Pros and cons of AI in education: A unique take

On the surface, the pros are obvious. Almost boring, actually. AI can personalise learning. It can help students practice at their own pace. It can translate, transcribe, summarise, scaffold, tutor, and explain things five different ways without getting tired or irritated. It can reduce teacher workload by handling routine grading and administrative tasks. It can give feedback faster than any human system ever could.

None of that is controversial. We already know all of this, and we all see the upside. Anyone who has worked in a classroom knows that more time, more feedback, and more individual attention are good things. And anyone who has worked in tech knows that ignoring a powerful tool because it feels uncomfortable is rarely a winning strategy.

But the real conversation does not start there. It starts with what AI quietly changes beneath the surface.

The first thing AI forces us to confront is what we actually mean by learning. For generations, schools (particularly state schools) have been built around scarcity. Scarcity of information, scarcity of expert feedback, scarcity of time. We teach students to memorise because information can be hard to access in under-resourced classrooms. They practice alone because teachers have thirty other students to take care of. They’re tested on recall because that’s what’s measurable at scale.

AI removes a lot of that scarcity. Information is instant. Feedback is constant. Support is always available. That sounds like progress, and in many ways it is.

But it also means that some of the structures we rely on to signal learning no longer make the same sense.

If a student can generate a competent essay in seconds, the problem is not that the student cheated. The problem is that the assignment assumed that producing text was the same thing as understanding. AI did not break that assumption. It exposed it.

Assessment is the real pressure point. Not instruction. Not tools. Assessment. Essays, homework, take-home projects, even some exams were already imperfect proxies for learning. AI turns those imperfections into cracks you can no longer ignore.

So, one of the biggest cons of AI in education is not misuse. It is denial. If schools keep pretending that old assessments still measure what they used to measure, trust erodes. Grades lose meaning. Credentials weaken. And everyone in the system knows it, even if no one wants to say it out loud.

Another pro that gets a lot of attention is personalisation. And yes, adaptive learning is powerful. Students do better when material meets them where they are. But there is a quieter downside here that does not get enough airtime.

When every student is on a slightly different path, working at a slightly different pace, with a slightly different AI tutor, you risk losing shared intellectual experience. Struggle becomes private. Confusion becomes invisible. Learning becomes something you do with a machine instead of something you negotiate with other people.

This isn’t just a theoretical concern. UNESCO warns that hyper-personalised AI learning, without (re-)socialising design, can erode social and collaborative skills that are central to education’s social fabric.

Education is not just about mastery. It is about learning how to think alongside others, how to disagree, how to explain yourself, how to sit with uncertainty in a group. AI does not eliminate those skills, but it does not naturally cultivate them either. That still takes human design and intention.

Then there is the question of dependence, which I think is often framed poorly. People worry students will rely on AI too much. That is true, but also incomplete. Students already rely on calculators, spell checkers, search engines, and navigation apps. The real issue is not reliance. It is literacy.

If students are taught that AI outputs are answers, we have a problem. If they are taught that AI outputs are starting points, suggestions, hypotheses, or tools to be evaluated, that is a different story entirely.

Think about Paul McCartney and John Lennon. They wrote together, but no one would argue the songs wrote themselves. Each brought taste, instincts, and judgment into the room. Sometimes one took the lead. Sometimes the other pushed back. The value wasn’t in removing effort. It was in shaping it. The songs worked because someone still had to decide what stayed, what went, and what mattered.

AI should live in that same space. Not as the songwriter, and not as the audience, but as the collaborator in the room. The risk isn’t that students work with AI. The risk is that we stop being clear about who is making the decisions.

Right now, many schools are banning, ignoring, or quietly tolerating AI without teaching students how it actually works. That is a missed opportunity. Worse, it is irresponsible. We are putting powerful systems in students’ hands without giving them the intellectual tools to question them.

Bias is another area where the conversation tends to stay shallow. Yes, AI can reflect bias in training data. We all know that. The deeper issue is that AI embeds values. Research on generative AI in education shows that these systems carry what some scholars describe as a hidden curriculum: implicit assumptions about what counts as good writing, what a correct answer sounds like, which topics are worth engaging with, and what tone is considered appropriate.

Those values often come from outside the classroom, outside the community, sometimes outside the country. When AI becomes a learning partner, it quietly becomes a curriculum influence. That does not mean it should be rejected. It means it should be acknowledged and examined. Schools have always curated values. AI just makes the curation less visible.

Equity comes up a lot in these conversations, usually in terms of access. Who has the tools, who does not. That matters, but it is only half the picture.

There is also an equity gap in expertise. Some teachers are learning how to shape AI use thoughtfully. Others are overwhelmed, undertrained, or quietly opting out. UK surveys show this isn’t hypothetical. A large Pearson report found only about 9 % of teachers feel equipped to teach AI concepts, and nearly a quarter admit they lack confidence using AI at all, even as many recognise its growing role in schools. Another UK study reported that around three-fifths of teachers said formal AI training would make them more confident and likely to use these tools. 

That creates uneven learning environments even within the same school. Over time, it risks creating a system where students’ exposure to meaningful AI literacy depends on which classroom they happen to land in.

That is not a student failure. It is a system failure.

Learning has always involved friction. Confusion, frustration, false starts. AI reduces friction dramatically. That is often good. But friction is also where resilience, persistence, and deep understanding are built. We do not yet know how much friction is necessary, or what happens when it is consistently removed.

That uncertainty itself is a con. Not because AI is dangerous, but because we are running a massive, uncontrolled experiment on how people learn. Anyone claiming certainty here should be viewed with skepticism…

For an article entitled, ‘Pros and cons of AI in education’, it’s only fair to present a balanced view point, and there is a real upside that does not get enough credit. AI can free education from some of its worst compromises. Large class sizes. One-size-fits-all pacing. Feedback that comes too late to matter. Teachers stretched so thin they cannot teach the way they know they should.

If used well, AI can give teachers back time and cognitive space. It can help them focus on judgment, mentorship, and human connection. It can help students explore ideas more deeply, ask better questions, and iterate faster. At one UK international academy, integrating an AI-enabled teaching and learning platform helped reduce planning and marking time, gave teachers more capacity to tailor instruction, and boosted student confidence and assessment results.

But that only happens if schools are honest about what AI is changing.

AI is not just another tool. It is a mirror. It reflects what we value, what we measure, and what we have been willing to accept as learning. The biggest risk is not that students will use it. They will. The risk is that institutions will pretend nothing fundamental has changed.

“It has noticeably streamlined lesson preparation. Instead of spending hours sourcing materials, the Lesson Planning Wizard helps us pull together relevant content quickly, which has eased the workload and allowed teachers to focus more on delivery and support.” FilBrit International Academy UK

So maybe the most balanced conclusion for the pros and cons of AI in education is this. AI in education is neither a cure nor a catastrophe. It is a forcing function. It forces us to rethink assessment, redefine rigor, clarify the role of teachers, and decide what we actually want students to become.

If we do that work, AI can amplify the best parts of education. If we avoid it, AI will still reshape education, just without our guidance.

Want to discuss how we're using AI to shape our tools at Access Education? Speak to one of our experts. 

Pros of AI in Education

Removes artificial scarcity

Instant access to information, feedback, and support challenges outdated assumptions about memorisation and pace-based learning.

Enables more meaningful personalisation

Adaptive tools can meet students where they are, allowing for targeted practice and differentiated pathways that were previously impossible at scale.

Frees teachers to focus on higher-value work

Automating routine tasks creates space for mentorship, judgment, discussion, and relationship-driven teaching.

Accelerates exploration and iteration

Students can test ideas, revise work, and explore concepts more quickly, supporting deeper inquiry when used thoughtfully.

Forces long-overdue reassessment of assessment

AI exposes weaknesses in traditional grading and testing models, creating an opportunity to design better measures of learning.

Expands accessibility and inclusion

Translation, transcription, and assistive tools can reduce barriers for multilingual learners and students with disabilities.

Prepares students for an AI-shaped world

When taught explicitly, AI use builds fluency, critical evaluation skills, and the ability to work alongside intelligent systems.

Cons of AI in Education

Undermines traditional assessments

Essays, homework, and take-home assignments no longer reliably indicate understanding, weakening trust in grades and credentials.

Risks shallow learning if misused

When AI is treated as an answer machine rather than a thinking partner, it can short-circuit productive struggle and deep understanding.

Can isolate learners

Highly personalised, AI-mediated learning may reduce shared intellectual experiences and peer-based sensemaking.

Creates new equity gaps

Differences in teacher AI literacy and institutional readiness can widen disparities, even when student access appears equal.

Embeds unexamined values

AI systems reflect assumptions about knowledge, language, and correctness that may conflict with local or educational values.

Encourages quiet dependence

Without explicit instruction in AI literacy, students may rely on outputs without questioning accuracy, bias, or limits.

Introduces long-term cognitive uncertainty

The impact of constant AI support on attention, persistence, and resilience is still unknown.

Shifts systems faster than policies

Schools risk reorganising learning around AI informally, without clear norms, safeguards, or shared understanding.