FUTURE OF LEARNING WITH AI
AI tools are now widely used in education. They are used by students to write essays, complete assignments in seconds, summarise chapters, and solve homework problems. As a result, schools have mainly focused on one issue: how to prevent students from using AI to cheat. But that might actually be the wrong question. The bigger issue is that students using AI to avoid thinking isn’t really a new problem. It’s just a more advanced version of something education systems have encouraged for years: focusing more on grades and finished work than on actual learning. AI didn’t create this problem. It just made the shortcut easier.
A lot of discussions about AI in schools treat students like they’re simply lazy or dishonest. But from a student’s perspective, using AI just makes their work easier. Think about it like this. A student remembers on Sunday night that an assignment is due the next morning. All the student would need to do is tell an AI model like ChatGPT to write it for them. There, problem solved. AI just happens to be the most powerful technology students have ever had. For the first time, someone who barely understands a topic can generate a convincing essay, not found anywhere on the internet, or answer within minutes. It doesn’t really count as copying, which makes it easier for the students to get away with it.
So, the problem isn’t just AI itself. It’s the way education has been designed for a long time.
The Difference Between Learning & Task Completion
One of the biggest mistakes people make is assuming that completing work is the same thing as learning. Clearly, it isn’t.
Real learning usually involves struggle, repetition, questioning yourself, experimenting, and slowly building understanding over time. In psychology, this connects to metacognition, being aware of your own thinking and understanding how you learn best.
The problem with most AI tools is that they remove a lot of this process.
Instead of encouraging students to struggle with a problem, test different ideas, and learn from mistakes, AI often provides a polished answer immediately. While this can save time, it can also reduce opportunities for the repetition, experimentation, and reflection that help build real understanding. As a result, the student may finish the assignment without actually understanding the ideas behind it. That becomes a problem later when they need to apply those concepts in exams or future assignments.
The Problem With Current AI Tools
An important thing to understand is that tools like OpenAI’s ChatGPT were not originally designed as educational chatbots. They were designed to generate useful and fluent responses quickly.
That is very different from helping someone learn.
Current AI tools usually give answers immediately, don’t check whether you’ve tried solving something yourself, don’t know whether you understand the response, and are optimised to feel helpful and satisfying. But in education, the most satisfying response is not always the most useful one. Sometimes the best learning happens when students are forced to struggle with a question a little longer, rethink their assumptions, or explain their reasoning before getting help. This way, they understand concepts better, apply them in multiple ways, and remember them longer.
Consider two students completing the same essay. One asks AI to write the entire thing and submits it after a quick review. The other uses AI to develop an argument, find sources, and test their ideas before writing the essay themselves. While the first student completes the task faster, the second is far more likely to understand and remember the material
The difference becomes clear when they are asked about the essay. The first student is likely to struggle to explain their points, while the second can discuss them confidently because they actually worked through the ideas themselves.
What Learning-Focused AI Could Look Like
Instead of replacing thinking, AI could actually be designed to strengthen it.
A learning-focused AI system would behave very differently from the tools most students use today.
For example, instead of instantly writing an essay, it could first ask:
- What have you already tried?
- What part are you struggling with?
- What’s your current argument?
- What evidence supports your point?
- What argument could someone make against it?
The goal wouldn’t be to complete the task for the student. The goal would be to guide the student’s thinking. The goal would be to help the student complete their own work.
AI could also encourage curiosity before providing content, help students reflect on how they learn, use memory across sessions to track growth, and focus more on skill development than finished outputs.
In that kind of system, AI becomes more like a mentor than a replacement brain.
This connects back to the Unified Meta-Learning Theory. If repetition, experimentation, imagination, and imitation are the core mechanisms behind learning, then AI should support those processes in students rather than perform them on their behalf.
Of course, learning-focused AI would have its own challenges. Privacy is one concern, since these systems would need to remember information about student progress over time. Access is another issue, because not every student has reliable devices or internet connections.
Even teachers could struggle with the extra work required to integrate these systems effectively. And like all AI systems, they can still make mistakes or generate misleading information. These challenges don’t make the idea impossible, but they do show that better AI design alone won’t solve every problem.
How Education Institutions can see beyond
Even better AI tools won’t completely solve the issue if schools continue to reward results over learning.
Students will continue using AI if schools only reward the final product. When grades depend mostly on the finished submission rather than the learning process, many students will naturally choose the fastest way to produce high-quality work. AI simply makes that option easier than ever before. Education systems may need to move more toward oral discussions, project-based learning, process portfolios, and assessments that show how students think, not just what they submit.
Otherwise, AI misuse will continue no matter how advanced detection software becomes.
This idea is the basis of Vadagam’s Unified Meta-Learning Theory, which describes learning as a process involving repetition, imitation, imagination, and experimentation. The theory argues that learning isn’t just about receiving information. It’s about training your thinking and decision-making abilities through active engagement. which argues that people learn best when genuine curiosity is active. According to the theory, when learning feels forced, disconnected from real life, and focused solely on grades, motivation drops and students start looking for shortcuts rather than understanding.
CDLT also argues that education should focus less on producing students who can simply follow instructions and more on developing people who can create things, solve problems, and make opportunities for themselves. That becomes even more important in a world where AI can already perform many routine tasks faster than humans can.
If schools want AI to support learning rather than replace it, they need to rethink both assessment and technology. Students should be rewarded for showing their thinking process, not just submitting polished final products. At the same time, AI tools should be designed to guide thinking rather than provide complete answers immediately.
The debate around AI in education is often treated like a technology problem. But at its core, it’s really a learning problem.
If education is supposed to develop people who can create things, solve problems, and adapt, then AI should help develop those abilities rather than replace them.
The future probably won’t belong to the students who simply have the strongest AI tools. It will belong to the students who understand how to use AI without letting it replace their own thinking.
That’s the difference between using AI as a shortcut and using it as a tool for growth.
Increasing Skill GAP due to AI
Research is now confirming what intuition already suspected: AI is not closing the skills gap. It is accelerating it.
The popular assumption has been that a first-year medical student armed with a diagnostic AI could produce the same clinical assessment as a physician with twenty years of experience. That the gap between novice and expert was collapsing overnight. A closer look reveals something far more precise. On routine, well-defined tasks, AI does bring the novice closer to the baseline. But the moment complexity enters the room, ambiguous symptoms, rare presentations, cases where the textbook offers three equally valid interpretations, the beginner with the AI doesn’t just plateau. They confidently go in the wrong direction. Meanwhile, the experienced clinician uses the exact same tool to surface patterns across thousands of cases in seconds, stress-test their hypothesis against outliers, and arrive at a diagnosis they could not have reached alone.
Same tool. Opposite outcomes.
Think of it like a surgical instrument. In the hands of someone who has never held a scalpel, a laser surgical system does not make them a surgeon. It makes them precisely and efficiently wrong. In the hands of a trained surgeon, that same system extends their precision beyond what the human hand alone could ever achieve.
For predictable, well-structured work, AI compresses the distance between amateur and professional. But for genuinely complex, high-stakes problems, expertise is not just helpful, it is the unlock condition. Without it, the machine cannot perform at its ceiling. It only reflects the ceiling of the person using it.
This is the part the “AI will replace everyone” narrative consistently misses. AI does not replace the expert. It exposes the difference between the person who understands the first principles and the person who is simply prompting. And as tasks grow harder, the gap between those two people does not shrink. It widens, faster than ever before.
Contributor: Parthavi Nistala
Student & Learning Enthusiast