Month: March 2026

Building New Learning (Part 5 of 5): Back to Basics

March 11, 2026 / haymest

For newcomers then the purpose is not to learn from talk as a substitute for legitimate peripheral participation; it is to learn to talk as a key to legitimate peripheral participation. – Jean Lavé and Etienne Wenger, 1992

Having to express an idea clarifies for learners what they do not fully understand, especially if their interlocutor is prepared to argue and question. – Diana Laurillard, 2002

Learning is a conversation. Large language models are dialogue machines. We can dialogue with them about the design of our classes. Our students must dialogue with them as part of their learning. Our “AI-challenges” are rhetorical challenges, not technological ones.

Whether or not a technology is useful doesn’t come down to how good the teacher is at using technology. It turns on whether the teacher was able to use it to stimulate human dialogue with and among their students.

Good technologies facilitate conversation. Bad ones retard it.

Not All Technologies Facilitate Conversation

Modern education has always been technologically-based. Essays, blackboards, blue books, and classrooms themselves are all technologies. We just don’t recognize them as such because they seem “normal” to us.

Over the last 30-40 years, education has been buffeted by waves of technology, from the CD-ROM to Web 3.0. Many of those degraded conversation because they ignored pedagogy in favor of dreams of efficiency. Artificial Intelligence seems like another in the same litany of complex technologies that further isolate student conversations.

AI’s negative uses have been to try to automate the existing assembly-line methods of instruction, like automated grading or assignment creation. These seem like, at best, another attempt to make learning more efficient at the expense of authentic conversations. At worst, it’s an excuse to replace teachers.

AI is Different Because it Surfaces Tasks

AI is different because it does one thing better than any other computing technology: it lowers, and even eliminates, technical barriers to tasks. No longer do you need to know how to use Illustrator to create graphics. You don’t have to know how to code to create applications.

AI reverses teaching’s recent relationship with technologies. The task can drive the technology, not the other way around.

As you can see from the earlier blogs in this series, I have used AI to reinvent and deepen the quality of my courses. I have used it to synthesize the best of the pedagogical literature and to think deeply about the impact of my assignments on student learning.

I don’t have to be a programmer or a master of complex software systems to achieve these goals. I just have to be a good thinker and questioner.

This lesson should be extended to our students. We must teach our students to be good thinkers and questioners, not coders for them to remain competitive in the job market.

This means stripping learning down to its studs. AI is a humanities problem, not a technical one.

The humanities teach us to be better pedagogues. That makes us better prepared to teach humanity to our students.

We can either teach conformity or creativity. AIs reduce everything to a norm. If your class is normative, it can be defeated by an AI. If your class encourages human flourishing, it cannot.

The same goes for your class design. If you reduce your class to mechanical functions, you can be replaced. If you make it more human, then it will be evergreen. Learning is forever. Education is not.

My new/old approach to pedagogy was designed in conversation with texts on learning, not technology. It is not based on organizational logic. It is based on surfacing their humanity. If we surface the human, we will teach them how to surface themselves in a world where authenticity and humanity matter more, not less.

Strategy 1: Focus on Processes for Thinking

Our first goal in human learning is to develop processes for thinking. Disorganized, or sloppy, thinking undermines so many projects and products. AI is an efficient “thinker” but in the end, it requires humans to design for humans. Effective design is impossible if humans lack the scaffolding of structured thinking.

Modern education is based on evaluating products. Bowen and Watson, however, note that we need to teach dialogue(Plato would approve) as a mechanism for getting what we need out of our technological systems. This is not a technical exercise; it is a rhetorical one.

The Old Way: “Write a 5-page essay on Hamlet.” (AI gets an A).
The AI-Proof Way: “Have a conversation with ChatGPT about Hamlet. Argue that Hamlet was actually the villain. Submit the transcript of your debate and a 1-page reflection on where the AI beat you.”

We need to stop grading the essay (which is now a commodity). We should be assessing the student’s ability to steerand critique an intelligence.

Strategy 2: Use AI to Focus on Learning, not Education

We must teach our students to leverage AI tools. Banning them is counter-productive. Instead, we must use AI to teach higher level thinking skills. Bowen and Watson argue for the idea of treating AI as a “Naive intern.”

Ask the language model to generate content. It’s good at that. The student’s job is to evaluate that content. Have them generate an AI answer to your assignment. Then, their job is to vet whether that response meets all of the requirements.

Effective critiques require a deep understanding of the subject. The AI can provide a superficial (or seemingly deep) response. Even if it is substantially correct, it is still subject to confirmation and bias checks.

This shifts the cognitive load from Task Execution (writing words) to Task Stewardship (verifying truth). You don’t need to understand the code behind the bot (or even model weighting) to be able to do this. You just have to ask good questions.

Strategy 3: Reintroduce the Human Premium

Even before AI, I struggled to get my students to connect meaning with what they were doing. They operated as the cogs in the educational machine that they were. Anything that humanized that was, at best, at distraction.

However, humanity is the thing that separates us from the machines. Agentic AIs like the recent Einstein agent, exploit this mechanistic structure of education. If the class is a checking-the-box exercise, an agent can take it for them.

In the workforce, as Emad Mostaque pointed out in a recent interview, if your job is to be a machine, you will be replaced. Most office jobs are mechanical and require relatively little creativity on a day-to-day basis. Humans are expensive, especially when you can “replace” them with a bot that costs a couple hundred dollars a year.

It is our humanity that will save us from this fate. Therefore, we need to stress human connection as central to our pedagogy. In my classes, the Gold level reintroduces the “human premium.” To get to this level (equivalent to a “B”), they must relate human issues directly to the more abstract debates that dominate our political discourse.

Machines cannot replicate their lived experience, much less the combined lived experience of a group of students. This is why the insights of Lavé and Wenger’s Situated Learning and Diana Laurillard’s “Conversational Spaces” are so powerful and even more relevant in an AI-driven world.

This World is More Human

One of the things I’ve experienced as a teacher is that, by offloading so much of the logistical load of running a class, I have that much more time to focus on the human element of teaching. The conversations I’m having with my students are deeper and more meaningful than ever.

These are the things that matter in teaching. I have only given up the things that frustrate me: the assessment gaps, the lesson planning, the assignment programming, etc.

Instead, I’m digging back into pedagogical roots stretching back to Socrates. I have created a “cheat sheet” to get you started on that journey. Also, use the OER Pedagogical library I’ve assembled in NotebookLM. Even better, make your own.

AI makes so much more possible. We can reinvent the world through dialogue, not code. We just need to dig the score sheet out of our dusty pedagogical cupboards.

Our job is to be conductors of learning. I can’t wait to hear what beautiful music our students will create with their new AI-powered instruments. We just have to teach them how to make their own music.

Building New Learning (Part 4 of 5): Navigating Conflicting Currents

March 6, 2026 / haymest

The pupil is thereby “schooled” to confuse teaching with learning, grade advancement with education, a diploma with competence, and fluency with the ability to say something new. His imagination is “schooled” to accept service in place of value. – Ivan Illich

ACT 1: The Local Friction

The student looked at me incredulously.

“Why can’t I do the next module?” “

“Because you haven’t mastered the last module,” was my reply.

“But I completed it.”

“Yes, you did some work but you did not demonstrate the minimum mastery necessary to continue.”

This is a common conversation with my students these days. They are used to just sliding by and are really struggling with the idea that learning is a process. How are you supposed to evaluate process when no one seems to recognize it exists?

There are two systemic challenges to assessing process over product in our current educational systems. One comes from below from the students and the other comes from above from administrators and the policymakers that are pressuring them to demonstrate “accountability” in exchange for the public funds that make public higher education possible. Both sides are squarely focused on product.

ACT 2: The Systemic Vise

My students are used to checking in and out of courses and dropping in to do the minimum necessary work to create marginal products to “demonstrate” their learning. This is a suicidal strategy in a world where AI does a lot more than getting by.

Students treat each assignment as a product. If you miss one, then you simply make up for it on the next one. One thing they really struggle with in my class is understanding that getting “0’s” on Gold and Platinum assignments is part of the process.

I have some leeway (unlike many K12 teachers) in messing with their realities, because of a natural deference to professors. However, the “best” students, who are best at playing the school game, are most likely to complain about me “messing with their GPA.” Few understand what I am trying to do.

On the other side of the coin, my college is laser focused on “student success.” This is a result of a legislative mandate for colleges to prove their worth to receive funding.

This mandate is complicated because no one seems to know what “student success” actually means. The simple answer is to fall back on credentialing and completion. Of course, no one wants to hear that the easiest way to achieve this metric is to make all the courses easy.

My course exists between these two systems. So far, I haven’t gotten a lot of pushback from the administration, but I can see that happening if my failure and drop rates spike due to students playing the old game and running up against the realities I am trying to teach.

These are systemic problems that any course that takes AI seriously has to face. Large language models challenge the logic of product-based education. Just bolting them onto outdated pedagogical practices on students who conflate school with learning will not work.

However, the vast majority of “AI-transition” policy focuses on doing just that because of the realities of being caught between the two poles of administrative/legislative priorities and student expectations. Donella Meadows, the noted systems thinker, writes, “Policy resistance comes from the bounded rationalities of the actors in a system, each with his or her (or “its” in the case of an institution) own goals.” (Thinking In Systems, p. 113)

Systemic resistance is real. Meadows goes on to say that, “[R]esistance to change arises when goals of subsystems are different from and inconsistent with each other… In a policy-resistant system with actors pulling in different directions, everyone has to put great effort into keeping the system where no one wants it to be.” (Thinking in Systems, p. 113)

In other words, no one has to love the system for it to persist. More importantly, these groups pulling in different directions actually strengthens the system’s resistance to change, because the one thing all actors can agree on is the maintenance of the system.

The students want the system of product-based credentialing to persist because they’ve staked their livelihood on it. Colleges are likewise invested in it.

Then there is the problem of valuing the process itself. If both system actors see the value in producing a product exclusively, learning becomes an “event” of production. If the course focuses on the process, the rhythm of the class must be different.

My students often have personal crises over the course of the semester. Their student existence is often precarious with family and work pressures interfering with their studies.

Within the constraints of the industrial semester system, it’s difficult to modulate the speed of the course for them. That 16-, 12-, 8-, 5-week clock is always ticking, and I can’t really change that.

Some students can play catch-up in my system, but most can’t because there isn’t enough space for them to learn the lessons they need to learn in order to proceed. After a certain point, they can no longer get to the end of the course before the semester clock runs out.

This is not the fault of the course structure. It’s the fault of the outdated industrial clock ticking.

Lately, my institution has focused on the laudable cause of “student care” to ameliorate this problem. My course risks looking “uncaring” by insisting that education requires an investment of time. Process takes time.

Is it really caring to send them out into the world without an understanding of how the learning process works? How are they going to learn the process of lifelong learning that will be essential in a shifting technological and economic world?

It is easy to see how these systems produce inferior products that will have difficulty adjusting to the processes of an AI-driven world, where product is plentiful and processes such as verification, curation, the human premium, and taste will be decisive for their chances of “life success.”

ACT 3: Surfing the Currents

My approach to this wicked problem is to have the students produce a tangible work product as a demonstration of their learning. However, I focus on and assess the importance of process in my pedagogy.

It is this discordance of pace that is central to the friction that I must surf. I’ve seen this before as my courses have been process-based for a long time. The big difference is that the weight of my assessment has continued to focus on the products of my students’ work. This has shifted with the latest course redesign.

To cite just one example, my blogs were so easily gamed by AI that they lost their functional value in the process. Students viewed them as product, not as part of the process of refining their storytelling skills before transitioning them to the visual medium of the web. They’re not part of the course anymore.

The sequence of process-building from one module to the next is central to the pacing of the class after the redesign. Even moving through the modules is a building process. This is essential pedagogical practice going back to Vygotsky’s Zone of Proximal Development.

I have already seen two students suffer crises that have knocked them off their pace. One was able to catch up. The other was not.

The only alternative to plowing on is to revert to the old, compromised system. I’ve seen colleagues do this and their life is filled with a losing battle against AI-generated product.

I worry about the systems of education being able to adapt to the frequency and amplitude of the waves striking it. Learning is still in those systems but it risks being washed away along with the vulnerable products.

Can my course survive? Only time will tell.