A guest post written by Kristine Mizzone. AI is changing what students can do. But what if the more important question is what they still get to practice?
AI in education is often discussed in terms of what it can help schools do faster, better, or at greater scale. But as technology takes on more of the work once performed by people, another question becomes increasingly important: what might students no longer have the opportunity to practice for themselves?
In this blog, Kristine Mizzone, a well-recognized voice in international education with a focus on social-emotional learning, leadership, and building thriving school communities, invites us to look beyond what AI can accomplish and consider a harder question: What do we want young people to become capable of doing for themselves?
There is a strange cultural phenomenon happening around optimization. We have looks-maxxing, fitness-maxxing, productivity-maxxing and, increasingly, life-maxxing. Underneath all of them is essentially the same question: How do I get the most out of this one life I have?
Bryan Johnson, the technology entrepreneur known for his extreme approach to longevity and self-optimization, has made this question almost a science. He tracks his sleep, diet, exercise, and biomarkers with extraordinary precision, all in pursuit of a longer, healthier, and more optimized life.
Whether or not we agree with his methods, the underlying question is compelling: If we only get one life, why wouldn’t we try to get the most out of it?
Education has its own version of that question: How do we get the most out of the time young people spend with us?
We have become very good at optimizing school. We optimize instructional minutes, curriculum coverage, assessment, achievement, productivity, and increasingly, personalization. Now AI promises to help us optimize even more. It can generate resources, analyze student work, provide feedback, translate communication, personalize learning, summarize meetings, and increasingly act on our behalf.
So the question we seem to be asking is: What can AI do for us?
I think we need to ask another question alongside it: What should humans still practice?
Because AI doesn’t just change what students can do. It changes what they get to practice.
Consider something as ordinary as writing an email. A student needs to send a difficult message to a teacher. Maybe they are asking for an extension, addressing a disagreement, or advocating for themselves. AI can write the email in seconds, and it may even produce a better email than the student would have written.
But what did the student not practice?
They didn’t have to figure out how to communicate what they needed, consider the teacher’s perspective, think about tone, or decide how direct to be. The email got written, but the student may have missed an opportunity to develop communication, judgment, empathy, and perspective.
The same thing happens with problem-solving. If a student immediately asks AI for the answer, the problem may be solved, but they may not have practised persistence, creative thinking, or sitting with ambiguity.
There is a difference between having access to a capability and developing a capability.
AI gives us extraordinary access to capabilities. But access is not development. We develop through practice.
This is why AI forces us to look at the things we ask students to do differently. Instead of asking only whether a task is necessary or efficient, we need to ask what the doing itself accomplishes.
What was the student supposed to become by doing this?
We sometimes talk about school as though its primary purpose is to produce academic outcomes. Students need to learn to read, write, calculate, research, present, and solve problems. They need to pass assessments, graduate, and eventually enter the world of work.
But underneath those outcomes are competencies that take years to develop.
A child doesn’t become a good communicator because communication appears to be a learning progression. They become a good communicator by communicating. They develop judgment by making decisions, collaboration by working with people who think differently, self-management by having something meaningful to manage, and agency by having something meaningful to decide.
School, in this sense, is a practice field.
This is also why the ISCA Student Standards are useful. The standards, developed by the International School Counseling Association identifies four interconnected domains of student development: Social-Emotional, Academic, Global Perspective & Identity Development, and Career. Together, they offer a broader picture of what young people need to develop throughout their education and beyond.
The important word is interconnected.
These aren’t four separate versions of a student. They are different dimensions of the same human being. A student making a complex decision may be drawing on academic thinking, self-awareness, and responsible decision-making. A student navigating disagreement is developing communication, relationship skills, identity, and perspective. A student exploring a future career is also exploring values, strengths, identity and possible futures.
And none of these competencies develops simply because we have named them as outcomes.
They develop because students practice them.
That makes the arrival of AI particularly interesting. If AI can write, where are students practicing communication? If it can generate solutions, where are students practicing problem-solving and persistence? If it can recommend what to do next, where are students practicing decision-making and self-management?
The question isn’t whether AI can support these competencies. Of course it can.
The question is whether, in making learning more efficient, we accidentally remove some of the experiences through which those competencies develop.
None of this means we should preserve every difficult task in the name of struggle. Schools contain enormous amounts of friction that serve very little developmental purpose.
Entering the same information into multiple systems, formatting spreadsheets, and writing repetitive emails are not experiences that young people need to become better humans. If AI can remove that friction, we should let it.
But there is another kind of friction: figuring out what you actually think, resolving a disagreement, making a decision when there isn’t an obvious answer, revising something you thought was finished, sitting with uncertainty, trying something and failing.
Some friction isn’t a bug in the human experience. It’s the gym.
The challenge for schools will be learning to tell the difference.
Which friction is waste, and which friction is doing developmental work?
There is another question I keep coming back to: What happens when AI gives us time back?
Imagine AI saves a teacher five hours a week. Those five hours could easily disappear into more planning, more data analysis, more meetings, and more initiatives. The school becomes more efficient, but the teacher doesn’t actually experience more capacity.
We’ve simply built a faster treadmill.
The same thing can happen to students. If AI makes research, writing, and organization faster, we could simply use that capacity to fit even more into the school day.
More content. More assignments. More output. More achievement.
But that would miss something important.
The point of efficiency is not to do more. The point of efficiency is to make room for what matters.
If AI gives a teacher five hours back, the question isn’t simply, What can we accomplish in those five hours? It is: What do we want those five hours to make possible?
And if AI gives a student an hour back, perhaps the question is even more interesting: What do we want that hour to become?
Imagine a school five years from now. AI handles much of the administrative work. It analyzes student work, generates resources, provides feedback, translates communication, personalizes practice, and supports planning.
Now imagine that school makes a deliberate choice not to fill all of that newly available capacity with more work.
What would actually be different?
Perhaps a teacher has more time to sit beside a struggling student, notice something the data didn’t capture, have a conversation, or simply respond to what is happening in front of them.
Perhaps students spend less time completing repetitive tasks and more time making things, collaborating, encountering different perspectives, wrestling with problems that don’t have obvious answers, and making meaningful decisions.
Maybe even the timetable begins to change.
Instead of asking primarily, What content do we need to cover?, we might start asking:
What competencies do young people need opportunities to practice?
And then the harder question: Where in the school day do they actually practice them?
If we say judgment matters, but students rarely make meaningful decisions, we shouldn’t be surprised when they struggle with judgment. If we say collaboration matters, but most of their work is individually optimized, we shouldn’t be surprised when collaboration is difficult. If we say agency matters, but every next step is prescribed, we shouldn’t be surprised when students wait to be told what to do.
You don’t develop competency simply by valuing it. You develop it through practice.
We often describe agency in terms of choice, voice, ownership, and independence. But AI forces us to make the idea more precise.
Agency can’t simply mean, I can do this myself.
Machines may soon be able to do almost everything themselves.
Perhaps agency becomes: I can decide what I should do myself.
And perhaps it goes deeper still: I can decide what is worth doing because doing it changes me.
An agentic young person knows when to use AI, when to automate, when to delegate, and when to ask for help. But they also know when to say, This is mine. This is something I want to practice. This is something I don’t want to outsource.
That may be exactly the kind of agency young people need in a world where the ability to delegate work to machines becomes almost limitless.
There is an interesting tension here with the growing conversation about agentic technology.
AI is becoming increasingly agentic. It can plan, act, execute, make decisions within parameters, and work on our behalf.
But more agentic technology does not automatically mean more agentic humans. In fact, it could produce the opposite.
Imagine a school where AI creates the lesson, personalizes the pathway, generates the feedback, schedules the next task, communicates with families, summarizes meetings, and recommends interventions.
Technologically, that school is incredibly agentic. Everything moves. Everything responds. Everything is optimized.
But what are the humans practicing?
A school can become more agentic as a system while making its students less agentic as people.
Unless we deliberately design for both.
An agentic school should not simply be one where technology can act on our behalf. It should be one where humans have more capacity to act on purpose: to choose, question, create, connect, decide, and lead.
We started with life-maxxing and the question: How do I get the most out of this one life?
Education has its own version: How do we get the most out of the time young people spend with us?
But perhaps the better question isn’t: How do we maximize school?
It is: What are we maximizing for?
Because maximum and meaningful are not synonyms.
AI is going to give schools extraordinary new choices about how to spend human capacity. But AI cannot tell us what that capacity is for.
AI cannot decide what is worth optimizing. That is still our job.
For school leaders, this is not simply a question of where AI should be introduced. It is a question of what kind of learning environment we want to introduce it into.
Every task we automate, every resource we generate, and every hour we give back creates a choice. We can use that capacity to produce more, or we can reinvest it in the experiences through which young people develop judgment, resilience, empathy, creativity, and agency.
The future of education will not be defined solely by what AI enables schools to do. It will also be shaped by the experiences schools choose not to outsource and by who students become through those experiences.
For schools ready to move from discussion to action, Faria Learn offers facilitated and on-demand professional learning to help educators and school leaders develop a thoughtful, human-centred approach to AI.
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Kristine Mizzone is an education consultant specializing in social and emotional learning (SEL) and leadership. She serves as the Learning and Development Coordinator for the International School Counselor Association (ISCA) and teaches aspiring school leaders as an adjunct professor at The College of New Jersey.
An experienced facilitator, coach, and curriculum designer, Kristine helps schools create coherent, learner-centered approaches that intentionally address students’ social and emotional needs alongside academic growth. Her work focuses on embedding life skills within and across the curriculum – not as an add-on, but as an essential part of everyday learning.
With two decades of experience in U.S. public schools and international schools, Kristine most recently served as Director of Learning at Benjamin Franklin International School in Barcelona and as Curriculum and Professional Learning Coordinator at International School of Beijing. Kristine is the author of The Leap Year: Practical Advice and Insights for Those Navigating Career Transitions. Her book offers guidance for those considering new pathways such as consulting and entrepreneurship.
Kristine’s Website: https://www.collabconsulting.org/
Follow Kristine on LinkedIn: https://www.linkedin.com/in/kristine-mizzone/
This article was written by Kristine Mizzone, with AI used as a thinking and writing partner during the drafting and editing process. AI supported the development of ideas, organization, and language; the perspective, arguments, examples, and final editorial decisions are Kristine’s.
In many ways, that process reflects the very question explored in this article: What do we want AI do for us, and what do we still want to do ourselves?
AI helped with the work. The thinking remained human.
