My choices of what to attempt and what not to attempt were determined to an embarrassingly great extent by considerations of clerical feasibility, not intellectual capability.— JCR Licklider, “Man-Computer Symbiosis,” – 1960

I have known for a while that education needed to change. I could sense it as far back as the 2000’s as I was starting my career in education and technology. Education was badly out of sync with the information flows of the rest of the world, and the students knew it. Now, however, with the tools I and my students can build using AI, there is no excuse not to change if you value learning over certification.

I didn’t redesign my class because of AI. I redesigned it because content mastery is no longer the organizing principle of education. Learning the skill of process is. Along the way, I discovered that I could do things with my classes that I had always wanted to do: fold in the best ideas from deep thinkers about learning and learning strategies.

Web 2.0 was the first real warning sign that technology would undermine the way education worked, but most people ignored it. Of course, you could go all the way back to the 70s and see the glimmerings of this in the works of Ivan Illichand Ted Nelson, both of whom could speculate on the trajectory of technology but realized we weren’t there yet.

In many ways this was still true in the noughties. Building stuff, especially complex stuff, was still difficult even for those of us who had been using technology for decades. It was a lot to expect faculty to create with this technology, much less students.

It took until the early 2010s before I was comfortable asking students to create multimedia with technology. Web authoring tools had matured enough to where students (most of them at least) to where the primary struggle was with the material, not the technology.

I teach two classes everyone must take, American and Texas Government. Most of my students view my class as an impediment to stuff they “really need.” Under these circumstances, it is easy for them to slip into a compliance mindset rather than a transformational one.

Motivation has always been at the top of my list of challenges. I have tried all kinds of tricks to see if I could make the traditional method of teaching and learning work. This model had me “leading” the class to knowledge. However, I was constantly frustrated when it came to naught. Sure, there were incremental improvements, but they weren’t transformational.

In 2018, I decided to reinvent my class structure and instituted a design model. Instead of essays and tests, the students built a public website about a challenge that is important to them. This was the first time I think I really implemented a constructionist strategy in my classes.

This meant that I needed to push the content into a secondary status and focus on their learning skills. After 18 years of trying to get my students to master complex theories of government, which they immediately forgot, this was a welcome shift in focus.

I discovered that this direction was not a panacea, however (I wrote about this in Learn at Your Own Risk). Students have been so well-trained in transactional teaching (I give you a grade in exchange for work) that the course became an exercise in frustration for both the students and myself as they struggled to know “what I wanted” when what I wanted is for them to stretch themselves creatively.

However, this structure, since it wasn’t obsessed with content (content was a source, not a pretext), adapted well to remote teaching. While my colleagues were obsessing over cheating, I had no tests to protect. Everything in my class was open book, just like the real world.

When AI came onto the scene, this also served to cushion the model. However, even though I thought and wrote extensively about the impact of AI on education, it was always a bolt-on.

Last semester, I had to do a quick setup for my classes and leaned into AI to help me with language and consistency. It helped but I could tell the model had some vulnerabilities. I introduced my students to AI and how to use it responsibly, but I could tell some of the blogs were written by ChatGPT. This told me the assignments were becoming meaningless.

I had more time for reinventing my courses this semester and decided to do another 2018-level reinvention. I recognized that this was possible in a much shorter time because of the AI tools I had been exploring. It was also long overdue.

In that redesign, I tried to shift the identity of the student from passive “consumer” to “designer.” This was a big shift for them, but it was also an artificial one. I understood it, but they didn’t. They were still playing a game that I was the master of and that they had little investment in.

I knew that AI demanded that they invest in the experience. If not, there was no incentive for them to do anything other than check boxes.

I have studied pedagogy for years looking for solutions to my challenges with students but also because I was looking for opportunities to integrate technology into the process. However, operationalizing good ideas is not the same as recognizing them.

Over the years, I had thought about the ideas of Papert, Harel, Csikszentmihalyi, Lavé, Wenger, Laurillard, and Wiliambut struggled to find systematic applications for my class. They were always, at best, bolt-ons and therefore had a diminished effect on the learning experience.

Last year, I constructed a Notebook LM library with my collection of pedagogical readings. I used it to construct just-in-time active learning for my Fall classes. I would come up with an idea, operationalize it in Gemini, and then check its pedagogical efficacy against my pedagogical library in Notebook LM. I’ve recently made an OER version of this library.

Therefore, it made sense to do a root-and-branch reconstruction of the class using these resources. However, there was a further step. In my AI-developer guise, I was doing a lot of deep exploration of how AI will impact how we live and work in the future. Discovering Digital Humanity also dovetailed nicely into this approach.

Carlo Iacono’s substack on AI and learning, which I highly recommend, talks about, among other things, that “direction” matters more than speed. In Discovering Digital Humanity, I talk about how we need to let technology enable us to create. We must learn to direct it to produce what’s in our heads.

AI only makes this easier. Therefore, instead of banning technology, we must teach our students how to become architects of the future.

Instead, we are training our students to be clarinet players when what we should be training them for is to be conductors. The AI becomes the orchestra. Getting it to sing is the real challenge.

AI will not let us digest this intelligence inversion for long. We don’t have another 20 years to figure out how teach in this environment. Education must shift from artifact production to teaching cognitive architecture now.

The factory is gone. It’s time for everyone to learn to be a builder. That’s exactly what my redesigned course does. However, it goes much further than my old design-based formula because it focuses assessment on process, not product. Learning should not be what you build, it should be about how you get there. Architects design. They let others build.