Elephants, Gorillas, No AI: Thoughts from a day spent at Nebraska Administrator Days

 The Nebraska Council of School Administrators puts on a summer event before school starts called Administrator Days. This year, for the third year in a row, I joined the conference, this time for just one day. I heard a lot about reading, changes to CTE, law, and behavior, but almost nothing about AI. Now, the Nebraska Department of Education and the ESUCC put together one session on the unified approach to Tech, including AI, and did an amazing job. But that was it. I tried to dig through the program and find a significant representation for this thing that is looming over us and disrupting our business as usual. Crickets.

This is the elephant that isn’t in the room, the gorilla that was not invited, and whatever other metaphor you would like to have about ignoring a real problem.

Crickets.

I am of two minds here. Reading is still important, maybe more than ever. Focusing on basic skills and making sure our students are ready to tackle any and all academic work through this medium is important, mission-critical, crucial. Hell, I was trained as a professor of literacy and taught reading to students of all ages with a reading disability. Yes it is VERY important. So maybe, we should talk about it with our leaders. I actually love the focus from the deprtment, that says no matter

But the relative silence about AI. Not just in the room and the program but in the corridor conversations, hit me hard. AI is here; it is changing classrooms, learning, and what our students can and will do with or without permission. Most districts in Nebraska have adopted legal language around AI, warning against unethical use and making AI policy a topic for teacher decisions. In essence, school systems are making it teachers’ problem.

So the positive spin on the lack of conversation around AI is that our schools are not being distracted by this shiny new thing. They need to improve what they are doing for every student at any age. I am all in for focusing on what students need. Another reason can be that we need to wait for this innovation to settle before we do anything about it.

The not-so-positive look is that schools, through their leaders, are sending a message that this enormous change that is sweeping through society and is now available to any student who has access to the internet, is not a big deal. Is this the right message?

My fear is that, by ignoring the reality forming on the ground, administrators are abandoning teachers to figure it out on their own, as the policy itself says. I actually love giving teachers agency, but at the same time you need to give them tools and force the issue. I want every Math teacher to know there is an app that allows students to upload any assignment and get the answer step by step. I want them to think about adjusting the way they do business BEFORE they catch the first student “cheating”. It’s not enough to say every teacher can create policy in her classroom. Give them some tools, quickly, before we have another completely avoidable crisis with discouraged teachers and students who are not spending enough time in the friction of learning.

I hope I am wrong and everything is going to be great; AI is going to be a non-event for education. But since I am right now in Chicago trying to learn further about it- you know where I stand.

Goldilocks and the Three AIs: What We Owe Graduate Students About AI

 Our graduate students are training for professional and academic careers, but our curriculum was built for a world before AI.

This isn’t an indictment; curricula move slowly by design. They encode what a field has agreed on, and agreement takes time. AI adoption is uneven, along what Ethan Mollick calls the jagged edge: strong in some tasks, unreliable in others, and on an improvement trajectory that makes next year (nearly) unforeseeable. A program that rewrote itself around last year’s capabilities would be revising again this year. Caution is a reasonable response.

The cases for using AI in academic work are tricky. Authorship is contested; Data protection rules were written before anyone pasted an interview transcript into a chat window. Faculty attitudes range from enthusiasm to anxiety, fear, and even hatred, and behind these emotions there are real arguments. I don’t think anyone is wrong to be uneasy.

I do think we are getting the cost of waiting for clarity wrong.

Our graduates are entering fields where AI literacy is already assumed. They will submit (and referee) to journals with AI disclosure policies. They will sit on IRB panels. They will be asked by a district or a dean or a search committee what their position is, and “I wasn’t taught that” is not a position. Sending them out without practical skill and a defensible ethical stance is not neutrality. It is a decision, and the cost lands on them.

On our podcast recently, a graduate student walked through how he arrived at his own working relationship with these tools. Not refusal, not delegation. He described testing where the tool helped his thinking and where it replaced thinking he needed to do himself, then settling into a level he could defend. It is about finding the Goldilocks level.

That process is what I want to teach. He got there on his own, which is fine for some but useless as a program design. Some students will figure it out. Others will land on one extreme or the other and stay there, and neither extreme serves them. Guiding students through that calibration is what post-secondary education is for. We do it with statistics, with critical theory, with writing. There is no reason to treat AI differently.

So rather than complaining in my writing (an exercise I enjoy), I am adding something alongside it. Starting this term, I am offering a free weekly work session for graduate students. Not a lecture series. Work time, with structure. Students bring their own projects, and each session anchors on a cluster of questions with substantial time to try things and see what breaks.

My initial plan covers nine clusters:

1. Authorship and agency. COPE standards, journal policies, what disclosure actually requires, and where the field has not settled.

2. Privacy and data protection. What you can put in a model, what you cannot, and how IRB commitments constrain your workflow.

3. AI in the literature review process. Where it accelerates search and synthesis, and where it steers you wrong.

4. Building a researcher identity with your AI. Setting up context so the tool works from your frameworks and your voice rather than a generic average.

5. Qualitative analysis. Promises and pitfalls, coding assistance, and the risk of losing contact with your data.

6. Quantitative analysis. Code generation, assumption checking, and verification you still have to do yourself.

7. Designing research instruments. Item generation, revision, and validity questions that do not go away but may look different.

8. AI as a teaching assistant. For their teaching, and for their own learning.

9. Friction and learning. Information processing theory, and why removing effort from a task sometimes removes the learning with it.

That last cluster matters most to me, and I plan to keep returning to it. Ease is not the goal. Some cognitive work has to happen in the student’s head, or it does not happen at all, and knowing which work that is separates skilled use from outsourcing.

I expect the list to change once students tell me what they actually need. That is part of the point. The clusters are a starting structure.

The argument is simple. Our students will practice in a field that has already changed. We can hand them the skill and judgment to work in it, or we can leave them to assemble it alone, after they graduate, with the stakes considerably higher. Those are the two options. There is no version where the question goes away.

P.S. Sarah often says that she hates this timeline. I often agree but until we can join the Time Variance Authority- this is pretty much it.

AI is not Social Media: But somtimes it is

AI is not social media. I keep sensing that much of the angst comes from the comparison, and I worry it may lead us to the wrong lessons and wrong actions. The anger at the arrogance of the tech bros is obvious and justified, and I share some of it. But the technology underneath works differently in ways that educators and Ed policymakers need to understand.

Social media has a main use and a main abuse. It captures attention and does not let go. Facebook decides what shows up in my feed. When I open ChatGPT, Claude, or Gemini, I bring the question, and I pick the task. That structure leaves agency with the user, and agency is at the center of what we try to build in students. We can take charge (though it has its agency traps) of AI, and we can teach our students to do it. We spent two decades watching attention platforms pull kids away from reading, sleep, and each other. A tool that helps a teacher differentiate a text, or helps a student get unstuck on a draft, runs on a different logic. A tool that helps students make, inquire, and design has potential.

Made with ChatGPT

The cost of running these models has also kept companies from chasing engagement the way social platforms did, at least so far. But the danger is still lurking in developing relationships with bots. Much like social media replacing human connections with synthetic ones at critical moments in development can have very harmful effects on kids. But unlike social media, AI offers a complete set of affordances unrelated to synthetic relationships. Moreover, you can realize the benefits without a unique login.

I am not saying the tech bros turned good. I am saying the technology is different, and our response in education should reflect that. Schools that treat AI as another attention trap will ban it and give up the chance to teach students to use it well. At the same time, the real lessons from social media, about business models and about protecting kids, still apply, and we should carry them forward. Nuance takes more effort. In classrooms, we cannot afford to skip it.

My proposition for education is to avoid a blanket ban on screens (though no phones in school sounds reasonable to me). Teach students about their own cognition and about AI. Then use AI to augment and enrich their education while emphasizing agency.

What I Actually Want From AI in Peer Review

 It’s summer, and I finally have a bit more breathing room, some of which I use for peer review (the rest is for my granddaughter). The pressure never really lets up, though. I’ve written before about how AI is reshaping that pressure, on both the writing and reviewing sides of the equation.

Publishers are drawing a clear line. They’re asking reviewers not to run submitted manuscripts through AI tools, and they’re right to ask. A paper under review is unpublished work. Feeding it into a commercial AI system risks the author’s intellectual property in ways reviewers might not stop to consider.

I follow that line carefully. I use AI to help me write up my reviews, sharpen the language, organize my thoughts, and make the feedback easier for authors to act on. I never run a manuscript I’m reviewing through AI, though often I think it could help.

I know it could be helpful because with my own writing I do something different. When I finish a draft, I ask AI to act as a peer reviewer and pick apart my argument. Where is it weak? What can I cut when I’m over a word limit? AI has been remarkably good at both. It catches gaps I’ve stopped seeing because I’ve read my own draft too many times. I get back to work the same way I would after an actual peer review, provided I agree with the notes, which, much like human reviewers, I do not have to agree with.

I do this through institutional AI that doesn’t train on what I feed it. I’ll admit, honestly, that the training question isn’t the part that keeps me up at night, even if I did not have such access; the benefits definitely outweigh the risks. What strikes me instead is how good the feedback is and how much better the review process could be if this kind of access extended to reviewers too.

I’m not calling for AI-written reviews- in that case humans are not needed. I want to be clear that we (still?) need human reviewers. What I’d love to see is publishers giving reviewers access to no-training AI as a tool.

Peer Review made with ChatGPT 5.5

Picture a reviewer working through a manuscript that cites a document by name. Right now, chasing that down means opening another tab, another search, another interruption. AI access built into the review platform could pull it up on the spot. Or picture a paper built on a statistical test with a dozen parameters. A reviewer could ask what those parameters mean and whether they were applied correctly, instead of taking the authors’ word for it or quietly moving past a section they don’t fully trust but have no time to verify.

I recently reviewed a paper where the data looked suspect. It was disappointing, and in that case, the signs were obvious enough to catch without much effort. But it left me wondering how many more subtle cases slip through, the ones that look clean on the surface and only fall apart under closer inspection. That’s exactly the kind of pattern-checking AI does well, if reviewers had a legitimate, secure way to run it.

The bigger opportunity is replication. Right now, a reviewer can, in principle, rerun an analysis or test an alternative approach if the authors shared their data. In practice, very few do it, because it takes far more than any reviewer has the time and resources for. AI with real document and data access could shrink that gap. Reviewers could actually check the math (within approximation) instead of trusting that it holds. This may be critically important when people are using AI to generate research facsimiles, sometimes fabricating the data and the writing.

None of this changes what a peer reviewer is for. The job is still to weigh the argument, judge whether the evidence supports the claims, and tell the editor whether the work deserves to move forward. AI wouldn’t replace that judgment. It would just let reviewers exercise it with more precision, on more of the paper, in less time.

Faster reviews. Sharper reviews. Reviewers who can actually verify instead of just trusting. That seems like a use of AI worth building, and one publishers are well positioned to build safely, since they control the data and access.

The Tinkering Ways AI is Transforming Research

 I recently sat on a grant panel. As on many panels, I was asked not to discuss the details of the decisions, a request I always respect. The proposals spanned a wide variety of fields, so there was plenty to learn and chew on. What struck me most was the robust use of AI.

Well, you’d say, we live in the age of AI. Everybody’s got ChatGPT (or pick your favorite model), and people mumble something about how it can be used because generative AI is sexy and new. The cynic would point out that we’re saturated with AI, both as an economic and a cultural phenomenon, and that if you want funding, you have to at least nod toward some magical use of AI that will completely transform your field. The cynic would add that in most cases, the AI is described vaguely and hand-wavily. And they wouldn’t be entirely wrong.

But in many of these applications, I saw something else entirely. Most of them actually included well-thought-out machine learning, not generative AI. They used small models, built from scratch for a specific purpose, as opposed to large language models. In a way, these proposals were “unsexy”: they didn’t reach for the new models at all. They offered innovative solutions using the old ones.

Created with ChatGPT 5.5

The insight took a few days to land. AI was everywhere, but not in the cynical way I’d expected. Here’s how I read it: the cultural significance of generative AI, and the sheer accessibility of AI, has gotten researchers in every domain thinking, “Hey, maybe this AI thing can help me solve this really hard problem.” (Sorry, I can’t reveal much from the panel, so no details.) It’s fairly clear to me that generative AI has started a conversation inside research that will produce exciting new solutions to some of our most intractable problems.

The next challenge, oddly, will be having enough compute, coders, and AI experts to bring these projects to fruition. I saw this at one of our research gatherings earlier this year: everyone is looking for an AI research partner, while our AI scientists are flooded with collaboration requests. I think we could triple the number of people doing that work and still have demand to spare.

So where does that leave us? I believe research universities should invest in compute and AI know-how as a shared service that can support many research efforts at once. Some have already gone this direction. I hope more, including UNL, will follow.