The Order Matters More Than the Tool

 Over the past few weeks, I kept seeing the same move in places that have nothing to do with each other.

For the past year, our Design Futures workshop has started the same way. Participants take a piece of paper and sketch a bicycle built for a Nebraska winter. Then they snap a photo of the sketch and ask AI to reproduce it, sometimes from the drawing and sometimes from a written description. By the time AI enters the process, a lot of thinking has already happened. Sometimes the results are fantastic. Sometimes the AI hands back a bike that floats above the pavement, not by intent, but because that is what it perceived.

Last week we interviewed Nick Husbye on the podcast, and he described something curiously similar. Nick asks his preservice teachers to draft a lesson plan by hand. When they have a draft, they let the AI come in and critique the plan. What Nick teaches is that AI can be a very good critic, and that the teacher still has the agency to say, “I hear this critique, but it doesn’t apply here, and here’s why.”

The third example comes from our work with Southeast Community College, where we are asking Writing Center tutors how they work with AI. One tutor described a simple structure they give students.

1. Start with the rules. Know what your instructor allows so you don’t break them.

2. Use your own words.

3. Only then bring in AI to critique, improve, and suggest.

Finally, in Tina Shetabi’s work on using AI in the fashion design process som students reported that starting with AI gave them a strong sense that it is not their work. When they started with their own sketch, they felt more ownership.

Created with ChatGPT

The pattern

These are four examples from the last few weeks, and there are more. None of these people coordinated. Each one puts human thinking first and AI second, sometimes third. Each treats AI as a critic or collaborator that extends the existing work or points out what isn’t working. The ideas stay thoroughly human. As Nick puts it, AI should amplify your thinking, not export it.

Why the order matters

All four cases involve novices, people entering a domain they are becoming experts in but are at the beginning stages. That might mean designing a bike, writing a lesson plan, drafting a paper in a writing class, or, designing a garment. For novices, the priority is learning the domain and thinking it through.

A while ago I called this the novice problem. Novices are less likely to develop if they start with AI. We want them to start with their own thinking, let AI mirror back what is working and what isn’t, and then iterate. Some of that iteration can happen with AI, but in many cases I want them to go back and iterate with their own thinking.

The novice’s first draft does two things. It gives them something to compare the AI against, and it gives them a sense of ownership.

When students start with AI, they tell us they don’t feel ownership. Their sense of agency is taken away. They often go with whatever the AI made, and they become less and less critical. We want students critical enough to compare what they intended with what is on the page, and to learn from that comparison instead of being replaced by it.

The bikes show how this works. Students asked AI to add cup holders, which is a fine idea. Some kept pushing until the bike had 30 cup holders in ridiculous places. They knew immediately it was funny and implausible. Because AI will go as ridiculous as you ask, stretching it shows students quickly where it doesn’t understand the physics of a working bike or a wearable garment.

That signal leads to metacognition. Even as novices, they are the ones who can evaluate the result, because the work started with their intent.

What this isn’t

This is not an argument against AI. In each case, the AI step adds real value. It also isn’t a universal rule.

The sequence lines up with decisions universities are making about when to bring AI into the curriculum in a more organized way, including the debate in computer science education about when and how much to introduce it. In most of our interviews with professors and students, both groups say the same thing. In entry-level classes, AI makes little sense as the creator. It becomes a crutch because it is too good, and students build up cognitive debt. Once learners move past that stage, the roles can shift. They need to learn how to use AI when they’re stuck, when they lack ideas, or when they need to do research. There are times when AI should go first.

What schools can do

Most AI policies, in K-12 and higher ed, focus on which tools are allowed and what counts as acceptable use. That’s fine. Many policies also leave a lot of agency to instructors, which is great, but instructors still need a framework to work from.

The better question is about sequence. What do students do, and in what order? The answer depends on how novice they are and how much expertise they bring. This changes the policy conversation. A tool can be allowed at one point in the process and not another, with a different intent at each stage.

A human sketches the bike, drafts the paper, or designs the garment. Then AI comes into play, because we can iterate much faster with it. We can ask for small changes or big ones, extend the thinking, or get a critique. Everything is judged against the original intent. That keeps the learner in control, even as a novice, and keeps the product connected to the person who created it.

Three practices make this work.

Model it yourself. Nick models his own failures with AI, and I try to do the same. I show what I used and where it didn’t work. That gives students permission to critique AI without rejecting all of it. It isn’t all in or all out. Sometimes I dump what the AI produces. Sometimes I’m amazed. Usually I’m somewhere in the middle. And sometimes I say out loud, “This isn’t very good. I prefer what I made myself, and I’m not sure AI was worth using here.”

Make comparison the assignment. Ask students to compare their intent with the final product and explain what they learned from the critique. That metacognitive move is where much of the learning happens.

Grade the comparison. If your students respond to grades, put significant weight here. AI output quality varies, so grading only the product partly grades the AI. Grading the comparison grades the thinking.

Where have you seen it work?

Nick’s advice to his students is simple. Write first. Use AI, but use it a little later.

These are observations from all of us grappling with how to teach in the age of AI. So my question is if you seen this sequence work, and where have you seen it fail?

Not What I Signed Up For: AI and Learning to Write

 Every technology, every change has unintended consequences on the world. Impacts that in hindsight are obvious but are actually hard to predict. This week I want to think through how teachers of writing are grappling with the impact of student AI use on their teaching life, and why I think the burden should not rest on them alone.

Recently, an acquaintance (who may be reading these words) admitted that after many years of teaching language and writing to English learners in higher education, she decided to retire, much like Victoria Livingstone, partly because of the rise of AI. This rhymes with what I am hearing around me. People are not leaving the profession in droves, but they are frustrated and challenged. What I hear is exhaustion and sometimes dread from the conversations instructors have to have a few times a week with a student who very clearly used AI to produce an assignment.

AI has gotten very good at creating polished texts at a linguistic level far above what a novice writer produces. I am not saying it’s better. I am saying it is smoother and appears more scholarly. Instructors dread two potential outcomes. The first is the defensive “I did not use AI” and the self-righteous indignation that comes with it, emotionally taxing and very unhelpful for learning. The second is the admission of guilt, explaining the stressful situation [time constraints, last-minute debacle, fear of failing, feeling of inadequacy], some true, some augmented, but real. It is followed by apologizing, sometimes heartfelt, always unhelpful for learning.

The refrain I hear is simple. This is not how I want to spend time with students. This is not what I signed up for. It is eating up my time, and if I fall for an AI assignment, I feel stupid. Not because I fell for it, but because I wasted my time giving feedback on a paper written by AI. Instructors hate viewing each successful student with a question mark. On the other hand, they know they cannot let it go. If instructors ignore the use of AI, students will soon tell each other, and the ones not using AI will be pushed to use it so they do not fall behind. If students do not feel valued for their hard-earned effort, they will not make an effort.

So do I foresee a language and writing teacher apocalypse coming? Perhaps. One option is to wait it out, and past innovations may be instructive here. In the last 20 years the rise of digital platforms and curricula (mainly K-12) pushed some teachers to retire or change professions. There was a dip for a while, and we came out of it with a new generation of teachers who take the platforms for granted.

A second option is that AI companies and institutions provide the kind of detection that reduces the need for policing and conversations. Each assignment would arrive with a message that evaluates the probability it is AI. The rise of Pangram and watermarking in the models helps that along. Detection will never be perfect, and false positives carry real costs, especially for English learners. Still, institutional detection moves the burden off the instructor and returns it to the level of concern we had before AI, because plagiarism and cheating were always part of the landscape.

In Abundance, Klein and Thompson argue that as a society we overemphasize the risks of action and underemphasize the costs of inaction. Waiting it out is inaction, and its cost is a generation of experienced instructors. I am calling on institutions to start acting and not leave it to overworked and underpaid instructors who are just trying to make it to next week with feedback that leads to better thinking and better writing. Their exhaustion is one of those unintended consequences, and it is no longer hard to predict.

The Agent I Wish My Students Had

 It is not a secret that I use AI, a lot. The most consistent use, though, is helping me manage all of my projects. I currently have over 15 research projects at different phases, from the idea stage through IRB, data collection, analysis, writing, and revising. I manage these projects and the next step in each using Claude, and I review them every Monday and Friday. On Monday I prioritize and set deadlines, and on Friday I update on progress.

This has changed my work significantly. Once a project makes its way into my AI workflow, I'm unlikely to forget it, and I am less haunted by the idea that I am forgetting something (what David Allen’s Getting Things Done calls open loops). It also allows me to have more projects, because I can manage more with my “digital brain.” Finally, AI helps me see which items I am ignoring. Right now I have a writing project about agency that has been dormant for 9 weeks, and my AI asked, is this still a project, or should I put it in a different category?

It reminded me of an idea Kathy Wilson and I discussed with the late Dave Brooks. At the time (over 20 years ago) he shared a writing support program he had created for students with learning disabilities. We put our heads together to see how we could improve it. The main idea was to develop a set of scaffolds for self-regulation of learning. The main challenge was are inability to have any meaningful way to differentiate for students and tailor it to the needs of the learner. The project never got off the ground, probably because both Dave and I had short attention spans and we just moved on. Now with AI agent in the mix I wonder if it is an idea worth revisiting

Screenshot of my Research Project Tracker

This weekend, I started thinking about how I manage my work against the backdrop of the rise of personal agents such as Meta’s Muse and OpenAI’s Dot. These agents are effective when they have context and access to our relevant data, including calendar, email, and other systems. It made me think that a good way to use AI for students would be a learning regulation agent. Imagine an agent that is connected to a student’s calendar, email (for messages from teachers and extracurriculars), and the LMS (learning management system) for assignment deadlines, tests, and even grades. That is the context the scaffolds Dave, Kathy, and I imagined never had.

The agent would be able to tell each student at the beginning of the week about major assignments and tests and help create a study plan, perhaps offering to create a practice test or index cards for studying. The possibilities are quite promising. For example, a teacher can give students 20 minutes on Monday to confer with their agent so they have an individualized learning plan for the week. On Friday, the agent can help students review what happened, which will help the agent understand what works for each student. Index cards may not help one student much, while practice quizzes with a timer really do.

In this way, each student would get occasional updates on which strategies work for them to accomplish goals, and be better prepared to meet challenges and plan well in the future. A scaffold is meant to come down, though. Two questions I am left with: first, how such an agent would hand the planning back to the student over time, or should we just assume that working with an AI agent to manage your study and work will be the way of the future; second, how do we make sure that the data is kept safely and without privacy concerns? 

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!