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AI in Action

Feedback in the Age of AI: More Thinking, More Learning, Less Noise

12/10/2025By James Cantonwine
Feedback in the Age of AI: More Thinking, More Learning, Less Noise

Feedback is one of the most powerful tools we have to support student learning, but only if we use it well. With AI tools, the question isn’t whether we can generate more feedback. We can. The real question is simpler and more important:

Does including AI feedback increase the chances that students will think hard about the content and improve their future performance?

If the answer is yes, it’s worth our time. If the answer is no, it likely adds noise rather than learning.

This post is about how feedback works, where grading gets in the way, and how AI can help us shift more feedback toward what students actually need to grow.

The Purpose of Feedback

Feedback is often thought of as a way that we communicate judgements of a piece of student work and suggestions for improvement. This view of feedback can lead to teachers making the same comments to the same students all year long. The students might make the adjustments on this assignment but then be unable to transfer the feedback to something else. This loop can give feedback a bad reputation because it focuses too much on the work and not the student.

Dylan Wiliam captures the heart of the matter: “The main role of feedback, at least in schools, is to improve the learner, not the work.”1

It’s not enough for a student to fix commas on this essay if they still do not understand sentence boundaries for the next one. A beautifully revised essay is nice, but the real measure of success is whether the student can apply the learning to the next essay: the one they haven’t written yet.

Feedback is only doing its job when it leads to more thinking, more understanding, and better performance over time. And that means the goal isn’t “more comments,” but better comments that cause students to think.

What Effective Feedback Looks Like

Effective feedback has a few consistent qualities across the research base. It is:

  • Timely, so students still remember their thinking and can act on the information.
  • Specific, so they know exactly what they did and what they should do next.
  • Actionable, so they have a clear path forward rather than a vague label or judgment.
  • Focused on learning, not on error correction alone. Good feedback helps students understand the underlying concept, strategy, or skill.

Another key finding that appears again and again across studies is that students engage more deeply with feedback when it is separate from a grade. Time pressures and workload sometimes cause us to try to do both of these at the same time. This is rarely the best choice for promoting student learning. To summarize a classic study on feedback types2:

  • Comments only → the greatest gains in learning
  • Comments + grade → students ignore the comments
  • Grade only → almost no improvement

None of this is really about student motivation. It’s about attention. When a grade appears, the brain locks onto the number rather than the message. If we want students to take feedback seriously, we have to give it in ways that keep the focus on thinking and improvement.

What Feedback Is Not: Where Grading Falls Short

Grading has a place, but it’s a small one. A grade is a summary of past performance, not a tool for future improvement. It tells you what happened, not what to do next.

This is where Stiggins et al offer a helpful reminder: “If our assessment practices don’t result in higher achievement, we would say a component of quality is missing.”3

A grade alone almost never increases achievement. If anything, it can shut the learning loop down prematurely. And when grades overshadow feedback, we lose one of the most powerful levers we have for student growth.

This is also why using AI as a grading tool is risky. Even when an automated system produces a reasonable score, the teacher is ultimately responsible for every grade in the class and for ensuring grades are fair, accurate, and aligned to standards. AI can help with organization or pattern-checking, but it cannot replace teacher judgment.

AI and Feedback: A Real Opportunity

Here’s the good news: while AI may not be ready for grading, it has enormous potential to improve feedback if used thoughtfully.

Historically, the biggest barrier to providing high-quality, individualized feedback has been time. Giving every student specific, actionable, next-step guidance takes hours. Teachers rarely have that kind of capacity.

AI tools change that equation. With the right prompts, AI can:

  • Generate specific comments tied to your rubric
  • Suggest next steps or “missions” aligned to learning goals
  • Provide quick examples or models for revision
  • Offer first-draft feedback that you can edit and finalize

This doesn’t replace your expertise: it amplifies it. Instead of spending hours writing similar comments across 30 essays, teachers can spend that time reviewing, refining, and customizing the most important feedback for each student.

A second opportunity lies with students themselves. If taught responsibly, students can use AI to collect feedback on demand. Students who historically received less individualized feedback now have more ways to access on-demand support. They can check clarity, ask for revision suggestions, compare their work to criteria, or break down teacher feedback into simpler language. That’s not cheating; that’s metacognition.

Transparency is essential. Students and families should know which parts of feedback were AI-generated and which were teacher-created. When we’re clear about that, trust stays intact and the learning stays front-and-center.

Bringing It All Together

At the end of the day, the goal isn’t to add another tool or dashboard or workflow. The goal is simple: do more of what actually helps students learn and grow.

Feedback works when it helps students think more deeply and perform more strongly in the future. AI can help us get more of that feedback into more students’ hands, more often and without overwhelming teachers.

We’re entering a moment where high-quality feedback doesn’t have to be limited by time. With thoughtful use of AI, we can increase both the quantity and quality of feedback students receive.

That’s the opportunity in front of us. Let’s use it well. Feedback should cause thinking, and AI can help us create more of those thinking moments.

Footnotes

  1. Education Endowment Foundation. (2021). Teacher feedback to improve pupil learning. Education Endowment Foundation. https://educationendowmentfoundation.org.uk ↩

  2. Koenka, A. C., Linnenbrink-Garcia, L., Moshontz, H., Atkinson, K. M., Sanchez, C. E., & Cooper, H. (2019). A meta-analysis on the impact of grades and comments on academic motivation and achievement: a case for written feedback. Educational Psychology, 41(7), 922–947. https://doi.org/10.1080/01443410.2019.1659939 ↩

  3. Chappuis, J., Stiggins, R. J., Chappuis, S., & Arter, J. (2012). Classroom assessment for student learning: Doing it right—using it well (2nd ed.). Pearson. ↩