Month: September 2025

“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

When I first started teaching, I thought that the students were cheating me when they cheated on a test or paper. This evolved into thinking the students were cheating themselves out of their own learning when they did that. Since at least the beginning of ChatGPT, I have realized what the students are actually cheating is a meaningless system of education.

This is a battle the system cannot win. It should be forcing teachers and administrators to ask some very deep questions about what the purpose of this educational exercise actually is. Is it to prepare our students for some sort of job or is it to prepare our students to face a world outside of school? While there is a lot of overlap, those are not the same thing.

If our primary purpose is credentialing, then we are marking them as being capable for a job. This shorthand for competence evolved over the last 75 years, where people are ruled out of jobs because they do not have a college education. However, if you ask the employer why they put this requirement in place, they often can’t really give you a good answer.

The system doesn’t communicate clearly to the students what the purpose of their learning is. Therefore, they buy into this mythology as well: that somehow having a piece of paper that says they graduated from an accredited institution prepares them to succeed in the world. If you ask them why this is, they also can’t give you a good answer.

The third group is the educators themselves. They are vested in a system whose purpose is to spit out degrees. Too often “learning” is measured by that benchmark. We are the drunk driver looking for his keys under the streetlight because that’s where the light is.

None of these benchmarks have anything to do with learning. This is the sad truth that sudden access to generative AI has stripped bare, but it has been recognizable for those who were paying attention since at least Web 2.0 came on the scene in the mid-2000s.

Culture is a hard thing to change. We have created a culture that supports the automated production of graduates in higher education. The students know they are widgets and behave accordingly. It’s just an exercise to get a degree. School becomes nothing more than a hazing exercise.

Why should we be surprised if, under these circumstances, the students rebel? A whole industry of products, from anti-plagiarism software to tools that limit internet access, has sprung up to aid the system in its vain attempts to stem that rebellion. However, to paraphrase Princess Leia: the tighter you grip, the more students will slip between your fingers.

Those of us who care about real learning find ourselves trapped between institutions demanding quick “student success” and students who just want to finish as easily as possible. Into this mix comes generative AI, which, if used poorly, makes shortcuts easier while stripping away the experience of learning. The result should not surprise us.

However, those students who learn how to dodge work in college using AI are in for a rude awakening when all they can do it is to try to get out of working in their jobs. If you can dodge work at your job using AI, you can be replaced by AI.

This applies to education as well. The more rote we make it, the more vulnerable it is to being replaced by automated systems. This isn’t learning but it is “efficient” at moving widgets through the process toward their “completion.”

I have written before about the tremendous opportunities that AI presents to us to teach and learn in very different ways. If we use it to augment ourselves and not automate ourselves, it can become a powerful tool for both teacher and student. If, instead, we spend all our energies somehow trying to freeze it out of the system, we are only asking for disappointment, frustration and eventual obsolescence.

Who is actually cheating here? Is it the educational system clinging to outdated methods and mechanisms for achievement? Or is it the student somehow trying to navigate an arcane and unfamiliar world of education in pursuit of some elusive goal? Or is it both?

More so than most technologies, AI provides us with tremendous opportunities to reinvent ourselves and what we are doing iteratively. It opens new ways to apply critical thinking, iteration, and creativity to augment our learning.

However, systems must also change. Assessing students based on how well they do on high-stakes tests or rote papers is a tool of convenience (looking for our keys). Yet, that is still the dominant method of assessment practiced in education.

Instead, all instruction should force the students to solve problems using AI as a partner. Whether that problem is writing or designing a physics or math experiment, forcing students to engage in the material in this manner strengthens their motivation, skills, and their interactions with technology.

I use AI in the classroom to generate just-in-time exercises that reinforce lessons that the students don’t understand fully. I also teach them how to use it to overcome their own creative and writing obstacles by using it to brainstorm assignments and edit them dynamically. This is just the beginning of what is possible.

On the instructional design front, I used it to reinvent my course in days instead of weeks by removing much of the detailed rote work of balancing and sequencing assignments. I have also used it to simplify complex instructions for assignments, which is critical to the practice of formative assessment strategies.

In the real world, your boss doesn’t care if you “cheat” if you get the job done. However, getting that job done will require you to have the skills to augment yourself with AI-driven tools, whether you’re a teacher, a student, or a professional employee.

For those of us trying to untie the Gordian Knot of the intertwingled drivers of systems of education and disempowered students, life can be a very frustrating experience. Gaming those systems is a natural byproduct of this tension.

The good news there is that we can use AI to try to reduce our levels of frustration by easing rote tasks thrown up by the needs of the system. I guess that makes us cheaters too, but I can live with that. If we don’t learn to “cheat” the system ourselves, we’ll cheat our students out of real learning.

“The purpose of education is knowledge, and yet education is blind to the realities of human knowledge, its systems, infirmities, difficulties, and its propensity to error and illusion. Education does not bother to teach what knowledge is.”  – Edgar Morin

“Learn from everyone; follow no one; look for patterns; and work like hell.” – Scott McCloud

One of the hardest tasks as a teacher is to teach critical inquiry. As the world of information becomes increasingly complex, the natural human reaction is to accept things as they are. This was never a good idea, but now it presents an existential crisis for us.

Primary school teaches us to conform. It is a socializing instrument and many of the debates that occur around it are about what kind of socializing we want to see. This is an outgrowth of industrial thinking, where there was an expectation that workers showed up at a certain time to become part of the machine, whether that machine was an office or a factory.

We are now at a fork in the road of human existence. With the emergence of Generative AI, we must ask what kind of perceived reality we want to live in: one that forces us to conform to machines and the humans that control them or one in which we use the machines to look at the world critically and build our own realities.

Too many people today accept the world as it is. This is particularly frustrating for a technologist like me because I see technology as a liberating force, not another version of the stifling shop floor.

For many years, the operation of technology was so complex that people learned how to do a certain thing a certain way, and then accepted the flaws of whatever technology they were using as a given. That made sense for multimillion-dollar factory machines, but never for software or digital tools.

I have a low tolerance for badly designed pieces of software because I know that there is no physical reason they should fail in this way. Think about how much training is required to overcome bad design decisions.

The idea that you must bend humans to the will of the machine is a direct outgrowth of this acceptance attitude towards technology.

Many of the debates I see around generative AI center on this misapprehension of technology. People have been trained to accept bad results, and generative AI can be hilariously bad.

This has also been an excuse to keep it as far away from education as possible. I am constantly bombarded by comments like “it will mislead our students“ or ”it doesn’t do this task well.”

I always push back and ask these critics whether humans do this any better. If you ask a human to summarize a book with no further instructions, wouldn’t they explain those parts of the book that were most meaningful to them and potentially miss the entire point of the author. Humans get facts wrong all the time, sometimes willfully.

In both cases, there is no substitute for a critical eye toward any information you receive. The industrial mode of acceptance has polluted the information environment. As the sheer volume of information has increased exponentially, the natural human reaction is to ignore all of those parts that don’t fit their conception of the world.

Generative AI is no better and no worse than most humans when it comes to providing answers. However, the major companies that control them are selling them as “answer machines” to be used uncritically.

This makes sense as a marketing strategy, but it leads to a lot of the problems people are having with them. We expect machines to be perfect, so we hold them to a different standard than “my dumb Uncle Al.”

Marketing should be treated with a high degree of skepticism. AIs are flawed because humans are flawed. AI models get their data from humans. They import our own biases and blindspots in the process. Furthermore, those models are weighted by human decision makers with their sets of biases about what information is important or should be discarded (or banned).

We have a tool for dealing with this problem that is usually only taught in a higher education setting. That tool is critical inquiry.

Scholars know to treat any bit of information with a high degree of skepticism until it has been thoroughly tested. Most people are not trained scholars. Even scholars aren’t perfect. They are subject to the very same human fallacies and too often accept the canon in their fields uncritically.

I like torturing AI. I can do things to them that I would never do to a human. That’s because they don’t have any feelings. One advantage of this is that I can engage in brutal critical inquiry when it comes to interrogating a chatbot. It doesn’t care.

But there is a hidden lesson here. By teaching this method of inquiry against an unfeeling opponent, we can teach ourselves not to accept other peoples’ realities as a given. We can use it to break the training of conformity that the industrial age has inflicted upon humanity.

The mere consideration of this is an alien concept to even highly educated people. We are uncomfortable when paradigms are questioned. As a constructivist, I make a hobby out of questioning paradigms. Some are worth keeping, but others inflict a great deal of pain and suffering on humans.

If we want to turn generative AI into a normative device, we can do that. However, it’s always going to be vulnerable to those who would seek to question it. The only way around this is to impose strict guardrails that limit the nature of inquiries to those that don’t threaten existing paradigms.

Furthermore, the threat to human jobs and livelihoods that is often posed by AI are a direct outgrowth of its willingness to exist unquestioningly within paradigms. Since it’s only mimicking us, that’s all it can do,

However, it does so more quickly and efficiently than any human can. It can also work tirelessly at those repetitive tasks.

Humans must be able to rise above that to be competitive in a marketplace dominated by a wide range of AI applications. We need to be the ones who manipulate reality because the AIs will only mirror back a flawed version of an accepted reality.

Critical inquiry is the only way around this trap. If all you ever do is write pre-selected code to adapt subroutines into a larger piece of software, that’s something the AI can do very easily. If you understand the purpose of that larger piece of software and how to adapt it to function more efficiently or humanely, that is something that the AI can only help you do. However, in order to do this, you need to function as a critic.

If we are not teaching ourselves and our children this, we are setting up humanity for failure. This will exacerbate economic dislocations and create lots of misery throughout the world.

To exist in a world dominated by machines, we need to make better humans. That does not mean we turn ourselves into robots, but that we lean into the distinctiveness of what it means to be human.

If we want to thrive in a machine-dominated world, we must resist becoming machine-like ourselves. The future belongs to humans who question.

“We refer to a way of life in an integrated domain where hunches, cut-and-try, intangibles, and the human ‘feel for a situation’ usefully coexist with powerful concepts, streamlined technology and notation, sophisticated methods, and high-powered electronic aids.” – Douglas Engelbart, 1962

Today I want to revisit an old topic: teaching augmented with technology. I’ve been building and rebuilding classes for over 20 years now and I’ve always tried to use technology to maximize the efficiency of my work. In the early days, just knowing how to cut and paste made a huge difference. But now, I’m discovering new horizons of AI assisted pedagogy and pedagogical creation.

Before this semester, I embarked on something that in previous semesters I would’ve considered absolute folly. I did a complete course redesign in hours instead of weeks.

Rethinking Teaching in the Age of AI

In 2018, I completely revised my approach to teaching my subject (American and Texas government) to incorporate a design process into the class. This allowed me to customize the class around the interests of my students and was entirely driven by the motivation factor. I described this in detail in my 2020 book Learn At Your Own Risk.

Student motivation drove this reinvention. My classes are required, and most students don’t want to take them.

However, the best plan fails on the battlefield. Most of my students have been well-trained to expect very little and to give very little to their college courses. They’re always a few who are highly motivated, and they loved my system and benefited from it. However, the rest of the class was usually trying to game the system.

AI Chatbots only made this easier. It became clear to me that I needed to do some significant modifications. I put these off because it wasn’t clear to me whether I would be continuing in my pursuit of teaching or abandoning that in favor of the Knowledge Navigator project. Now that I have a full-time faculty position, I can do both.

Rapid Redesign Under Pressure

I did not have much time to implement the strategies between the time I found out I was going to be full-time and the beginning of classes. Major iterations always involve a balancing act between ideas and execution.

At the same time, I didn’t want to teach five classes using the old method. I needed to streamline the structure, incorporate AI directly into the learning process, and set it up for both in-person and asynchronous classes.

Normally, this process would’ve taken me weeks to do, but with all the onboarding and other meetings, I was left with three or four days. The thing that changed my approach though was the development process of coding with an AI partner to generate python code as well as the brainstorming process I use when writing.

I first took my existing course material, which involves a design process of writing three blogs and converting those into a website that the students could maintain as a tangible work product from the class. I asked the Chatbot to analyze my existing assignments and suggest ways in which I could inject AI into the creation process for the blogs.

Within a few iterations, I had a new point structure and set of assignments that simplified the somewhat cumbersome process I was using over the summer. This was essential for the asynchronous classes because I would not be there to keep them on course. I was dependent on the online materials to do that.

Testing and Refining the Model

Once I had the assignments, I dropped them into the NotebookLM library that had my collection of assessment and pedagogical literature   in it. I then asked it to assess them against formative assessment and conversational pedagogy texts like Diana Laurillard’s Teaching as a Design Science and Dylan William’s Embedded Formative Assessment (and more than 50 other sources).

Since this DNA was already plugged into my original structure, the chatbot maintained that approach to teaching and learning. Every one of the assignments got high marks from my shadow teachers in NotebookLM (email me if you want access to the library).

Finally, I used the chatbot to simplify the language and develop rubrics for each of the new assignments. This whole process took three or four hours. The most time-consuming part of the process was updating my class shells in the learning management system.

Once classes started, I discovered a few mistakes along the way. However, there were probably fewer mistakes than I would have made doing it myself and none of them were structural.

Results in Practice

I am teaching two accelerated courses this semester. Both are asynchronous, so they presented an acid test of the design of my classes. Approximately 80 to 90% of the students in those classes could follow the sequence and created better than average first blogs. The extensive employment of reflection exercises dilutes the possibility for them to use AI to complete assignments unless that is the assignment.

I know this is a small sample size, but I can say with some confidence that the courses are no worse than what I was doing in prior semesters. The scaffolding exercises work better and are more logical.

For my in-person or synchronous online classes, this structure, coupled with AI, allows me to create live experiences with immediate feedback within the bounds of the class itself. I’ve been doing this for a while, but now I’m able to shift between synchronous and asynchronous modes on the fly.

Lessons from Conversational Pedagogy

I have been working on understanding conversational spaces across all physical and virtual dimensions, which goes back to the work I did for Arizona State University’s ShapingEDU project. As I discovered, the best learning experiences shift effortlessly between all environments as needed.

Learning is not a linear process. Like the Knowledge Navigator concept, it is a web of experience. The more I can diversify that experience for my students, the better the learning will be. I am still making some tweaks this semester, but I am a lot closer to that ideal in my courses than I have been in some time. This AI process has made that possible under very tight time constraints.

The Bigger Picture: Adaptation and Antifragility

I recognize that no core structure should remain static forever. As I iteratively adapt my courses to new possibilities, having an AI partner to assist me makes change far more feasible.

There is a lot of resistance to AI within academia in various forums. A lot of this stems from a reluctance to do the work necessary to rebuild entrenched teaching methods. However, the technology provides a “cheat” that teachers can use to significantly accelerate their process of change.

I’m conservative when it comes to new technologies. I need to see their utility before I’m willing to waste my time with them. AI is different. It’s like an onion. Every time I feel like I’ve reached the limits of what it allows me to do, I find another layer. As I described in a blog a few weeks ago, I think the next frontier is on-demand coding.

AI presents real challenges to the way we teach and learn. Smart strategies are necessary to adapt and thrive using this technology. Nicholas Taleb describes this process as developing antifragility.

If we can use technology to reinvent ourselves, we can thrive under these circumstances. More importantly, by teaching these tools, we’re giving our students superpowers to face the challenging future that awaits them in a world of AI, climate change, and political uncertainty.