By Lucas Massolo, Founder of Nextreks
The Shift
A high school student can now describe a website and watch AI draft the code.
She can upload a spreadsheet and ask for an analysis. She can turn a page of notes into a presentation, summarize a research paper, generate a logo, and produce a polished first draft before breakfast.
A few years ago, each of those outputs could signal a hard-won technical ability. Today, the output alone tells us less about the person who made it.
That should change how we prepare students for the world ahead—but not in the way the loudest headlines suggest.
The question is not whether students should still learn to write, code, calculate, research, or design. They should.
The question is what becomes most valuable when more of the technical execution can be performed with a prompt.
The answer is not another tool.
It is the set of skills that lets a person decide what the tool should do, determine whether it did it well, work with other people, and take responsibility for what happens next.
Those are durable skills.
What Is AI Actually Changing?
AI is not simply replacing entire jobs. It is moving into specific tasks inside them.
It can draft the email. Someone still has to decide what the email should accomplish.
It can generate the code. Someone still has to understand the user, define the constraints, test the system, and decide whether the tradeoffs are acceptable.
It can analyze the data. Someone still has to ask whether the data is relevant, whether the assumptions hold, and whether the conclusion should change a real decision.
The International Labour Organization estimates that one in four workers worldwide is in an occupation with some exposure to generative AI. Yet only 3.3% of global employment falls into its highest-exposure category. Because most occupations contain a mix of tasks and continue to require human input, the ILO concludes that job transformation is more likely than wholesale replacement.
AI does not encounter a job description.
It encounters pieces of the job.
As more of those pieces become automated or assisted, human value moves toward direction, context, judgment, creativity, and accountability.
Why Is This Urgent Now?
Because AI is not only making work faster. It is compressing the gap in basic execution.
In a study of 5,179 customer-support agents, access to a generative AI assistant increased productivity by 14% on average. The improvement reached 34% for novice and lower-skilled workers, while the effect on experienced, highly skilled workers was minimal.
A separate experiment involving college-educated professionals found that ChatGPT reduced the time required for common writing tasks by 40% while raising independently evaluated output quality by 18%. Once again, the people who initially performed less well benefited most.
That is one of the most important changes for students to understand.
A person with less experience can now produce a credible first draft, analysis, response, or prototype much sooner than before.
When acceptable execution becomes easier to access, acceptable execution becomes less distinctive.
14%
Average productivity increase among customer-support agents using a generative AI assistant.
39%
Share of workers’ core skills employers expect to change by 2030, according to the World Economic Forum.
76%
Share of U.S. job postings requesting at least one durable skill, across nearly 76 million postings analyzed by America Succeeds and Lightcast.
The Human Layer Grows
The labor market is not choosing between technical skills and human skills. It is asking for both—and the combination matters.
The World Economic Forum’s Future of Jobs Report 2025 found that employers expect 39% of workers’ core skills to change by 2030.
AI and big data are among the fastest-growing skill categories. At the same time, analytical thinking remains the most sought-after core skill, considered essential by seven in ten employers. Resilience, flexibility, leadership, creative thinking, and collaboration also remain critical.
America Succeeds and Lightcast reached the same conclusion from a different direction.
Their Durable by Design research analyzed nearly 76 million U.S. job postings from 2023 and 2024. It found that 76% requested at least one durable skill, 47% requested three or more, and eight of the ten most-requested skills were durable skills.
Even jobs that require AI skills still ask for communication, management, leadership, research, writing, and problem-solving. In one Lightcast analysis, only two of the ten most common skills in AI-related job postings were themselves AI skills.
That is the future students are entering.
They need technical fluency, including AI literacy. But technical fluency tells us which tools a student can operate.
Durable skills determine what the student can accomplish with them.
What Does Durable Mean?
Durable does not mean permanent, automatic, or immune to change.
Communication, critical thinking, leadership, creativity, and resilience still require practice. A student does not earn them once and keep them at the same level forever.
Durable means transferable.
The programming language may change. Critical thinking transfers.
The design software may change. Creativity transfers.
The project-management platform may change. Leadership and collaboration transfer.
The first career may disappear. Adaptability, curiosity, metacognition, and fortitude help a person build the next one.
As explored in The Skills That Don’t Expire, technical capabilities are often tied to a tool, process, or moment.
Durable skills travel across tools, industries, teams, and stages of life.
AI makes that distinction impossible to ignore.
The New Work of Thinking
There is another reason durable skills matter: using AI well is not cognitively passive.
A 2025 Microsoft Research study surveyed 319 knowledge workers and collected 936 examples of AI-assisted work.
Higher confidence in AI was associated with less critical thinking. Greater confidence in one’s own subject-matter ability was associated with more. The researchers also found that AI shifted critical thinking toward three activities: verifying information, integrating the response into the larger task, and maintaining stewardship over the work.
That distinction matters for high school students.
The risk is not simply that AI might produce a wrong answer. The deeper risk is that a student may receive a plausible answer before developing the judgment required to question it.
AI can write the essay. Can the student defend its argument?
AI can build the financial model. Can the student identify the assumption that breaks it?
AI can create the presentation. Can the student read a skeptical room and change course?
AI can recommend a decision. Can the student recognize who bears the consequences?
AI can generate ten options. Can the student decide which problem is worth solving in the first place?
These are not gaps around the real work.
Increasingly, they are the real work.
What Education Must Change
For generations, school has often rewarded students for producing the correct answer through an approved process.
AI has made the production of an answer abundant. Education now has to place more value on everything around it:
Framing the problem before attempting to solve it.
Asking questions that expose missing information.
Testing an output against evidence and context.
Explaining why one option is stronger than another.
Listening, negotiating, and working through disagreement.
Revising after feedback rather than defending the first attempt.
Disclosing where AI was used and owning the final result.
UNESCO’s AI Competency Framework for Students takes this human-centered approach. It pairs technical understanding with human agency, ethical responsibility, critical judgment, problem-scoping, creativity, and design.
The goal should not be to keep AI away from students. AI literacy will matter.
The goal should be to make sure the tool strengthens human agency rather than replacing the practice through which agency develops.
Students should learn to use AI as a collaborator, not an oracle.
Why Practice Matters
Durable skills cannot be downloaded. They are built through use.
A student develops adaptability when the original plan stops working.
She develops communication when another person does not understand her first explanation.
She develops leadership when a group needs direction but no one has assigned her authority.
She develops critical thinking when the most polished answer turns out to be wrong.
That is why real work matters.
An Aspen Institute survey of more than 550 employers found that 85% believe durable skills develop primarily through work and life experience rather than formal instruction.
Practice has to begin before the first consequential interview, team conflict, failed project, or paycheck depends on it.
At Nextreks, each Challenge combines an interactive micro-lesson with worked examples, guided practice, quick checks, and an optional real-world “Try It.” Across 80 Challenges—10 on each of eight islands; students build durable skills mapped to the 74 sub-skills in the licensed Pathsmith durable-skills framework. When students take a skill into the real world, they document what they did, evaluate how it went, and turn that experience into evidence for a growing portfolio organized through island-level Trait Cards.
That evidence matters because the hiring system is changing too.
Nearly 70% of employers in NACE’s 2026 survey reported using skills-based hiring. When asked how candidates should demonstrate their abilities, employers’ top recommendation was simple: share specific examples of using a skill to solve a problem.
This is the standard students should prepare for:
Not, “Tell us you are a problem-solver.”
But, “Show us the problem, what you tried, what changed, and what you learned.”
As explored in Why Portfolios Change the Trajectory for High Schoolers, students need more than a list of activities or traits.
They need a growing body of proof.
Where This Leaves Students
The students who thrive in an AI-rich world will not be the ones who avoid the technology.
They will not necessarily be the ones who produce the fastest first draft, either.
They will be the ones who can see a problem other people have missed, give the technology useful direction, recognize when a confident answer is weak, explain an idea so another person can act on it, build trust inside a team, and adapt when the conditions change.
Technical skills will continue to matter. Some will become even more valuable.
But every specific tool students learn today will evolve, and many technical tasks will become easier to automate or perform with assistance.
That is not a reason to prepare students for less.
It is a reason to prepare them for the part of the work that becomes more important when execution is abundant: choosing, questioning, connecting, creating, persuading, adapting, and leading.
AI will keep changing what students can do with a tool.
Durable skills determine what they can build with it, who they can build it with, and whether anyone trusts them to lead the work.
The more AI can do, the more durable skills matter.
Tools will change. Durable skills travel with you.
Sources
National Bureau of Economic Research, “Generative AI at Work”
Microsoft Research, “The Impact of Generative AI on Critical Thinking”
Nextreks LLC · nextreks.com
