Change Everything!!! And Then What???
A Pragmatic Vision of AI-Responsive Instructional and Curricular Reform
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Nick
The conversation around AI in education is reaching a fever pitch. Everywhere one turns, there's a call to action, a sense of urgency, a feeling that everything needs to be radically different now that AI is in the classroom. "Change everything!" is the rallying cry, echoed by both those excited by AI's potential and those deeply concerned about its impact. It's a compelling narrative, this idea of a clean break with the past. But as educators, tasked with the very practical responsibility of helping students learn, we might benefit from taking a slightly more grounded, perhaps even reassuring, perspective. Is "change everything" truly the most effective, or even the most necessary, response? Or is there a more stable, reliable foundation upon which educators can build?
Let's acknowledge the valid reasons for this sense of urgency. AI does present real shifts in the educational landscape. In a recent presentation on AI in Practice, I explored these very shifts. We face important questions for researchers, for businesses, for universities, and for high school teachers. Concerns about "cognitive bleed" – that subtle erosion of independent thinking, the rapidly increasing student usage of AI, the emerging data on AI's impact on critical thinking – these are not hypotheticals. The promise of AI-driven efficiency is tempting, but we also recognize the potential for over-reliance and skill atrophy. This creates a natural inclination to think in terms of radical change. When confronted with something so transformative, a sweeping response feels almost intuitively correct.
However, a truly pragmatic approach calls for a more measured response. The "change everything" approach, while understandable, might actually be premature, even counterproductive. Instead of discarding what is known to work, perhaps the most effective strategy is to re-emphasize what has always worked. I'm talking about the enduring principles of learning. Instead of seeing AI as a wrecking ball to pedagogical foundations, let's see it as an opportunity to rediscover and redeploy the very theories that have guided effective teaching for decades. Consider: Generative Learning Theory, Cognitive Load Theory, the SOI framework, and those practical generative strategies – these aren't tied to any specific technology. They are grounded in the fundamental processes of human learning. They describe how students build knowledge, develop understanding, and become critical thinkers. And these fundamental processes... these are not disrupted by AI. In fact, they become even more important.
The call to "change everything" resonates deeply in educational circles, capturing both excitement and anxiety. It speaks to a profound sense that AI represents not merely a new tool, but a fundamental shift in the educational landscape. What makes this call so powerful isn't just the allure of technological progress—it's the implicit recognition that AI touches something core to the educational enterprise itself: the relationship between knowledge, learning, and human development. The rallying cry suggests that previous educational paradigms have been rendered obsolete, that continuing with business-as-usual would be not only ineffective but possibly harmful to students' futures.
This is where Cognitive Load Theory (CLT) becomes particularly illuminating. CLT reminds us that deep, meaningful learning is not about skipping "low-level stuff" to rush to higher-order thinking. Instead, it underscores that effective learning requires careful management of cognitive load at all levels. There's intrinsic load – the inherent complexity of the subject matter itself – which we cannot and should not bypass. There's germane load – the effortful cognitive processing that leads to understanding and schema construction – which is the ultimate goal. And then there's extraneous load – anything that hinders learning and doesn't contribute to understanding.
The risk with a purely "change everything" approach, ironically, is that it might inadvertently increase extraneous load and undermine the very conditions necessary for germane learning. Imagine a classroom completely upended by AI, with students overwhelmed by new tools, unfamiliar workflows, and a sense of pedagogical disorientation. This chaotic environment could actually increase extraneous cognitive load, making it harder for students to grapple with the intrinsic complexity of the subject matter and hindering the development of deep understanding. CLT doesn't suggest educators should avoid AI, but rather that they need to be especially mindful of how to integrate it. It challenges us to ask: how can we use AI strategically to optimize cognitive load across all three types? How can AI potentially scaffold the processing of intrinsic load, making complex concepts more accessible without oversimplifying them? How can learning experiences be designed that maximize germane load – that effortful, meaning-making cognitive work that leads to lasting learning? And crucially, how can extraneous load be minimized in this new AI-infused learning environment, ensuring students are not cognitively overwhelmed by the very tools intended to help them?
The SOI framework and generative strategies provide concrete pathways for this nuanced approach. They are not outdated methods; they are proven techniques for fostering active, generative learning, perfectly adaptable to an AI-enhanced environment. They guide educators in designing learning experiences where students are actively selecting, organizing, and integrating information – exactly the skills needed to thrive in an AI-rich world. "Possibility Literacy" and the practical classroom strategies being explored are practical examples of how these enduring theories can be applied in today's classrooms. We are not reinventing the wheel; we are re-tuning the engine, ensuring it runs smoothly and powerfully in this new context.
Of course, some adaptations are necessary. Curricula will evolve. AI literacy, framed as Possibility Literacy, becomes even more critical. AI source interrogation is now an essential skill. Ethical considerations demand attention. But these are adjustments, thoughtful adaptations built upon a solid foundation, not a wholesale revolution. Our core mission remains unchanged: to guide students towards deep and meaningful learning. And for this mission, the well-established principles of learning theory are not just helpful; they are the most reliable and steady guide.
So, when one hears the call to "change everything" because of AI, take a breath. Resist the impulse to abandon what is known to work. Instead, let's find reassurance and direction in the enduring principles of learning. Let's use generative learning theory, cognitive load theory, and the SOI framework not as relics of the past, but as our steadfast guides as we navigate this evolving educational landscape. The "change everything" clamor might sound exciting, but true progress lies in thoughtful evolution, in building upon proven foundations, and in re-centering ourselves on the timeless goal of education: to ensure students learn deeply, meaningfully, and confidently in this new AI era. What will be the guiding principle as educators respond to AI? For many, it will always be learning, a principle that endures.
Nick Potkalitsky, Ph.D.
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I want to agree with you but I can't. I am not a knee-jerk person; I've been thinking about the problem of education for a very long time. I despise the polarisation of this debate and so many others. But, my conclusion is other than yours. Allow me to explain:
Firstly, I am not promoting a full systems change because of AI, but I do hope that AI is the straw that breaks the camels back. Institutions are rigid structures, designed to resist change. Most schools do not have built-in mechanisms that allow for evolution; to the contrary, most accumulate inertia, inefficiency, and institutional plaque. Meanwhile, life is defined by change, and it is changing very quickly right now. The result is that we have a tool fit for the purposes of the past but not for the present and certainly not for the future. I would argue that the first principle of education is currently to produce employable workers. It may not have started that way but that's what its become. If your first principles are broken then it's time for a complete rebuild.
Secondly, education is causing real suffering both for students and teachers. This is evidenced by disengagement, absenteeism, late/non submissions, grade inflation, etc. These are the 'freeze' of the 4Fs of anxiety. As fight or flight are not an option, all that is left for students is freeze or fawn. Fawn is a feature of students up until about high school, then they switch to freeze. If school is producing an autonomic nervous reaction in students then it's no longer working. I would ask, which part of the system do you think is worth salvaging? The authoritarian structure of professors and teachers as knowledge bearers in an age of intelligent machines, doling out hall passes and bathroom breaks to intelligent people? Standardised curricula/textbooks that are wrong, biased, or at least outdated as much as they are right? Standardised testing and assessment that proves little more than you are good at tests, while reducing the complexity of a human to single digits? Classroom sizes of up to 30+ in k-12, and more in universities, where teachers have little to no agency or relationship with students? I could go on. The point is, when the majority of problems are systemic, resulting not only in ineffective learning but also in the stigmatisation of learning, then it's time for a change. The world in many ways is at a point of crisis and we need the next generation of youth to act address and survive the problems we've helped to create. Now is not the time for moving slowly.
Does that mean we should be reckless and simply collapse everything all at once? Absolutely not. We need to reconstruct with precision and care. We need to build the new boat while sailing in the existing one. But we need urgent change because the cost is being paid in the lives of our youth. The youth of the present are not the same humans as those of the past; they're humanity's first digital natives. They don't sync with the analogue systems we've forced them into. They deserve an instrument of education that meets them where they're at and empowers their success.
The way forward must be radical but sensible, wise and extremely careful. I've proposed sandboxes of innovation to be placed inside of each school as a starting place - classrooms where the rules don't apply but that allow for experimentation, iteration, and feedback. We should fast-track laboratories for education - fast-moving research facilities, populated with students, that rapidly prototype new solutions for education. We have a wealth of new knowledge about the science of learning that has no landing pad inside of educational institutions. What we now know changes the entire methodology if learning. These are just a few ideas from my ruminations.
Institutional transformation is required, not only because of AI but also because of AI.
Cogent analysis and provocative ideas. Actually, I’ve already got huge amounts of knowledge that I never learned or forgot: how to make shoes, stoke a furnace, or grow food. New skills and cognitive loads are always replacing old ones and there is always nostalgia for them ( maybe not for using an outhouse without freezing). Writing? It may go the way of buggy whips and postcards. New very necessary skills; managing multiple Ais, connecting the dots on information from virtual simulations, communicating effectively across time zones in virtual reality. You get the picture. It’s normal human technological advancement sped up
and bigger. Strap
In.