The Curriculum Illusion: How AI Exposes Long-Standing Educational Flaws

Artificial intelligence is often blamed for disrupting education, but it has created few new problems. Instead, it exposes existing flaws, bringing them into stark relief. Among these is the arbitrary nature of curriculum design, an issue that has long been hidden behind tradition and consensus. The sequences and structures of formal education are not based on objective logic or evidence but on habit and convenience. AI did not cause this; it is simply making these issues more visible.

Curriculum theory has never provided a robust framework for sequencing knowledge. Beyond the essentials of literacy and numeracy, where developmental progression is more or less clear, the rationale for curricular order becomes murky. Why are algebra and geometry taught in a particular order? Why more algebra than statistics is taught? Why are some historical periods prioritized over others? The answers lie in tradition and precedent rather than in any coherent theoretical justification. The assumptions about foundational skills, so central to curriculum logic, do not extend well beyond the basics. For advanced skills like critical, creative, or discerning thinking, the idea of prerequisites becomes less justified. Mid-range procedural skills like writing mechanics or computational fluency are frequently used as gatekeepers, though their role in fostering higher-order thinking is often overstated or misunderstood. 

For example, in middle school students are often subjected to a torrent of tasks that serve little developmental purpose. Much of what students do in these years amounts to busywork, designed more to keep them occupied and compliant than to foster meaningful learning. The situation is no better in higher education. College and graduate programs are often constructed around professional or disciplinary standards that themselves are arbitrary, built on consensus rather than evidence. These norms dictate course sequences and learning objectives but rarely align with the actual developmental or professional needs of students. The result is a system full of redundancies and inefficiencies, where tasks and assignments exist more to justify the structure than to serve the learner.

Education as a profession bears much of the responsibility for this state of affairs. Despite its long history, it lacks a disciplined, founded approach to curriculum design. Instead, education relies on an uneasy mix of tradition, politics, and institutional priorities. Curriculum committees and accrediting bodies often default to consensus-driven decisions, perpetuating outdated practices rather than challenging them. The absence of a rigorous theoretical framework for curriculum design leaves the field vulnerable to inertia and inefficiency.

AI did not create this problem, but it is illuminating it in uncomfortable ways. The displacement of certain procedural mid-range skills shows how poorly structured many learning sequences are and how little coherence exists between tasks and their intended outcomes. Yet, while AI can diagnose these flaws, it cannot solve them. The recommendations it offers depend on the data and assumptions it is given. Without a strong theoretical foundation, AI risks exposing the problem without solving it.

What AI provides is an opportunity, not a solution. It forces educators and policymakers to confront the arbitrary nature of curriculum design and to rethink the assumptions that underpin it. Massive curricular revision is urgently needed, not only to eliminate inefficiencies but also to realign education with meaningful developmental goals. This will require abandoning tasks that lack purpose, shifting focus from intermediary to higher-order skills, designing learning experiences to reflect the shift. It will also mean questioning the professional and disciplinary standards that dominate higher education and asking whether they truly serve learners or simply perpetuate tradition.

AI is revealing what has long been true: education has been operating on shaky foundations. The challenge now is to use this visibility to build something better, to replace the old traditions and arbitrary standards with a system that is logical, evidence-based, and focused on learning. The flaws were always there. AI is just making them harder to ignore.

Is the college essay dead?

The college essay, once a revered academic exercise, is now facing an existential crisis. It used to be a good tool—a structured way for students to demonstrate their understanding, showcase their critical thinking, and express ideas with clarity . The college essay was not merely about content; it was a skill-building process, teaching students to organize thoughts, develop arguments, and refine language. Yet today, AI  has made the traditional essay feel outdated, as it can generate polished, formulaic essays effortlessly. Policing AI use in these assignments is nearly impossible, and the conventional essay’s value is rapidly diminishing.

Not all essays are created equal, however, and the future of the college essay might depend on the type of skills we emphasize. The expository essay, designed to see if students understand material or can apply concepts, is on its last legs. When AI can churn out a satisfactory response in seconds, it is a clear sign that this form of assessment is no longer viable. The AI does not just pass these assignments; it excels at them, raising an uncomfortable question—if a machine can do it, why are we still teaching it? For these kinds of essays, the challenge is that they often assess recall rather than thinking. They were already on shaky ground; AI is just the final push. 

The essays that may survive, though, are those that demand novelty, creativity, and genuine problem-solving. AI may help in drafting, structuring, or even generating ideas, but it does not replace the kind of original thinking needed to solve real-world problems. It cannot fully simulate human intuition, lived experience, or deep critical evaluation. AI's writing is wooden, and often devoid of true beauty. Essays that require students to synthesize information in new ways, explore original ideas, exhibit artistic talent, or reflect deeply on personal experiences still have value. These essays are not about whether you know a theory; they are about what you can do with it. This is where the human element—the messy, unpredictable spark of creativity—remains irreplaceable. 

The deeper issue is not AI itself but the way we have been teaching and valuing writing. For decades, the emphasis has been on producing “correct” essays—structured, grammatically precise, and obedient to the format. We have been training students to write well enough to meet requirements, not to push the boundaries of their creativity. It is like teaching students to be proficient typists when what we really need are novelists or inventors. We have confused competency with originality, thinking that writing formulaic content is a necessary step before producing meaningful work. This is a misunderstanding of how creativity works; mastery does not come from repetition of the mundane but from risk-taking and exploration, even if that means stumbling along the way.

The real future of the essay should start with this recognition. Imagine if instead of book reports or basic expository pieces, students were challenged to write for real audiences—to draft scientific papers for journals, craft poems for literary contests, or propose solutions to pressing social issues. Sure, many students would not reach the publication stage, but the act of aiming higher would teach them infinitely more about the writing process, and more importantly, about thinking itself. This would not just be about mastering the mechanics of writing but developing a mindset of curiosity and originality. AI could still play a role in these processes, helping with the technicalities, leaving the student free to focus on developing and articulating novel ideas.   

The problem with the book report or the “explain Theory A” essay is not just that they are boring; it is that they are irrelevant. Nobody in the professional world is paid to summarize books or explain theories in isolation. These are stepping stones that lead nowhere. Excelling at pointless, terrible genre does not prepare to succeed ad an authentic genre. Instead of teaching students to write these antiquated forms, we should ask them to write pieces that demand something more—something they cannot copy-paste or generate easily with a prompt. Authentic, context-rich, and creative assignments are the ones that will endure. If there is no expectation of novelty or problem-solving, the essay format becomes an exercise in futility. 

AI’s rise does not have to spell the end of the essay. It might, in fact, be the nudge needed to reinvent it. We have the chance to move beyond teaching “correct” writing toward cultivating insightful, original work that challenges the boundaries of what students can do. AI’s presence forces us to ask hard questions about what we want students to learn. If writing is no longer about mechanics or regurgitating content but about generating ideas and engaging critically, then AI becomes a collaborator, not a competitor. It can help with the structure, but the essence—the thinking—must come from the student.

In the end, the college essay is not dead; it is just in need of reinvention. The conventional model of essays as rote demonstrations of knowledge is no longer viable. But the essay that challenges students to think, create, and solve problems—those essays will survive. They might even thrive, as the focus shifts from the mechanics of writing to the art of thinking. The key is to evolve our teaching methods and expectations, making room for a new kind of writing that leverages AI without losing the human touch. Raising expectations is the main strategy in dealing with AI in education. 

Advanced AI users develop special cognitive models

When we encounter a stranger, we make swift, often unconscious judgments about who they are and what they are capable of. A person who speaks our language with barely a hint of an accent? We assume they are fluent. Someone who drops a reference to a complex scientific theory? We peg them as well-educated, likely to be literate, and probably knowledgeable about a range of topics from current events to social norms.

These snap judgments form the backbone of our social interactions. They are mental shortcuts, honed over millennia of human evolution, allowing us to navigate the complexities of social life with remarkable efficiency. Most of the time, they serve us well. We can usually guess whether someone will understand a joke, follow a complex argument, or need help using a smartphone. These are cognitive models. 

But when we step into the realm of artificial intelligence, these time-tested models crumble. Our human-centric predictions fail spectacularly, leaving us confused and often frustrated. Consider a recent incident with ChatGPT, a sophisticated language model. When asked to count the number of 'r's in the word "strawberry," it faltered. Many observers scoffed, concluding that AI must be fundamentally stupid if it couldn't handle such a simple task.

Yet this reaction reveals more about our flawed expectations than any shortcoming of AI. Those familiar with AI's inner workings were not surprised. They understand that a language model, no matter how advanced, is not optimized for character-level analysis. It is like expecting a master chef to be an expert accountant simply because both professions involve numbers.

This misalignment between our expectations and AI's actual capabilities stems from our tendency to anthropomorphize. We instinctively attribute human-like qualities to these digital entities. We expect them to have consistent opinions, to learn from our interactions, to understand context and nuance as we do. But AI, in its current form, does none of these things.

Unlike humans, AI does not carry the baggage of personal experience or emotion. It does not have good days or bad days. It will not be flattered by praise or offended by insults. It can switch from discussing quantum physics to writing poetry without missing a beat, unencumbered by the specialization that defines human expertise.

But AI's differences extend beyond mere capability. It lacks the fundamental attributes we associate with consciousness. It has no self-awareness, no goals or motivations of its own. It does not truly understand the content it generates, despite how convincing it may seem. It is a reflection of the data it was trained on, not a sentient being forming its own thoughts and opinions.

To interact effectively with AI, we need to develop new mental models. We must learn to predict its behavior not based on human analogies, but on an understanding of its unique nature. This means recognizing that AI might struggle with tasks we find trivially easy, while effortlessly accomplishing feats that would challenge even the most brilliant human minds.

It means understanding that every interaction with AI is essentially new. Unlike humans, who build on past conversations and experiences, most current AI systems do not retain information from one chat to the next. They do not learn or evolve through our interactions. Each query is processed afresh, without the context of what came before.

This new model of understanding also requires us to be more precise in our interactions with AI. While humans often fill in gaps in conversation with assumed context, AI interprets our requests literally. It does not automatically infer our unstated needs or desires. The clarity of our input directly influences the quality of the AI's output.

As AI becomes an increasingly integral part of our lives, developing these new mental models is not just about avoiding frustration. It is about unlocking the full potential of these powerful tools. By understanding AI's strengths and limitations, we can craft our interactions to leverage its capabilities more effectively.

The future of human-AI interaction lies not in expecting AI to conform to human patterns, but in adapting our approach to align with AI's unique characteristics. It is a future that requires us to be more thoughtful, more precise, and more open to rethinking our instinctive assumptions. In doing so, we may not only improve our interactions with AI but also gain new insights into the nature of intelligence itself. 

Cognitive Offloading: Learning more by doing less

In the AI-rich environment, educators and learners alike are grappling with a seeming paradox: how can we enhance cognitive growth by doing less? The answer lies in the concept of cognitive offloading, a phenomenon that is gaining increasing attention in cognitive science and educational circles.

Cognitive offloading, as defined by Risko and Gilbert (2016) in their seminal paper "Cognitive Offloading," is "the use of physical action to alter the information processing requirements of a task so as to reduce cognitive demand." In other words, it is about leveraging external tools and resources to ease the mental burden of cognitive tasks.

Some educators mistakenly believe that any cognitive effort is beneficial for growth and development. However, this perspective overlooks the crucial role of cognitive offloading in effective learning. As Risko and Gilbert point out, "Offloading cognition helps us to overcome such capacity limitations, minimize computational effort, and achieve cognitive feats that would not otherwise be possible."

The ability to effectively offload cognitive tasks has always been important for human cognition. Throughout history, we've developed tools and strategies to extend our mental capabilities, from simple note-taking to complex computational devices. However, the advent of AI has made this skill more crucial than ever before.

With AI, we are not just offloading simple calculations or memory tasks; we are potentially shifting complex analytical and creative processes to these powerful tools. This new landscape requires a sophisticated understanding of AI capabilities and limitations. More importantly, it demands the ability to strategically split tasks into elements that can be offloaded to AI and those that require human cognition.

This skill - the ability to effectively partition cognitive tasks between human and AI - is becoming a key challenge for contemporary pedagogy. It is not just about using AI as a tool, but about understanding how to integrate AI into our cognitive processes in a way that enhances rather than replaces human thinking.

As Risko and Gilbert note, "the propensity to offload cognition is influenced by the internal cognitive demands that would otherwise be necessary." In the context of AI, this means learners need to develop a nuanced understanding of when AI can reduce cognitive load in beneficial ways, and when human cognition is irreplaceable.

For educators, this presents both a challenge and an opportunity. The challenge lies in teaching students not just how to use AI tools, but how to think about using them. This involves developing metacognitive skills that allow students to analyze tasks, assess AI capabilities, and make strategic decisions about cognitive offloading.

The opportunity, however, is immense. By embracing cognitive offloading and teaching students how to effectively leverage AI, we can potentially unlock new levels of human cognitive performance. We are not just making learning easier; we are expanding the boundaries of what is learnable.

It is crucial to recognize the value of cognitive offloading and develop sophisticated strategies for its use. The paradox of doing less to learn more is not just a quirk of our technological age; it is a key to unlocking human potential in a world of ever-increasing complexity. The true measure of intelligence in the AI era may well be the ability to know when to think for ourselves, and when to let AI do the thinking for us.