Author: haymest (Page 2 of 12)

“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.

“Tools provide a path, a context, and almost an excuse for developing enlightenment, but no tool ever contained it or can dispense it. Cesare Pavese observed: to know the world we must construct it. In other words, we make not just to have, but to know. But the having can happen without most of the knowing taking place.” – Alan Kay, 1993

Technology has always shaped how we think. I’ve written before about how technology turned me into a constructivist thinker. In short, that means I believe we create the world we live in. However, I am also a systems thinker, and what that means is that I understand that we are constrained by systems of technology, knowledge, culture, and practice even if the systems are constructions.

Over the last 50 years, we’ve watched the gradual democratization of technology. AI takes this to a new level. What was once a domain dominated by a technical elite, however, now risks becoming the domain of those who have the critical thinking skills to use AI tools to solve complex problems and drive new innovations.

Ted Nelson, in his manifesto Computer Lib/Thinking Machines, envisioned computers as “Thinkertoys” to liberate us from rigid, industrial-age systems. “We do not make important decisions, we should not make delicate decisions, serially and irreversibly. Rather, the power of the computer display (and its computing and filing support) must be so crafted that we may develop alternatives, spin out their complications and interrelationships, and visualize these upon a screen.” Using Thinkertoys effectively requires critical thinking skills.

However, Bill Gates’s letter to hobbyists in 1974 pointed us to an alternative future where someone else would code for us, because that was where the money was. We traded flexibility for convenience. Applications became our gatekeepers. For most of us, the ability to shape our digital environment vanished.

We accepted the software we were given because learning to code was too difficult and too costly in time. Most importantly, we adapted to their logic instead of thinking critically about alternative possibilities.

We allow ourselves to get into traps largely driven by applications and systems over which we have no control. Most of the pain I hear from computer users stems directly from technology that fences them in, doesn’t work right, or both. Whether we’re talking about web applications that frustrate us or corporate systems that undermine productivity, we are at the mercy of those who speak the language of machines.

In parallel to this world, there’s been a hacker world of coders that have developed their own software to do exactly what they wanted to do. However, this takes a level of expertise and commitment of time that most of us don’t have.

AI will change that calculus. For the last couple of months, I have been meme coding using an AI and it’s amazing to me how fast I have progressed while developing a relatively complex program.

A few weeks ago, a professor at another institution told me that he had created customizations to his learning management system using its API and meme coding. Learning management systems are one of those inflexible apps that we struggle with as we try to create the best online learning environments for our students.

This caused a lightbulb to go off in my head. Maybe we were finally entering the world that Ted Nelson had envisioned. This was a world where anyone could create whatever functionality they needed in a computer without the limitations of their coding skills or by relying on someone else’s coding skills.

However, even if this becomes simple, it’s still going to require sets of skills because you do have to know how to get what you want. This is a logical puzzle, not a technical one, and one that even many low-level programmers struggle with.

Master programmers understand that they are mediating between human and machine worlds and design software that serves the user. However, there are lot of programmers who simply must meet a deadline for a logic imposed by those who understand neither computers nor people.

Currently, I am debugging a portion of my application and, even though the AI is helpful, it does not explore all possible pathways and often gets stuck in a loop. I need to know the right questions to ask to get it out of that loop.

That process involves a combination of logical and lateral thinking. It also opens the door to more complex problems than simply getting the code to work right.

At the same time, I am dealing with students who are having trouble following rather straightforward directions and thinking critically about their own work. The real deficit in their learning is not computing or coding, it’s critical thinking.

This has always been the Achilles’ heel of education based on an industrial model. If the primary purpose is feeding students through a system, the easiest way to do that is down a straight path. The easiest way to go down a straight path is not to encourage them to think critically or creatively.

I am excited about the prospect of a maker world where I can customize my computing (and real world) existence around my needs. However, power should not be concentrated around even an elite characterized by critical thinking skills. If we can teach people to think like makers, to design rather than merely consume, we transform technology from a set of fences into a field of possibilities.

This is the real digital divide: not access to computing, but critical thinking abilities. Access to computing is a technical problem. Access to critical thinking is societal one. Without it, AI will not liberate us. It will deepen the gap between those who can shape technology and those who are shaped by it. This is the real threat of AI that no one is talking about.

It is time to get beyond being users of AI. We need to be architects of a maker world. There is an urgent need to integrate AI tools into our learning and work processes but do so in a critical manner that teaches thinking over replacement.

We must overcome the idea that we are subject to the technologies that given to us, and that includes AI. I don’t teach my students to accept AI. I teach them to critique it and use it to critique themselves.

We stand on threshold of a maker world where you can call up digital tools to do anything you want and build a digital world customized around you. This is a truly constructed world. That’s the world I want to leave to my kids and students.

Part I: Democratizing Imagination

Part II: Making Opportunity with AI

“To extend our abilities to think and perceive in new ways, and to translate those into tools that enable others to think and perceive in new ways, is probably the essential part of the human story.” – Howard Rheingold and Petar Jandric, 2015

I can build things I only dreamt of even a few months ago. This is both liberating and a bit intimidating because it opens whole new questions about what is possible.

If you look at technology as an evolution towards simplicity, we may have crossed a milestone in recent months or years. It used to be that coding was confined to a relatively exclusive elite. You had to have a certain kind of mindset for it, and then acquire the skills.

We need to consider how AI fits into the broader technological and societal picture. I don’t tend to believe in revolutions, either as a political scientist or as a technologist. They don’t really fit the patterns of human behavior. People are naturally conservative unless they see no other way out and change.

The same thing is true with technology adoption. There are a few who delight in doing things no one else can do, but most are more interested in getting through their workload.

This doesn’t mean they don’t have ideas or needs that technology (or writing) can express for them. It is our responsibility to continuously lower barriers to access those technologies.

Most people fear technology because it wastes their time and takes tasks out of their control. Yet this fear has never been about a lack of imagination. It’s about the barriers between them and their ideas. AI, like the PC in the 1970s, represents a massive lowering of those barriers.

For decades, education has focused on technical skills rather than logical ones, because the technology itself was hard to master. But AI changes that. We don’t think about “technical skills” when we pick up an iPhone, and soon we won’t think about them when we build with AI either.

The true revolution with AI will come with making. There are so many projects that I have thought of over the years that I have discarded because I don’t have the technical skills to code them properly. No matter how much I wanted these tools, they required prohibitive opportunity costs to realize.

A few weeks ago, I was on the T is for Training podcast where I’m a regular panelist. We were discussing the use of escape rooms for teaching and training with Laura Gehringer and Madeline Shellgren.

It suddenly struck me that I could generate escape room scenarios for my classes using a generative AI Chatbot. This is not just about games. The escape room example showed me how AI doesn’t just speed up existing workflows. It makes previously impossible things practical. That shift in feasibility is the real revolution.

I have built games for learning before. The challenge in constructing games in an educational setting is that to do it well is incredibly time-consuming. Most teachers don’t have time for that.

You also want to tailor the game around your instructional goals and style as much as possible. Therefore, sharing games across multiple subjects or pedagogical styles doesn’t always scale well.

I have been writing over the last couple of months about how AI can be used to significantly speed up logistical tasks in my teaching and classroom management. I had not thought of using it to construct games until my conversation with Laura and Madeline.

The next day, I was speaking to a fellow professor at a startup meeting, and we started talking about escape rooms. He brought up the idea of coding a game that would have the same effect but would be interactive and repeatable. My first reaction to the suggestion was that I thought it would be too hard. My brain was still in the manual coding world.

However, I recognized the connection between that and what I was already doing with the Knowledge Navigator project, which is far more complex. Given what I’ve learned about meme coding over the last couple of months I realized that this might be technically and logistically feasible for me.

It is amazing to me how much progress I’ve made on a relatively sophisticated technology project. However, with this progress, I’ve unlocked new, much tougher, questions than the technical aspects of the project.

My priority has always been focused on the task, whatever that task may be. When the tasks don’t align with the software, I inject the human element (usually me) into the equation. I’m always considering the time investment into any task and, if I can use technology to save me time, I do it.

If technology wastes my time, I have no patience for it. I am here to help my students, not fight with technology, much of which was never designed to work in a learning environment in the first place.

Thinking about AI in this way will open whole new possibilities for human employment. I’m not talking about prompt engineering here. I’m talking about operationalizing dreams.

Augmenting ourselves with AI tools will unlock creative forces that will accelerate all economies forward at a rapid pace that need not come at the expense of killing the climate. That’s because this will be an acceleration of ideas, not products.

The revolution will come in the form of human potential. AI will make it easier for everyone to define their future. We just need to recognize and grasp that (and show others the way). But realizing that future will require a different kind of literacy: learning how to think like makers.

…next up: life in a Maker World (Part III)

Part I: Democratizing Imagination

Part III: The Maker World

“Our fine arts were developed, their types and uses were established, in times very different from the present, by men whose power of action upon things was insignificant in comparison with ours. But the amazing growth of our techniques, the adaptability and precision they have attained, the ideas and habits they are creating, make it a certainty that profound changes are impending in the ancient craft of the Beautiful.” – Walter Benjamin, 1935

Less than 200 years ago, a small group of people decided what counted as art. They could do this because only a few had both the talent and the patronage to create it. The vast majority of art that was created and survived to this day was therefore images of the rich and powerful.

Speaking as an artist for a change, I also know that artists are an ornery lot. They frequently stepped out of line and painted things that they were not paid for. However, even then they represented a very small slice of the population with the imaginative and physical talents necessary to create complex works of art.

With the invention of photography, however, the level of physical talent required declined. Suddenly people who were previously excluded from expressing themselves individually had the power to do so.

Initially, the costs and time necessary to do photography limited the group of people who could create images, but with the introduction of technologies like the Kodak Brownie in 1900, suddenly the masses had access to creation tools. Now, we take over 2 trillion pictures per year.

This literally changed what we can see. Visual media are far more powerful than textual media, especially when a large proportion of the population was illiterate. But even today a picture is far more dramatic than lines and lines of text like I’m producing now.

Technology democratizes access to creation. It empowers people to do things that were impossible before. This goes against the centralization theory of technology, such as that expressed by Tim Wu, which argues that technology will inevitably be gathered into a small number of hands.

In an era of tech giants, that is an easy theory to believe. AI carries the baggage of Web 2.0, where centralized platforms like Facebook and Google built empires by controlling data and attention. Companies like OpenAI and Anthropic are explicitly modeling themselves on the first generation of Web 2.0 giants.

These companies were, in turn, modeled on industrial models where massive machines produced everything from automobiles to washing machines. In the industrial age size meant power. It manifested in systems of production that dictated how we worked and what was available to us as consumers.

Over the last 40 years, this model has been undermined as production has become far more efficient. Technology has allowed relatively small startups to become successful. Of course, the founders of these startups were raised in the industrial age and very much see size as the ultimate arbiter of success.

However, we no longer need thousands of people to produce one product. It can be produced by a few dozen people or even one person with the right technology.

We have also democratized access to art. We went from glass plate photography to the iPhone in the span of a little more than a century.

When we democratize access to creation, we empower new voices. In societies where people are increasingly frustrated by the old voices who seem to only lead them down blind alleys, this is a powerful force to counterbalance the conservative elites.

I agree that sometimes in order to build stuff you have to go big. However, why does this have to be the end state? We used to need a massive factory to do injection molding. Now I can create custom versions of the same thing in my living room with a 3-D printer.

Until recently, the biggest barrier to makerspace innovation was the complexity of design software like AutoCAD, which was needed to operate CNC machines or 3D printers. Now, I can get an AI to generate models for me with a text prompt.

As I play with the relative capabilities of large and small AI models, I recognize the power of brute force when it comes to transformer technology. At the same time, I see a narrowing of the gap between the smaller models and those that require huge server farms.

Of course, those huge server farms come with immense cost, and only large corporations and governments can shoulder that level of investment. With that, comes control. They may control the smartest models in the world, but where does the true power lie? It lies in the creative inputs they use to train the models.

I have argued before that the power of the PC revolution was in its democratizing force. It spread technology over a much wider group of people and every one of those people had a brain and experience that were unique. Kevin Kelly called this the Hive Mind. Diversity is a bedrock of innovation.

Even with today’s biggest models locked behind corporate walls, I can now accomplish things that were once impossible for me. I can run powerful models locally on my Apple silicon Mac and use the big models for a limited set of problems. This is why small, open models are the future, not massive server farms.

This is tremendously empowering for humans. AI has made me a more efficient teacher. It has made me a more efficient creator. And the only real limits are in my imagination. And this is only the beginning.

… of a three part series on our transition into an AI-driven maker world.

Part II: Making Opportunity With AI

Part III: A Maker World

“Every time there is a new technology, there is a paranoid faction of less than brilliant artists who feel that that’s the end of their career; who don’t understand that art never dies; that technology will go on and serve art.” – Stan Winston, 1993

I’ve always been fascinated by creative journeys. When people create something amazing I want to know how they got there. Technology always plays a role. Creativity comes from understanding how to augment our vision with technology, from ink to notation to paint to film to pixels.

Creative work is about being able to express your vision in a way that the rest of humanity can appreciate. Technology is how we get there as humans, whether it’s through the mechanisms of writing, music, painting, photographing, coding, and a multitude of other pursuits. These are just the ways that we tell our stories, not the vision itself.

We chase our dreams through technology. We are always pushing it to see where it can take us. AI is just the latest chapter of this journey.

No matter what I’m doing I’m always frustrated with the limits of the technology to express my creative vision. I’ve probably come closest to my ideals with my photography and my writing, but in both cases, I still struggle to tell stories that I cannot capture or express.

The limiting factor is me messing things up with the technology of words or the photographic process. In both cases, digital has closed the gap between my vision and reality. However, even there, I still run up into blind alleyways, where I just can’t seem to make it work.

AI presents a unique opportunity and challenge. It extends our creative capacities even further by taking mundane tasks off the table. I can use it to help me write. I’m using it right now to dictate this into my phone. I will use it to edit and polish this blog before I publish it.

The latest iteration of this struggle is using AI to code a prototype of the Knowledge Navigator. Ironically, my creative vision is for a technology that helps others clarify theirs. However, until AI, I lacked the tools to execute this vision.

I have never been a very good coder. I understand how it works, but I get lost and frustrated with the details of passing my thoughts into something the machine can understand. Computers are far more particular about details than I am.

I am trying to use AI to overcome those mundane detail-oriented tasks, which limit the execution of my vision. As a result, I have made more progress in a month than I likely would have in a year of reading coding manuals.

Using AI, I have learned more about coding in the last month doing it this way than I have in the last 30 years. Now I will admit that I already have a fair foundation in how code works, but it’s different to have a theoretical knowledge of something than to have a practical knowledge in using it to create something new.

This is not “cheating” or “taking the easy way out,” as some have argued. You are cheating if you’re not learning. You are cheating if you’re not growing. You are cheating if you’re not innovating. Learning, growing, and innovating all stem from the basic human impulse to create and explore. AI can supplant us, but not if we use it to learn and grow.

Almost all criticisms of how AI is disrupting society come from contexts where creativity isn’t the main goal, like standardized testing or corporate output. That’s a different conversation than what we’re having here. These are disruptions to systems, not humans.

Systems drive us with extrinsic motivators like money or grades. AI challenges extrinsic motivators, but that gives us a chance to realign our systems around intrinsic learning and creativity.

The intrinsic motivation of most students is usually clouded if not eliminated by the extrinsic motivators of grades. The purely extrinsic motivation of a quick buck creates fake art. There is no intrinsic creative motivator there. There is no creative growth there, only mindless replication.

The industrial mindset is based on mindless replication. Over the last two centuries, we’ve learned to mass produce all sorts of things. I argued in Discovering Digital Humanity that we have learned to serve machines. This is not a good role for humans.

Those of us particularly sensitive to creative thought struggle to exist in this kind of world. We are naturally non-conformist and resist becoming part of mindless machines. We recognize that only humans can truly create.

Humans are naturally curious, but extrinsic motivators dull that curiosity because it is considered a luxury not a necessity in a paradigm where we are taught to be cogs in machines.

In a world where AI is omnipresent, creativity rooted in curiosity is no longer optional, it’s essential. We must inject that creative drive into our learning and working. This is the antithesis of industrial thinking because if you are just part of the machine, creativity is a liability. You don’t want a cog spinning at its own speed

I am not an AI accelerationist. I don’t tend to get excited by any technology. I get excited about what technologies can do for me to help me grow and to make the world a more interesting place.

AI offers vast potential to augment our creativity. But like any technology, we must use it with that end in mind. If we do not, the machine will discard us. We must teach ourselves and our students to create with the machines.

What we’re doing with the technology is at least as important as what the technology does. The more powerful the technology, the more powerful the human element needs to be. AI is a powerful technology. Humans must be mindful in how they use it.

Mindfulness and creativity are deeply intertwingled. They infuse our lives with meaning and purpose. I can create photographs, writing, music, tools, and so much more to help other people through mindfulness. Without mindful creativity, we are little more than machines. With it, we can create with any technology to reach heights limited only by our own imaginations.

“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, 1960

“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

Technology should augment us, not handicap us. All too often, I see people, from students to fellow faculty, approach technology with fear. Generative AI tools are generating a lot of fear. We need to make the choice to use them to augment what we’re doing. It’s the human choice about how we use it that matters. We need to teach ourselves as well as our students the right way to steer this technology, not ignore it or ban it.

Faced with the challenge of reinventing my class, I chose the augmentation path for both my work and teaching my students how to augment their own work. It’s amazing how quickly things get better when you use AI to augment what you are doing.

In redesigning my course over the past few weeks, I didn’t just focus on teaching AI as augmentation, I used it to augment my ability to overcome constraints of time and inspiration.

I quite often get responses from colleagues who look at the challenges of AI and throw up their hands and say I don’t have time to deal with this. This has been a recurring topic of discussion on Bryan Alexander‘s Future Trends Forum as well as the Talking with machines podcast that I do with Bryan and Mark Corbett Wilson.

As I confronted getting ready for my summer courses, the idea of a complete reinvention seemed far beyond my means. Whenever you upset the applecart of a carefully designed course, you have to recognize all of the different parts of the system that are likely to become undone. As Douglas Adams said, “A common mistake that people make when trying to design something completely foolproof is to underestimate the ingenuity of complete fools.”

Classes are complicated systems. If you’re trying to get students to do something unfamiliar to them like I do, it’s really easy to see things go haywire. I am usually very conservative when it comes to tinkering with the class structure.

At the same time, I was acutely aware that I was not living up to my own ideals when it came to teaching my students the skills they needed to be successful. I put a light coating of AI onto the class over the last couple of years, but it was always sort of an add-on.

Also, students were abusing it in the way that people often accuse them of. Instead of writing papers they were letting ChatGPT do it for them. They failed because ChatGPT doesn’t really know how to write papers for my class. But failing them is not my goal.

Most of my students are technology illiterate. Sure, they use technology, but they don’t really understand what they’re doing.

Education does a lousy job of teaching students how to use technology to maximize their potential. Usually, when confronted with a new technology, the collective response is to do the equivalent of handing the car keys to a 16-year old and telling them, “You go figure it out for yourself.”

My class is already fairly technology-training intensive. Bear in mind that the subject that I teach is government, not computer science. I already teach them blogging, digital and visual communication, and the final project is a website on a public web service like Google Sites or WordPress.

AI, however, demands something more than that. Properly employed, it is a tool for augmenting yourself, not just augmenting your output. This has the potential for changing our relationship with our tools at a fundamental level.

On the first day of class, I asked them what part of school was most difficult for them. Almost universally, it was writing. As a writer myself, I know the pain of putting things into words so I could relate to that. I’ve used AI to ease that pain, so I decided that a key goal of my AI use in class would be to help them engage with writing more effectively.

But first, I had to solve my own problems, which, ironically, were like those of my students. I needed to rewrite/redesign parts of my course in a short amount of time.

I knew what I wanted to do, but I didn’t really know how to do it. I got some good input from a friend who is a course designer. She gave me some excellent ideas. However, her suggestions were too extensive for me to comfortably implement given the time constraints.

Instead, I grasped for the very tool I was trying to teach my students. I gave Notebook LM and my AI pedagogy library a set of queries based on existing assignments and the goals of the class. Starting with my own text and instructions, it modified assignments so that they folded AI into the process. Using this iterative process with AI simplified the process of modifying my class system without eliminating it entirely.

In short, I developed a sequence of “Blog Prep” assignments. In the first assignment, I have the students query an AI chatbot about how they thought the question should be asked. We then reviewed these live in class. It is very important that they understand what I’m asking them to do as my class is based on learning by doing and if they do it wrong, they miss a lot of the learning.

In the second step, the students found sources to develop their arguments to the question. They could use AI to help them with this or more traditional search tools.

Finally, I had them bring in a rough draft of their first blog post and post it in the discussion. I then ran those through either ChatGPT or Gemini using the same strategies I use for my own writing. Again, this was done in class and was a centerpiece of the discussion that day.

We asked the chatbot what the draft was about to verify that it was clear in what it was trying to say. Then, we asked it to compare with the assignment question and verify whether it had answered that.

Finally, we asked the chatbot for ways we could improve the post. The results generated a surprised reaction from the class. They had never thought of using an AI chatbot in this way before.

JR Licklider in the quote above talks about how he often rejected work because of the clerical infeasibility of it. I realized that that’s exactly what prevented me from changing my course. It is also what my students needed to help them change how they approached the work of learning. In both cases AI removed barriers.

For those of us trying to facilitate change, whether with our students or the process of teaching and learning, time barriers are a major roadblock. In the same way that I use AI to facilitate my writing processes, I was able to use it to facilitate rewriting my course, and as a mechanism to teach my students to do the same.

I know I can solve a lot of problems given enough time, but therein lies the rub: I never seem to have the time. If I can use AI to overcome barriers and save time, I can make a lot more problems solvable. The same logic applies to my colleagues and my students.

“Technological systems contain messy, complex, problem-solving components. They are both socially constructed and society shaping.”  – Thomas P. Hughes

“It’s the questions we can’t answer that teach us the most. They teach us how to think. If you give a man an answer, all he gains is a little fact. But give him a question and he’ll look for his own answers.” – Patrick Rothfuss

Every day there seems to be a new story about how AI is destroying education, work, or civilization itself. When I dig into them, they almost always describe a situation where AI is being misused or abused by human fallacies or systems.

It’s like damning hammers by highlighting every instance where someone uses one to beat someone to death instead of using them to build a home. The hammer is an apt metaphor because I often feel like I’m playing whack-a-mole when people bring these stories up to me to justify ignoring, banning, or fearing AI tools.

People are afraid and they have a right to be afraid. However, we must realize that those fears are of change and our own infirmities (we all have them).

This fear blinds many to the possibilities of any new technology, but the cascade of skeptical articles of AI is unprecedented in my experience of new technologies emerging on the scene. I am speculating, but I do think writers and journalists are particularly threatened by this technology because, for the first time, there is a tool that threatens the routine aspects of their jobs rather than someone else’s.

It is time to embark on a clear-headed exploration of how humans should approach this technology. AI can’t be banned. At this point, that would be like banning wind from an outdoor wedding.

Nor should we ignore AI. There is no disputing its likely impact and the potential for humans to abuse it. We also shouldn’t dismiss its potential to change the world for the good.

We need a human-centered framework for moving forward into a changed world. For this, I would suggest a set of mirrored principles to guide us forward.

It is a lack of transparency that fuels criticisms of AI that center on it generating a torrent of misinformation. I constantly must remind people that humans already do this. The problem is not the information, it is finding a way back to its source. AI built on transparency will always connect information (see next point) and help us navigate toward finding our own answers, not give them (see #5 below).

Technological isolation is not a new thing. AI may make it more appealing, but it is no different from those who use it to hide for whatever reason.

Critics of AI’s impact on labor often ignore the reality that the industrialized economy has dehumanized us for decades, if not centuries. This is not a new thing. Neither is automation replacing menial labor.

If we only teach humans to do mindless, repetitive tasks, they will be ripe for replacement by any technology. This is a societal choice and one that needs to be fixed in the social sphere.

AI can mimic human creative expression but cannot replace it. Creative people such as myself embrace AI because it gives us another tool with which we can express ourselves. Most of the criticism I see in this area comes from creatives who aren’t doing very creative work in the first place and those who think they own our creativity. AI can never threaten authentic creativity.

Learning requires questioning. AI’s power to shift perspectives gives us a powerful ladder to see through our conceptual blinders.

When you rely on anyone for answers instead of exploring for them yourself, you make yourself vulnerable to bias. I have seen many criticisms of AI dwell on this question of bias. This is only a problem if we accept its responses unquestioningly as answers.

No matter where we get information from, we must develop good skills to question information that is given to us even when dealing with pure human-engineered content.

AI is actually a powerful tool for examining bias at scale but only if we use it to question information rather than assuming what it is giving us is somehow magically unbiased.

As humans, we do the same thing but (hopefully) have the critical thinking skills to dismiss those associations that don’t make sense. AI lacks critical thinking skills. We should never forget that.

How we adapt and learn as humans and societies is far more likely to determine whether we thrive or wither under the pressure of technological change. These are the kinds of questions we should be asking ourselves, not tilting at the windmills of a technology that can’t be stuffed back in the bottle.

These five principles and their antitheses should guide us as we use these tools and, more importantly, adapt our human systems to their capabilities and challenges. At the end of the day, these are a product of human choices, not technological ones.

“The work of art is valuable only in so far as it is vibrated by the reflexes of the future.” – André Breton

As I’ve been exploring the AI world and using it for my own creative facilitation, I have occasionally written about its ability to help me get past the blank page phenomenon. Every creative writer, painter, and photographer struggles with this phenomenon. In my case, this can last for days or even weeks. I never know when my creative energy will emerge.

One of the frequent criticisms I hear about AI from creatives is that if you use AI to mitigate this pain, you will lessen the product of that inspiration. Has this happened to me? I don’t know. As someone who is usually insecure about his work, I’m never sure whether my output has gotten better, gotten worse, or become in some way not me.

I washed the last blog I published through ChatGPT much earlier than I otherwise would have because I had two related half-blogs, neither of which worked alone. After two or three cycles in the chatbot, I went back to editing with a much clearer vision of what I was trying to say.

At the end of that process, however, it was very difficult for me to figure out how much of that work was mine and how much of that was a product of ChatGPT. It conveyed the message I wanted to transmit.

Often when I see people (especially my students) use AI chatbots, they are using it to replace their lack of inspiration. This creates inauthentic outputs that are easily discernible from quality human work. Human inspiration is central to the human voice. AI can tune that voice, not replicate it.

I like to tell people that if they are seeking answers from AI, it will disappoint them. Instead, we should seek questions. Completing your work is to offer an answer. Suggesting courses of action should stimulate questions.

The Knowledge Navigator I am working on is at its root a platform to help surface questions. I see it as an intuition accelerator. In this way, it can inspire our writing and creating. It does so by generating the seeds of inspiration by pulling together disparate ideas and assembling them into maps that the writer or creative can then navigate to figure out what to say.

Creating has always been a product of synthesis aided and abetted by technology. We went from copying phrases of music to sampling them in the 1980s. However, you can tell which songs are the product of creative genius by what the synthesizer does with the phrases and ideas in their new creation.

Instead of Beethoven, listening to Mozart and then putting phrases of his into his own work, the artists of the 20th century could literally take the performed music itself and assemble it into whole new works of art.

This extended beyond music. Visual artists like Andy Warhol took photography and embellished it into novel art forms. Collage art is another example of synthesis in the visual sphere.

However, I’m not trying to appropriate anybody else’s work more than any other kind of creative writer or photographer. I am trying to use AI to make my own writing better and more accessible.

It’s always been a struggle for me to reduce the complexity of my thoughts to two-dimensional sentences. Even worse is trying to make those sentences convey the richness of my thought without losing the reader.

I am dictating this blog into my phone right now as I walk down the street, so in its initial draft, it’s a very unfiltered version of my thoughts. It takes a lot of hammering to get those thoughts into a format that I find understandable to others.

Is it cheating to let AI help me with that? The thoughts that are coming out of my head are genuine and a product of my creative brain at work. I use the technology of my phone to take dictation as I walk. Is that an inappropriate use of technology? How far does this go?

The more I use various AI tools, the more I realize just how much AI will change how we think and communicate our thinking. It will change how we research and assemble ideas. It will also change how we polish our ideas for others.

AI allows us to tone them for various audiences. For instance, it should allow us to translate scholarly writing into something understandable for a general audience. Applied creatively, these tools magnify our ability to exchange ideas.

But what is most important to this process, the ideas or the struggle to communicate them? Is suffering required to produce art?

Would you be able to tell the difference between something that’s never been touched by AI and something that has? Which one is better?

One thing, however, that we should hold onto as our own as a species if not individual is that genesis moment of creation. Whatever methods we use to create the spark that ignites the art, whether written or visual; it must be a human spark. That is where authenticity lies.

AI will redefine art, just like reproduction and broadcast did. It will broaden the reaches of art to most forms of human generated content.

Humans generate a lot of low-level content, such as graphics for advertising or filler writing for marketing brochures. Sometimes this reaches the level of art, but, most of the time, people paid to mass produce content generate it.

Like many creatives, I’ve done both artistic and commercial work as a photographer and as a writer. Most creators recognize the difference between the work that they do for themselves and the work that they did for a paycheck.

AI will take over a lot of that commercial space. It’s already happening.

This should prompt a deeper conversation about how we support creative work and value it in our society. Capitalism and art have always had an uneasy relationship. AI will throw that into even starker relief.

We need to build systems to incentivize humans to push themselves into new areas of thinking and creation. Right now, our educational systems dis-incentivize that kind of activity. So do most of our work environments.

The more we teach people that creativity is not worth the creative pain, the more vulnerable we make ourselves to being replaced by AI. Creativity requires a frame of mind motivated by intrinsic forces, not the promise of extrinsic rewards.

AI may simplify the process of creation, but the act of creation will always be artisanal. I’m able to do photography these days, because I have access to digital tools. I am a better writer because of word processing. AI tools are another evolution of my toolbox, not a replacement for me, any more than PhotoShop or a grammar checker are.

If we can use AI to build bridges and help us communicate our passions better, it will lead to greater innovation and understanding among all of us. That can’t be a bad thing.

“And today, the book is already, as the present mode of scholarly production demonstrates, an outdated mediation between two different filing systems. For everything that matters is to be found in the card box of the researcher who wrote it, and the scholar studying it assimilates it into his own card index.” — Walter Benjamin, ca. 1923-26

“Give a man a book, and you entertain him for a night. Teach a man to write, and you give him crippling self-doubt for life.” — Andy Weir, 2015

Scott McCloud, 2001

The most intimidating thing about storytelling is seeing a complex world in front of you and trying to distill it into a linear narrative. Grammar, spelling, and style are tools to make narratives more understandable—but the real challenge is the linear form itself.

Written narratives are a byproduct of a technological paradigm—paper—that forced ideas into constrained, two-dimensional forms. Though we’ve started to move beyond paper as our primary medium, we still shoehorn our thoughts into formats designed for it.

Anyone who has ever tried to write knows the world isn’t linear. Capturing the diversity of what we see, hear, and feel in a straight line of text is a hard task. Transforming our internal idea spaces into something comprehensible is even harder.

As someone who’s struggled with writing, I know how hard it is to make ideas understandable. We lose so much insight and perspective because our tools require time and skill most of us don’t have.

Digital storytelling attempts to solve this, but we still dig for meaning in incomplete records. Historiography and pretty much any kind of research requires both expertise and time to piece together coherent narratives out of fragmentary evidence.

While we now capture vastly more information than ever, our storytelling hasn’t caught up. We’re flooded with unfiltered content and disconnected data, yet we still rely on boundaries like “the document” to shape and constrain ideas.

That’s a habit we inherited from paper—and we need to move on. Visual narratives, like the one constructed by Scott McCloud above, have explored what digital visualization truly means. Even visual artists, like Nick Sousanis, constrained by the page, demonstrate the possibilities of visual languages.

Visualization plus digitization shows us that paper is no longer a necessary organizing metaphor. While we remain conceptually constrained, the technical reasons for avoiding this exploration have diminished. AI can illustrate connections for us and break us free from text and paper.

Our education systems still rely on carving off manageable chunks of knowledge—because we’ve given up on perceiving holistic abundance. We try to make it digestible through citations, cataloging and tagging systems. Those forms reflect a slower, flatter information space that is over matched by current information flows.

Scholarly formats—from five-paragraph essays to footnote rituals—exist because of paper’s physical limitations. They have logic and even beauty in their structure—but they’re also exclusionary. Poor writing frequently obscures good ideas. Extracting meaning requires specialized training, especially in the humanities.

Clear communication is a particular challenge in STEM fields. Most scientists lack writing training. Much gets lost in translation. Structured papers help readers extract meaning, but they still confine ideas to rigid formats. It’s no surprise that AI tools are seeing the fastest adoption in scientific domains—because those domains are desperate to move beyond their restrictive storytelling forms.

This points the way forward. Literature, statistics, code—all are more than the lines they appear on. Everything has context. Producing holistic narratives has been difficult; not anymore. The technology simply wasn’t there to break ideas free from paper systematically.

That’s the promise of large language models. They allow us to move in and out of dimensions and design narratives that aren’t stuck to a page. Scholars can now create rich, interactive diagrams of their work—instantly understandable but open to deeper exploration.

Information can now evolve beyond the technology of the book. This shift could usher in a new era of scholarship, creativity, and innovation. That’s what we’re trying to build with the Knowledge Navigator.

For a content creator—whether you’re a scholar, storyteller, educator, or entrepreneur—this shift means you don’t have to force your ideas into a rigid, linear format. Instead of starting with an essay or blog post, you might begin by building a concept map, a visual story, or an interactive explainer that connects your ideas multidimensionally (Robert Horn has been doing this for years). You might even co-create with an AI that helps you draw connections you hadn’t considered, offering insights or references in real time.

Imagine a researcher publishing a study not just as a dense PDF, but as a navigable, visual web of concepts—where a viewer can zoom in on data, click into contextual backstories, and explore connected fields without ever losing the thread. The traditional paper becomes just one mode of access—one “window” into a multidimensional landscape.

If you’re a teacher, this might require rethinking how tools reshape how students interact with knowledge. Rather than expecting them to summarize a chapter in a five-paragraph essay, you could ask them to create a dynamic timeline, an interactive comic, or a narrated walkthrough of connected events. With the help of tools like AI, students can build artifacts that reflect how they think—visually, spatially, relationally—not just how well they can conform to essay templates.

For businesses, it might mean ditching the standard pitch deck or white paper in favor of a living knowledge hub. AI could help tailor a narrative to a stakeholder’s needs, pulling from different content layers depending on interest—skimming visuals for high-level decision makers or diving into deep technical specs for engineers.

And for writers like me—those who’ve struggled with linear formats—it’s a shift from battling the blank page to collaborating with a canvas. I can explore fragments of ideas visually or conversationally and let AI help weave those fragments into coherent, nonlinear wholes. It turns writing from a rigid process into a fluid, evolving ecosystem of thought.

Too often, I’ve found that complex-sounding ideas are actually simple underneath. It’s the form that drives the complexity. Explaining paradigm shifts is especially messy—not because the ideas are hard, but because they ripple across contexts. Everyone lives in their own paradigm and in the collective one. Challenging that triggers cognitive friction.

If people don’t want to listen, even the best explanations fall flat. But visual explanations? They cut through. That’s why Knowledge Navigator is a visual tool: to finally unflatten the way we think