Listening to My Students at Scale: Exit Tickets, NotebookLM, and the Tightest Feedback Loop I've Ever Built

It started at a teaching workshop, last semester: Craig Kapp and Rob Egan presented a seminar at the NYU Center for Teaching and Learning called "Real-Time Insights: Leveraging AI for Responsive Teaching in Large Classrooms." They (re-)introduced a deceptively simple concept: the exit ticket. The idea is that at the end of every class session, you ask students three quick questions, each with a different shape metaphor:

• 🔵 Circle: What is still circling in your mind? (What are you confused about?)
• 🟥 Square: What "squared" with your understanding? (What clicked today?)
• 🔺 Triangle: What are three key takeaways from today's session?

Then, take these answers, and use LLMs to process them quickly and get feedback before the next session.

Getting structured feedback from students after every single session? Not at the end of the semester when it's too late to change anything, but right now, while you can still do something about it? I immediately wanted to try it.

Below I describe the details of the approach presented by Craig and Rob, and my own adjustments to the recipe. Hope you will find it useful.

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The setup: Making it required (and why that matters)

It starts by setting up the exit ticket surveys as auto-graded quizzes on Brightspace (NYU's LMS). The auto-grading part is a nice little trick: one of the questions is simply "Select True in this question to get your points." Students complete the survey, they get their credit. No manual processing of ~50 submissions on my end.

We do tell students upfront: write something substantive. Don't game the system. We reserve the right to deduct points if someone slacks through the exit tickets all semester. And here's the nice irony: since we're already running AI-powered analysis on the responses, identifying freeriders who type "asdf" every week is trivial. The same pipeline that processes the feedback also flags the people not providing any.

The critical design decision: make it part of the grade, not optional. Optional feedback gets ~30% response rates and self-selected complainers. Required feedback gets everyone. And because this is formative feedback (not evaluative), students have every reason to be honest and detailed. They're not rating me. They're telling me what they need.

Compare this to the end-of-semester evaluation. Students fill it out in December, the professor reads it in January (maybe), and any changes happen next year for a completely different group of students. The feedback loop is so long that it barely qualifies as a loop. Exit tickets close that loop within days. Sometimes hours.

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From exit ticket to next session: the processing pipeline

So now I have all this feedback. ~50 students, after every session, telling me what confused them, what clicked, and what they're taking away. The question becomes: how do you actually process all of that quickly enough to act on it?

NYU IT built an official path for this, which Rob demonstrated in the seminar. You export the exit ticket responses into the Brightspace Insights Portal (which Rob's team manages) and run AI-powered analysis using a prompt like this:

You are an expert Instructional Designer and Data Scientist assisting
a professor with the course "AI/ML Product Management" at NYU Stern
School of Business (undergraduate).

Your goal is to analyze student feedback survey data to improve course
delivery. The survey questions and student answers are provided below.
Please perform the following two steps:

### Step 1: Thematic Analysis
Analyze the responses to identify key themes. Do not just look for
keywords; look for semantic similarities and underlying sentiment. For
each theme, provide:
1. **Theme Name**: A concise title.
2. **Prevalence**: The approximate number of students who mentioned this.
3. **Explanation**: A brief summary of the sentiment or issue.
4. **Evidence**: A direct, representative quote from the data.

### Step 2: Actionable Pedagogy (Bloom's Taxonomy)
For each theme identified above, propose a short course activity.
* If the theme represents a **knowledge gap/pain point**, propose a
  remedial activity.
* If the theme represents a **strength/interest**, propose an activity
  to deepen understanding.
* **Constraint**: The activity must be supported by Bloom's Taxonomy.
  Explicitly state which level of Bloom's Taxonomy the activity targets
  (e.g., Application, Analysis, Evaluation).

**Format**:
Start the suggestion section for each theme with the label: "PRACTICE IDEA".

I attach the survey data.

It's a well-designed prompt. Thematic coding, prevalence counts, representative quotes, remedial activities aligned with Bloom's Taxonomy. The output is genuinely useful.

But I prefer to do something slightly different. I use the same prompt from the Insights Portal, but I run it inside NotebookLM with just the student feedback as input. For those unfamiliar: NotebookLM is Google's AI-powered research assistant. You upload your own documents, and it generates analysis, summaries, explainer videos, and podcast-style audio overviews grounded entirely in your uploaded sources. NYU provides institutional access through Google Workspace, so the data never trains any AI models, which matters when you're working with student feedback.

Why NotebookLM over the Insights Portal? Because the exit ticket analysis is just the starting point. What I really need is to prepare the follow-up material. Once NotebookLM identifies the themes and suggests activities, I take those suggestions and combine them with my lecture slides, readings, and case studies (which are already loaded in the same notebook). Then I ask it to generate explainers, videos, infographics, and targeted activities that address the confusion, all grounded in my actual course content.

The Insights Portal gives me a diagnosis. NotebookLM gives me the diagnosis and helps me build the treatment.

My workflow after every class:

1. Students complete the exit ticket on Brightspace (takes them 2-3 minutes)
2. I export the responses and upload them into a NotebookLM notebook, together with the materials for that session
3. NotebookLM identifies the themes: what's confusing people, what clicked, what they found most valuable
4. Based on those themes, I generate explainer materials, short videos, and targeted activities for the next session

(As an example, here is the NotebookLM that we use for the Zillow Offers case, which we use to discuss leading and lagging metrics, model and output monitoring, concept drift, adverse selection and other product-management-related topics. Note: this notebook contains only course materials for preparing the case discussion, not student feedback data.)

One small but annoying wrinkle: NotebookLM's default slide output has that unmistakable "AI-generated" aesthetic. You know the one. (Yes, they are visually gorgeous compared to my own slides, but after a while it starts feeling a bit like slop.) So I started uploading the NYU brand style guide as an additional source in my notebooks, and prompting NotebookLM to follow it when generating visual materials. The results are noticeably closer to proper NYU-branded slides. Not perfect, but much better than the generic AI look. I'm still waiting for NotebookLM to support custom templates or branding natively, but that's a different story.

The per-session overhead is maybe 15-20 minutes.

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Why this actually works

The circle/square/triangle structure does something clever: it gives students permission to be confused. "What is still circling in your mind?" is a much less intimidating question than "What don't you understand?" And the three-takeaways question forces them to reflect, even briefly, which helps consolidate their learning.

But the real reason students engage is that they see the results. When I open the next class by saying "Several of you mentioned you were confused about X, so let's spend 15 minutes on this before we move on," students learn that their feedback actually matters. It creates a virtuous cycle: they write thoughtful responses because they know I'll respond, and I can respond because NotebookLM makes processing all the responses feasible. Without the AI assist, no professor has time to synthesize free-text responses from 50 students after every class and create targeted follow-up materials. Definitely not after every single session. The economics just don't work.

With NotebookLM doing the heavy lifting? The economics suddenly work beautifully.

The exit ticket has been around for decades. Craig and Rob simply showed how to supercharge it with AI. The hard part was never getting students to talk. It was finding the time to listen. Once students realize someone is actually listening, they start saying things worth hearing. That's the loop. That's the whole trick.

Everybody Is a CEO Now (And What Exactly Am I Doing Here?)

It's hard to pinpoint the exact moment when something fundamentally shifts. There's no day when you wake up and say, "Today, everything is different." It's more like boiling a frog. Except in this case, the frog is me, and the water feels amazing.

Over the last few weeks, a confluence of AI developments crossed an invisible threshold. None of them is dramatic on their own. All of them, together, are profoundly changing how I work, how I teach, and honestly, how I think about what comes next.

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Claude stopped being a chatty know-it-all

Let me start with the most concrete thing. Around December, Claude became... different. Not in some flashy, press-release way. It just started being right. Consistently, reliably right. The suggestions were spot on. The reasoning was good. The writing did not feel like fluffy AI slop. The output needed minimal editing.

I know, I know—"AI is getting better" isn't exactly breaking news. People have been saying this for years. But there's a qualitative difference between "impressive compared to what we had before, but I still need to direct and edit this very carefully" and "I now trust this thing with real work." We crossed that line.

Here's the moment it hit me. Yesterday, I had a brainstorming session with a student. We shared documents, exchanged ideas, sketched out some research directions. Normal academic stuff. Afterwards, I dumped my messy meeting notes into Claude and asked it to organize them.

What came back was not just a cleaned-up document with better formatting.

It was a research program.

Legitimate research questions, well thought out, properly scoped, organized into a coherent agenda with clear methodological approaches. I sat there staring at my screen. I did not feel like I was a professor advising a student and making some progress. It felt like we were in reality two grad students who had been goofing around with half-baked ideas, and then our wise, respected senior professor walked into the room, sat down, and said: "OK, here's how research is actually done. Here's how you think about this. Here's how you organize your work."

Not a helpful assistant anymore. Claude was setting the agenda this time around. It was the senior colleague. It was the advisor.

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The Agent That Puts PhD Students to Shame

And then there's the agent setup, which is where things get truly surreal.

When you pair Claude with GitHub for memory, an AGENTS.md file for context, and a TODO.md for task tracking, something clicks. The AI labs have been saying for a while that their agents were reaching "PhD student level." I've supervised PhD students for 20 years. I love them. Truly. But let me be blunt: I have never worked with a PhD student this organized and this diligent.

None of them have ever created a table mapping every data-driven claim in the LaTeX code to the specific code and data files that support each claim. None of them has had a full pipeline for the data analysis and the figures in a makefile, ready to repeat everything if necessary. None of them has had a reproducibility package ready before we even sent out the first manuscript.

The only downside? I will not be able to have drinks with this PhD student in the future and feel happy seeing them be so much more successful than I am.

A paper is about to go out. I started writing in earnest on Saturday. It took a total of four days of work to get to a submittable manuscript. The experimental analysis, the writing, the polishing. Four days. This would have taken four weeks minimum with a human collaborator, and that's being generous. And the quality isn't "good enough for a draft." It's "ready for submission with minor tweaks."

I find myself glued to my screen all day. I am not doing busy work. I write down what needs to be done, and this is happening behind the scenes. I am getting back the next iteration in an hour, I look at it, I give feedback, we cross things out from the TODO.md and we move forward. This is real work being done. Not just coding. Paper writing. Report preparation. Coding practices leak into other types of work, and things are moving. My real work is getting done, not just my academic software prototypes.

It's like having an infinite pool of employees, each one eager, competent, and ready to come back with actual deliverables. Not drafts that need to be rewritten. Not outlines that need to be fleshed out. Deliverables.

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Teaching as Curation: The NotebookLM Story

Let me tell you about another shift that's been happening in parallel, this one in our classroom.

We teach an AI Product Management course at Stern, and starting in November, something strange happened to how we prepare. We stopped creating content. We started curating it.

Here's our workflow now: After every class session, we collect student feedback. What clicked, what didn't, what questions came up, what topics generated the most energy. We dump all of this (the feedback forms, our own notes, relevant articles, the previous session's materials) into NotebookLM.

And then we ask it to help us design the next session.

NotebookLM digests the student feedback, identifies the gaps, suggests educational activities, and creates new explainer material that directly addresses what students found confusing or wanted to explore further. It connects themes across sessions that we might not have noticed. It proposes case studies that are relevant to the questions students actually asked, not the ones we assumed they'd ask.

The result? The course is absurdly adaptive. Every session builds on what students actually need, not on a syllabus we wrote in August. We're not creating lectures from scratch anymore. We're curating a learning experience, with AI as our editorial partner. The student feedback loop, which used to inform maybe the next semester's version of the course, now informs the next class.

We feel like careful curators, because we're still the ones making the final calls. For now. For how long? No idea. Perhaps in Summer even the curation will be something the AI does better than us.

Education is changing. Bloom's two sigma problem, the finding that one-on-one tutoring outperforms classroom instruction by two standard deviations, is solvable. Now. What is our role? No clue. Perhaps the future of education does not need professors. But the future of education is bright. We will not believe how bad we are. Almost like going from writing with a marker on transparencies to having an interactive demo of the concept. That transition took 30 years. Let's see where we will be in 30 months.

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So... Everybody's a CEO Now?

Here's where I start to feel a little dizzy. The marginal cost of competence is hitting zero.

If I can supervise an AI agent the way I'd supervise a research team (giving it direction, reviewing output, iterating on results) and if this scales to writing papers, analyzing data, building prototypes, designing courses... then what am I? I'm a manager. A director. A CEO of a one-person company with an arbitrarily large AI workforce.

But here's the question: What happens when everyone can do this?

When every professor can produce research at 10x the speed. When every consultant can deliver analyses that used to require a team of five. When every entrepreneur can build and ship products without hiring engineers. When every student can produce work indistinguishable from an expert's.

Do we still need employees? Is it even feasible for everyone to operate like a one-person business? And if so, who are the customers? If everyone is a CEO, who is buying?

I don't have answers. The words people have been saying for the last few years, "AI will change everything," "this is the new industrial revolution," "knowledge work will be transformed," those words haven't changed.

But the feeling has.

It used to feel like a prediction. The prediction is here. You will feel it soon, if you have not felt it already. It will be a mix of awe and fear. Impostor syndrome to the fullest. What exactly am I adding here?

I'd love to tell you that the human role is now "taste, judgment, direction-setting" and that AI just handles the execution. That's the comforting version. But I just told you that Claude set the research agenda, not me. So even that may not hold for long.

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Bye now

And for now, if you'll excuse me, I need to go review the deliverables my AI team just submitted. Four papers in the queue, a course redesign in progress, and a blog post that, unlike this one, I didn't write myself.

OK fine, I didn't write this one myself either.

(Kidding. Mostly.)