Why Might Young People Be More Resistant to AI Than We Are?

I have gradually begun to notice something in conversations with my daughter and people around her age: some young people seem to meet AI with more resistance than anticipation.

This is not a statistical conclusion, and it is not a label for an entire generation. It is a question that has surfaced through family conversations, anxieties around creative work, and uncertainty about what a future career will even mean. Why might the generation most at home in digital life be less willing to trust AI than we expect?

Adults often encounter AI as another layer of leverage. We lived through dial-up internet, search engines, smartphones and cloud software. We learned to interpret each wave of technology as a new capability: a way to write faster, organize more clearly, or make something that once required a larger team.

For young people who are still forming an identity, AI arrives at a very different moment. It does not appear after they have established a career. It appears before they have had the chance to prove to themselves what they can do.

Perhaps the resistance is not to AI, but to premature erasure

When an adult sees AI produce an essay, the first thought might be: “Good. That could save me two hours.”

A young person learning to write may see the same result and wonder: “Then what is the point of spending years learning how to do this?”

Both reactions make sense. The difference is not who understands technology better. It is where each person stands in life. Adults may already have professional experience, relationships and judgment that AI can amplify. A young person is still building those foundations while being compared with a system that seems faster, endlessly patient and never tired.

If our only message is “learn to use it quickly or you will be left behind,” we deepen the fear underneath the resistance: your value depends on your ability to keep pace with a machine.

The real question may not be adoption. It may be the quieter uncertainty beneath it. If work can be generated instantly, does effort still count? If answers are always available, does understanding still matter? If a system appears able to do almost everything, is there still a reason to slowly become a particular kind of person?

Young people may be closer to the cost

Adults tend to begin the AI conversation with productivity. Young people may encounter identity, trust and fairness first.

They face suspicion that an assignment was written by AI. They see creative platforms fill with work that looks familiar but has no clear relationship to an author. They watch portfolio standards, entry-level jobs and evaluation systems shift before they have even entered the workplace.

Resistance, then, is not necessarily conservatism. It may be a form of protection: protecting effort that has not yet been measured, protecting the relationship between a work and its maker, and protecting the weight of the sentence, “I made this.”

UNICEF's consultations with adolescents about AI offer an important reminder. Young people are not merely users who need protection; they should participate in decisions about systems that shape their lives. The next generation should not simply inherit an AI world designed by adults and then be told to adapt.

Education cannot stop at prompts and tool lists

If education teaches only how to write prompts, choose models and increase output, we will have handed the next generation our own anxiety about efficiency.

The UNESCO AI Competency Framework for Students places a human-centred mindset and human agency alongside technical understanding. The order matters. Students need room to decide why they would use a system, what responsibility they will accept, and what they may choose to refuse before learning how to operate it more efficiently.

The AI education I care about would help young people practise things that are harder to measure:

• Forming their own questions instead of transferring every question to a system.
• Distinguishing what is convenient, what is correct and what is worth doing.
• Taking responsibility for an adopted answer rather than assigning responsibility to the tool.
• Making something with other people and learning that trust is not a by-product of efficiency.
• Continuing to learn and create even when there is no immediate reward.

None of this is anti-technology. These are the capacities that allow people to keep a direction when technology becomes more powerful.

What is uniquely human may not be a skill

We often ask what AI cannot do, then search for a safe territory: creativity, empathy, craft or intuition. But any answer built this way can be unsettled by the next model release.

Human value becomes fragile if it depends on a list of things machines cannot yet do.

A more durable starting point may be this: people do not merely produce outcomes; they live with them. We are hurt, repaired, committed and changed through relationships. We sometimes choose the difficult path because something is worth doing. Memory, the body, place and other people enter our judgment. These are not mysterious superpowers. They are the limits and responsibilities that come with being alive.

AI can offer options, but it cannot live the life that follows a choice. It can imitate a tone, but it cannot lose a relationship because of what it said. It can generate a plan, but it does not have to keep caring for the people affected when that plan fails.

Perhaps human value is not our ability to remain permanently better than machines at one task. It is our obligation to answer why a task should be done, whom it should serve, and who will carry its consequences.

Accompaniment matters more than persuasion

If a young person resists AI, we do not need to correct them immediately. Nor should “the future is inevitable” be used to end the conversation. A technological trend may be difficult to reverse, but there has never been only one way for people to live with technology.

Adults can begin by treating resistance as a signal rather than an obstacle. What does the person fear losing: the possibility of work, the authenticity of creation, a fair evaluation, or trust in their own ability? Different fears require different responses.

We may also need to admit that we do not know. The next generation will enter an adulthood none of us has experienced. Rather than turning our excitement into their obligation, we can build principles to revisit together: preserve choice, see the cost, ask for transparency, protect relationships, and refuse to let efficiency determine human dignity.

The deepest challenge may not be teaching young people to use AI. It may be accompanying them as they discover what remains worth understanding, choosing and carrying themselves as AI becomes more capable.

Three questions that follow

Are young people resistant simply because they are unfamiliar with AI?

Not necessarily. Familiarity and trust are different. Resistance may come from concerns about fairness, creative ownership, privacy, work or identity. Understanding the specific concern matters more than simply increasing tool exposure.

Should schools prohibit students from using AI?

A complete ban makes it difficult to develop judgment, while unrestricted use can bypass the learning process. A better approach is to clarify when independent thought is necessary, when collaboration with AI is permitted, and how its use must be disclosed and owned.

If AI can perform better, why should a person still practise?

Practice is not only preparation for outperforming a tool. It develops taste, patience, judgment and self-knowledge. Even when a result can be generated, people still need to know what is worth keeping and why they are willing to put their name on it.