked to rubric criteria. This can save time and reduce cognitive load, especially across large numbers of assignments. But the handout and demo notes are clear about the risk here: the model may introduce details that were never in the student work, overstate strengths or weaknesses, or generalize beyond the evidence provided. Faculty still have to verify every statement.

The third lane is academic support. AI can help translate feedback into next steps that students can actually use. It can explain what feedback means in simpler language, generate examples, and ask reflective questions that support revision. That can be genuinely useful, especially for students who struggle to act on feedback. But again, this is support, not evaluation. Faculty still define expectations and standards.

Put simply, AI can help with structure, language, and translation. Faculty remain responsible for standards, truth, and judgment.

The Guardrails Are Not Optional

Once AI enters grading workflows, the ethical issues become very concrete.

The first is privacy. Your March presentation emphasized that student assignments and academic records are protected under FERPA, and that uploading identifiable student work into public AI systems is not compliant. The guidance in the deck is straightforward: anonymize submissions, remove identifying details, and use institution-approved tools when available.

The second is bias. AI systems can reflect biases embedded in their training data, and that can shape feedback, scoring language, or the kinds of student performance they implicitly reward. Your materials repeatedly note that responses should be reviewed for fairness across student populations.

The third is hallucination. AI can generate statements that sound polished and authoritative but are factually wrong or unsupported by the student submission. That is why evidence-linked feedback matters. In the research document, one recommended mitigation strategy was to require exact quotes from student work as evidence for scoring decisions and to flag uncertainty when a criterion cannot be evaluated confidently.

These are not edge cases. They are part of responsible use.

What This Means for Faculty

I do not think the future of grading is automated judgment, and I do not think it should be.

What I do think is this: faculty can use AI to reduce friction around the repetitive parts of academic work without giving up authority over evaluation.

That is the real balance.

AI can help generate a first draft of a rubric. It can help phrase feedback more clearly. It can help students understand what revision should look like. But it does not know whether a response is truly thoughtful, whether reasoning is clinically sound, or whether a piece of work reflects growth in the context of a course.

Those are human determinations.

Your session materials say it in the clearest possible way: AI generated the structure. Faculty define the standard. AI helps you say it. You still decide if it is true.

That is the position I keep coming back to.

A Thoughtful Path Forward

AI in grading is not inherently the threat. Misunderstanding it is.

If faculty treat AI as a silent authority, the risks grow quickly. If they treat it as a structured drafting tool that requires scrutiny, verification, and professional oversight, then it becomes much more useful.

That is where I think the conversation needs to go next.

Less fascination with what AI can produce on first pass. More attention to what it is actually doing, where it performs well, and where it should never be left alone.

Because in the end, grading is not just a writing task. It is a judgment task.

And judgment is still ours.