I don't bike to biking group: Grappling with Ai contradictions

 I am heading out, as I do most Sundays, to meet with the biking group. No, I don't bike; more than that, I don't want to be judged for joining a biking group coffee conversation without having biked there. I love solving the world’s problems before noon and then returning to my everyday life having had a meaningful, even joyful interaction, without biking.

Why am I sharing this? I have been thinking about the backlash against (and shaming of) AI-generated everything, but most prominently, writing. I want to be judged by the quality of my ideas, the clarity, and the flow of what I want to say. Just like I want to show up for the biking group not having biked, without judgment. When I read AI, it is the same; I do not want to care if it was generated (or smoothed) by AI; I want to care about the content and the ideas.

The truth is that it takes discipline. There is ample work that shows that AI users are better at spotting AI writing. I am not sure I am better than average, but there are cases when I am quite sure that I am reading AI. Notice I did not say AI slop, just AI. And that is the distinction for me: slop is slop whether AI-generated or not.` Coherent arguments are coherent regardless of authorship.

What I noticed, especially when I do peer reviews, is that I have an instinctive reaction to writing that strikes me as Claude-ish (or AI-ish). My bias translates to: “if you asked AI to write this, why did you ask me to read this? Why should I care?” This has also been documented as the AI tax: people devalue what they perceive (accurately or not) as a product of AI. As with other cognitive biases, I have to use metacognitive strategies to shape the bias and ask the more substantive question. What is the merit of the argument? What does it add to our scientific knowledge? Where can it be better. The biggest challenge may be that cognitive biases are notoriously hard to shake. Kahenmann said: “This is the essence of intuitive heuristics: when faced with a difficult question, we often answer an easier one instead, usually without noticing the substitution.”
― Daniel Kahneman, Thinking, Fast and Slow

Few of us are immune to this blind spot. In the case of AI I am painfully aware that dismissing AI is a lazy heuristic but that does not make it much easier to overcome. I make a special effort to work through the text and get to the heart of the argument.

Notice I am not claiming that AI text is better. In fact, I believe it is often worse because it tends to use expected but dense language. The point is that I need to judge it on its own merit without considering the source.

This argument does not hold in education. In my classes, I am teaching students to think, and so evaluating the argument made by AI is irrelevant; the point was to teach humans to think. This makes AI a much more nuanced partner. In science, the only question is whether science has moved forward; in training teachers, the question was whether they developed the discernment and capacity.

The education part requires more thought (or so Claude has informed me) but I am off to meet the bike group.

The Cost of Silence about AI

I apologize; I skipped a week of posting. It was my 60th birthday, and I was feeling existential angst. I had a topic ready, but when Sunday morning rolled around, I just could not bring myself to write. So apologies, but also maybe you had a few more minutes for yourselves, which is never a bad idea.

The topic I wanted to write about last week is the peculiar situation that is uniquely American. Older Americans see AI more positively than younger generations n(though not be much). My inquiry into this issue began when I had a chance encounter with a colleague who retired quite a few years ago; after recognizing me, he excitedly said he would love to meet and talk about AI. A few months ago, I shared about another former colleague who wanted to share and learn about what we were doing and thinking. At the same time, many of my colleagues and quite a few of my undergraduate students react with resistance, which I accept, but also an emotional response that is hard to process. So, older pAmericans were welcoming and younger ones rejecting AI. I wanted to know if my anecdotal data collection was backed up with more data. It turns out this is a trend that the Pew and others have noted in large scale polling.

This is hard to disentangle because this is not universal. International data shows a more familiar trend, with younger people more excited and positive about AI. I believe that the reasons are complex and multi-layered, including our disillusionment with the Tech industry, especially social media, and our rising environmental concerns. This may be uniquely American because the companies driving this change are US (and Chinese) companies, and we feel we should have a stake and a say, whereas the rest of the world lacks that sense of agency. This is not my point though.

In multiple conversations with colleagues exploring this world of AI and its impact, I heard frustration and a sense of foreboding. We are becoming reluctant to talk about our work because we do not wish to be the subjects of an emotional response or get drawn into an argument. The cost of talking about AI is rising, and it is impacting our ability to talk about what the field is experiencing.

From Gemini Dreambeans App

This brings me to the beginning of the semester. I am teaching a class about technology integration. AI must have a seminal presence in that class. I found myself dreading broaching the subject with my students. This dread is useful because it makes me more deliberate and thoughtful about the scaffolds and exit ramps I put in place. I must open the world of AI to my students; it will be their world, and if they choose to reject AI, I want them to do so from a place of understanding and acknowledgment of the tradeoffs. The cost of that silence may be too high as we prepare professionals who will not be able to face the moment, who will not be able to prepare their students for a world where AI in everywhere and expected.

SO I will get over myself and introduce AI its advantages and concerns throughout the curriculum. Wish me luck!

Notes from Google Educator Series for Higher Education

 This week I had the opportunity to spend two days with the combined teams from ISTE and Google with fifty or so colleagues from all around the country thinking about AI in higher education. This was in my top two professional development experiences for working with AI, and it has changed my opinion of using Gemini in higher education.

The PD started with NotebookLM (now Gemini Notebook). As I watched it I had a sinking feeling that this PD, much like a few others I had in the last few months, would stay at the ground floor of capabilities. I was wrong. In the following sessions, the team was wise enough to divide us by level of comfort with AI for some activities, while in others we worked based on interest in a theme or the point our institution was on a developmental path toward AI readiness.

As always the conversations around the tables with different themes were enlightening. I worked side by side with plant researchers, law school instructors, computer science professors and student support professionals. I learned something from each and I hope I was able to contribute.

The discussion and use of Gemini trying to solve the problem of supporting my peers on their AI journey was fascinating. Last year, a colleague and I created the CEHS AI circles, trying to respond to faculty's call for opportunities to learn. We held a session every Friday for a full academic year but had very limited success attracting participants. We did have 4-10 people every time (more staff than faculty), but very little experimentation and actual work. This was a great opportunity to use my time with prompt suggestions from the PD team to explore the issue further.

So I gave Gemini the framework of the situation, not just AI but also about our budget cuts, new interim Dean and the general mood as I read. Gemini gave me what I regarded as soft language so instead I asked for a hard look. This is what I got:

The Raw Truth About Your Faculty Problem

• The “Nobody Gives a Shit” Frame: Let’s stop sugarcoating it. Your faculty aren’t showing up because they fundamentally do not care about your well-structured, low-barrier, kumbaya professional development. To them, it’s just another useless pile of crap on a plate that is already overflowing. “Voluntary” doesn’t mean low-pressure; it means “optional bullshit I’m going to ignore so I can survive the week.”

• The Paranoia and Cynicism Frame: The atmosphere is choking on toxic, “negative vibes” and bitching about AI is currently the easiest way for faculty to deal. Walking into an AI workshop feels like walking into a trap where they have to admit they’re lost, or worse, risk getting side-eyed by their peers for drinking the tech Kool-Aid.

• The Bureaucratic Middle-Finger Frame: You want leadership to give you a golden ticket—sanction without sounding like a mandatory “do more with less” beatdown. But let’s be real: administration is terrified, budgets are bleeding, and faculty are already convinced any top-down tech initiative is just a Trojan horse to squeeze them harder or make their jobs obsolete. Until you stop treating time like a magical, flexible resource that they can just “carve out” of their already-shattered schedules, you’re just screaming into an empty room.

As always, when asking someone for a raw, unfiltered opinion, it can be challenging to read and process. It is also a reminder that it might not be true. That said I believe it is more true than not. So what I asked: can I do as a faculty member who wants to support others WITHOUT creating mandates, keeping it voluntary, and remembering that I am NOT in a leadership position.

This is what Gemini Suggested:

The “Trojan Horse” Relief Trap (Zero-Effort Utility)

Forget workshops, open PD, and “AI literacy.” Faculty don’t want to learn how to prompt; they want their worst, most mind-numbing administrative chores to magically disappear.

• The Move: Bypass the “learning” phase entirely. Do the work for them behind closed doors, or set up a high-yield, done-for-you swap.

• The Execution: Instead of advertising an AI session, announce: “Send me your ugliest, most time-consuming rubric, your messiest qualitative student feedback data, or your most tedious syllabus policy update, and I will hand it back to you restructured and optimized by Friday.”

• Why it works: It shifts the value proposition from “come do extra work” to “let this tool steal a miserable chore off my plate.” Once they see a tangible artifact built specifically for their class that saves them three hours, the conceptual resistance starts to crack.

It makes sense to me, and I am going to try a version of it. A 15-minute tool demo followed by a sample solution to somebody’s real problem. I will let you all know how this works.

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.