AI in Education: Moving Beyond the Hype to What Matters

There is no shortage of opinions about artificial intelligence in education. In many cases, those opinions are formed at a distance, based on headlines, product demos, or surface-level interactions with tools like ChatGPT. What’s often missing is a grounded understanding of how these systems actually function, and more importantly, how they should be applied.

In my role as an AI engineer who develops the software in question, I can confidently say that there is quite a wide gap between the opinions of people and the actuality of things. Opinions usually go to extremes – either AI would replace teachers altogether or would have little effect on the teaching process.

The Wrong Question: “Do We Still Need Teachers?”

One of the most common and concerning questions I hear is whether AI will eliminate the need for teachers or traditional learning materials. It reflects a fundamental misunderstanding of both AI and learning itself.

AI can process, generate, and organize information at scale. What it cannot do is replicate human experience, context, or judgment. Learning is not simply about acquiring information; it is about applying that information in real-world scenarios, developing intuition, and building the ability to think critically in ambiguous situations.

That human layer is not optional. It is foundational.

Rather than replacing educators, AI has the potential to extend their reach, freeing them from repetitive tasks and enabling more focus on higher-value interactions with learners. The distinction is important. This is not about substitution; it is about augmentation.

AI as an Accelerator, Not an Authority

A more productive way to think about AI is as an accelerator of processes that already exist. In education, this means enabling faster content creation, more adaptive learning pathways, and greater personalization at scale.

For example, AI can help transform static content into dynamic, multi-modal learning experiences—combining text, video, simulations, and interactive elements tailored to how an individual student learns best. What previously required significant time and resources can now be developed more efficiently.

However, acceleration introduces its own risks. When speed becomes the primary goal, understanding often suffers. This is one of the most significant issues I see today—not that AI is overhyped, but that it is frequently misused.

The Overuse Problem: Skipping the Process

If there is one pattern that stands out across education and beyond, it is the tendency to use AI to jump directly to outcomes.

Students, professionals, and even organizations are increasingly using AI to generate final answers without engaging with the process that leads to those answers. This is not a limitation of the technology. It is a behavioral issue.

The value of learning does not sit in the answer itself. It sits in the process of arriving there—the exploration, the iteration, and the understanding of underlying concepts. When that process is bypassed, the long-term benefit is lost.

This is not a new challenge, but AI amplifies it. The ease of generating outputs creates a strong pull toward efficiency at the expense of depth. Rebalancing that dynamic is critical if AI is to be used effectively in learning environments.

Navigating Complexity Without Needing All the Answers

One of the challenges with AI is the pace at which it is evolving. Even within the engineering community, it is difficult to stay fully up to date with every new model, framework, or capability.

For those outside of that space, the expectation to understand everything can become a barrier to engagement. That expectation is unnecessary.

It is entirely reasonable, and increasingly common, to work with AI without fully understanding every underlying mechanism. The more practical approach is to start with what is accessible, build familiarity through usage, and expand from there.

Whether that begins with simple interactions or progresses into building applications using AI-assisted tools, the key is continued engagement. Comfort with uncertainty is part of the process.

A More Measured Path Forward

The trajectory of AI in education will not be defined by the technology alone. It will be shaped by how people choose to use it. Moving forward requires a more measured approach. That means resisting the urge to
over-automate, maintaining focus on the learning process, and recognizing the continued importance of human judgment.

AI will continue to evolve. The question is not whether it will change education—it already is. The more important question is whether we can integrate it in a way that strengthens learning rather than shortcuts it. Be sure to check out the full episode of EdTech on the Street in the City That Never Sleeps to learn more.