Meaningful Education in the Age of AI
Introduction
In my work I spend a lot of time around software, technology, and now increasingly AI. I am seeing first-hand how quickly it’s changing the way my business operate, and how our own AI changes the live of others. So I often get asked about what all this means for the future. Education in the age of AI often comes up in that conversation and as a parent with school-age children, it carries a personal relevance.
I am not writing this article as an academic or as a teacher, that’s not my background. I’m trying as someone who works with AI every day in business to think about what it might mean for families and schools that I know. What seems to be a theme is that schools don’t yet have enough confidence to adopt AI convincingly or to build a view of how it might be used. Teachers invest effort year after year into their lesson plans and schools run on an established rhythm with little room for the culture shock that AI brings. In startups, change comes fast and often and so adaption is something I’ve been learning about for more years than I care to remember. With the changes that AI will bring, the work and experiences of teachers will need to be utilised in new ways if schools are to grasp what’s needed in education in the coming decades. The question is how AI can build on the good work of educators and enhance it in a meaningful and relevant way.
What I’ve tried to do here is organise some ideas and experiences from my business experiences, into a narrative - as much as a thought experiment as anything else. So the first section sets out a broad view of how I understand teaching to have evolved to a point where many current themes lend themselves towards utilising AI. I try to detail some of the challenges AI presents, and what opportunities it opens up too. The second section then goes into some practical detail about how the things we use in our business today could translate in classrooms too. Perhaps this article will spark some good debate — about how we can use AI in ways that keep teachers at the centre, prepare students for a world where they may never be the smartest in the room, and bring forward the human skills that matter most.
Part One:
The Changing Role of Education in the Age of AI
An existential shift is happening…
Having spoken to some teachers, what I understand is that the classroom has always worked on the simple but sacred assumption that the teacher is the one who knows the most (which resonates with my own memories of school). That assumption provides teachers their authority and their central role. But with the rise of generative AI — systems like ChatGPT and NotebookLM — knowledge, facts, and connections are now accessible in seconds. In many domains, AI already outpaces humans in speed, and recall, and teachers will likely need to get used to the fact that they may no longer be the ones with the deepest store of information.
This alters something important: what is the role of a teacher, when the teacher is no longer the most knowledgeable? For students too, the same challenge looms; in their education and their future work, students will rarely be the smartest entity in the conversation. They will enter a world where intelligence is distributed, and where in probability AI will always be in the room. Many people conclude that we will all need to learn how to bring the more distinctively human skills to the table to create our unique value. The challenge facing teachers applies equally to their students and to the rest of us.
Understanding the ‘isms’.
Education has moved through major shifts before and for the last hundred years they seem to be categorised with ‘isms that need a little explanation.
First, Behaviourism apparently treated learning as the transmission of knowledge from teacher to pupil, reinforced through repetition and reward. So, it was effective memorisation but limited without fostering deep understanding or creativity.
Then, I understand that Constructivism reframed learning as something pupils actively build for themselves. Knowledge becomes constructed through the perspective of doing, not passively absorbed. But the experts concluded it was incomplete as it lacked connected from social participation.
For the last period of time, Social constructivism has - I think importantly from an employer’s perspective - placed emphasis on dialogue, collaboration, and the co-creation of meaningful outcomes. This is the participation that teaches the type of knowledge beyond doing and into relational participation with others. Here, the teacher facilitates live interaction, supporting not only factual learning but also skills such as research, critical thinking, problem solving, and creativity.
The evolution of meaninful learning
Each teaching evolution widened the scope of what education could achieve and opened new forms of knowing. Cognitive scientist John Vervaeke offers a helpful frame for this through his work on ways of knowing.
Education can move us from propositional knowledge — abstract ideas that are easy to state and measure: “I know that music is heard and evokes feelings.” To procedural knowledge — understanding processes and methods: “I can explain what a treble and bass clef are and how they’re used.” To perspectival knowledge — embodying a skill in practice: “I can play enjoyable music on a guitar.” And finally to participatory knowledge — engaging in a meaningful activity with others: “I can play my guitar in a group, which I enjoy as a meaningful pastime.”
As the knowledge becomes richer, it becomes harder to measure — but also more meaningful for the individual and more valuable for society. This is why social constructivism, which many classrooms now aspire to, matters: students working together on shared problems, guided by the teacher, develop not just knowledge but the wider human skills that flourish at the participatory level.
Dialogue and collaboration are important but difficult to capture in ways that schools and assessment systems recognise. Because of that difficulty, schools often default back to tasks that are easy to measure — written work, copying from the board, long essays, filling PowerPoints. The result is that very often students may spend five minutes on research and 25 minutes writing it up, when the real value I believe lies the other way round.
This tendency undermines the knowledge and skills that social constructivism was meant to foster, and it distorts creative subjects most of all. Art students are asked to read and write about artists instead of creating art. Music students may end up memorising the chromatic scale or cataloguing instruments in detail, rather than spending time actually making music. These compromises show how the pressures of measurement can flatten education into written propositions, even in domains where expression, creativity, and participation should be at the centre. And let’s face it, in the working world, the human skill set that makes us team players who can help solve problems, is more valuable than knowing the timeline for the Battle of Hastings.
Relevance Realisation in the age of AI
If AI is now “smarter” in the sense of holding more facts and generating faster outputs, what remains a uniquely human skillset? In my work I find that the answer lies in those skills AI cannot easily replicate: fresh insight, leaps of imagination, good intuitive judgment, originality, curiosity, and the ability to connect beyond the written information, connecting with others with purpose and empathy.
From my wider reading I’ve understood that international bodies such as the OECD and the World Economic Forum repeatedly identify the most vital “21st-century skills” as collaboration, creativity, problem solving, critical thinking, and adaptability. Research skills matter just as much — not simply gathering information, but questioning its relevance and quality. Curiosity drives inquiry, creativity enables new connections, problem-solving applies judgment, and collaboration ensures knowledge is developed socially, not in isolation.
Together, these skills underpin what cognitive scientists have called ‘relevance realisation’ — the ability to discern what information matters, as well as when, and how to apply it to create value. It seems like a neat term that explains the skills the world will need in abundance when humans are no longer the smartest entities in the room.
In business, this shift is already visible to me as a leader and I try to role-model it when I bring curiosity and creativity, encouraging better questions, encourage exploration of alternatives or ‘thought experiments', and frame problems in new ways. There is an acceptance that AI will surface the data and generate the summaries we need; what matters is our capacity to steer, interpret, and decide. Schools now face a similar change and that is an opportunity. Putting these highly human traits at the centre of how young people learn is the best way that they can stay relevant.
AI, Friend or Foe?
It is true that the things that make things easier in the short run sometimes harm us in the long run. It is true that AI is making commonplace jobs obsolete already. Companies and nations are allocating so much money to AI however, that it is hard to imagine that it is not here for the long haul.
If we choose to receive AI not as an intruder, but as a co-worker then In my experience the perspective changes on a lot of things; it alleviates some of the tension and opens fresh opportunities to be more relevant in the age we live in. Rather than a challenging subject, AI becomes an enabler.
Our company’s leaders are beginning to show that by using AI to strip away repetitive work and administrative burden, for example note taking and meeting minutes which are all now transcribed, they can free more time for the work of curiosity, creativity, and better connection. These behaviours are increasingly the ones that drive value.
In the classroom, the same logic probably applies. AI could capture dialogue, highlight key points, and generate resources for reflection, evaluation and revision, where more time in the room was given to critical thinking and less time to scribing.
Conclusion Part 1.
In this way, as AI is adopted as a multiplier, the teacher’s role has the potential to shift from being the sole source of knowledge to being the most creative facilitator in the room: the one who designs the questions, curates the dialogue, and models how to use AI critically and responsibly.
We can argue all day over whether or not AI is a good thing, but the fact is it is here and my experience has been that my teams are better off embracing it’s exciting potential.
Part Two:
Making AI Meaningful in Education
The following section draws on my own experiences from business, where tools like ChatGPT and Notebook LM are already maturing and proving their value. The exact platforms will probably change over time, and schools will eventually make their own choices about which to adopt. What follows is not the only way forward, but one possible recipe for how teachers and students could begin an ongoing experiment to change their practice.
A Teacher Workflow for Lesson planning
Building on the foundation
Year after year, teachers invest in building their materials. So using the investment in those materials as a foundation for working with AI could be an invaluable start point for teachers and schools. A curriculum and all its rich lesson plans used effectively as AI training materials might be the first step in using AI in class effectively. Taking those existing resources — from worksheets to handwritten notes — then digitising them using nothing more than a smartphone camera could be the beginning of a valuable enrichment exercise. If teaching is anything like being a knowledge worker in my business, this is the start.
Snapping a picture of some old notes or a text book straight into ChatGPT could be the start. Once digitised, and all materials uploaded into the app, the AI’s information augmentation can start. This is not just about “getting more text back,” though it can be if materials are scant - but about using the AI to help organise, check, and strengthen what already exists.
For example, a teacher could upload their notes on the Battle of Hastings and ask ChatGPT to organise the events into a clear timeline, group information into themes such as strategy, leadership, and context, flag likely misconceptions that students may hold, and expand shorthand notes into coherent teaching points.
This turns existing material into a structured and relevant digitised resource that get used within trained AI projects again and again. The teacher would then have the choice to refine this — deciding what to keep, what to cut, and how to frame it for their class. The deep research capabilities, along with the alternative models available in the platform we have found to be an accelerated source of deepening understanding for team members with the experience to understand them. For teachers, this can become an act of relevance realisation, drawing out what really matters for their students at that moment, in our ever faster world.
Enrichment
What I found is that the breadth of learning and teaching materials needed in a thriving organisation vary. For that reason - creating augmented information and project files in ChatGPT is not an end point. From there, the refined and enhanced versions of lesson notes can be copied and uploaded into NotebookLM, which creates a personal ‘notebook’ with it’s own projects containing any first and third party materials, website links and other content required. Multifaceted data inputs like this have provided a rich frame for learning in our business already. With curated content as a starting point in a ‘notebook’, the system can then generate targeted outputs with specific outcomes in mind. In schools, where different types of teaching and learning are needed; interactive mind maps, quizzes, interactive podcasts, short explainer videos, or interactive tasks are all available features. It seems to me at least, that these could be used very effectively as aligned inputs and outputs that match the teacher’s intentions in many lessons and assignments.
A Student Workflow for learning
Students could follow a very similar workflow, though their starting point would be a little different. Instead of long-term teaching materials, they begin with their own work: class notes, handouts, assigned readings, or sources they have researched themselves.
These materials would be uploaded into ChatGPT. As with the teacher’s process, the aim is always to structure and enrich what they already have. The AI might turn their bullet points into a coherent summary, show how ideas connect in a hierarchy, or surface gaps where further evidence is needed.
Reframing AI
Rather than being seen as a plagiarism tool in education, students refining the AI’s draft, deciding what is useful, what is missing, and how to frame it in their own words then choosing the Notebook LM or other output formats needed to meet their intended outcomes is exactly the learning style and process that I believe will continue to be needed in the work place.
When I think about the amount of time kids spend on write up and even scribing in class, versus the time that could be spend on the critical thinking and dialogue of research and refinement. This approach flips the time balance: less time on repetitive writing, more on thinking, refining, and creating.
Phrases I’d lift from the working world to describe what they honing skills for would be ‘punching above their weight’ in terms of information access, ‘joining the dots’ between their sources, so that their answers are more creative and original and that ‘earn the right’ to the teacher’s praise as they progress on their learning journey.
In doing this, they practise relevance realisation: the skill of judgment and prioritisation.
The Daily Practice
For teachers, this model could keep their years of preparation and their student relational skills central while offering ways to extend for the future. Their role could shift from being the only source of knowledge to being the most creative facilitator: modelling how to refine content, question AI critically, and connect material to create meaning.
For students, the process could mirrors this. They’d learn to work with AI as a partner, not a shortcut, and exercise curiosity, critique, and creativity as they do so.
As I write, my thought go back to the ‘isms’. Social constructivism was about dialogue, collaboration, and co-creation; the most modern, aspiration and meaningful of the isms, but the central barrier as I’ve understood it is that dialogue and collaboration are difficult to capture in ways that schools and assessment systems recognise.
Better Measurement
So how could AI in lessons help teachers and students contribution together in real time in ways that co-develop modern, relevant skills and learning and provide for measurement?
Transcription tools now provide part of the solution.
By running in the background, tools like otter.ai create a record of who said what, what ideas emerged, and how thinking developed. Teachers would not need to read every word, because transcripts can be summarised automatically, searched for key points, and revisited when needed for reference; they capture turning points, key questions, and unresolved points too. Over time, they build a record of engagement — not just of memorised facts, but of reasoning and collaboration.
This mirrors business practice, where transcription has become routine in meetings. There, transcripts are used to generate summaries, action points, and records of key insights. In schools, when paired with NotebookLM outputs, perhaps they could provide a workable way to make social constructivist learning more measurable — capturing interaction without reducing it to rote writing.
Collaboration and co-creation
Teachers might project a ChatGPT thread containing their structured notes, set out the learning goal, and show the class how they prepared it. This frames the tool as something transparent and shared.
Teachers can then model the new ways of working by prompting out loud: why they are giving context, what format they are asking for, and how they will use the result. A simple frame such as why / what / how can help students see the process. Students might then suggest prompts, critique outputs, and even take turns as a “prompt lead,” coming to the front to write the request and explain the intention behind it.
To close the co-creation phase, teachers might ask the model to summarise the class dialogue under the key headings used in the lesson, using excerpts from the transcript to anchor it. These materials, combined with the refined preparation, can then be moved into NotebookLM and be used within videos, podcasts, mind maps, quizzes as learning materials. The result is a package that contains the structured content, the class discussion, and the distilled notes — from which more outputs can be generated as needed.
Embedding and follow-up
Homework would be an extension of this way of working and in a subsequent lesson, the teacher could revisit both their own enriched notes and the student-refined versions. Together, the class could discuss which details mattered most? What did AI highlight that we overlooked? What did we cut that it over-emphasised?
This cycle could build a culture of co-learning, where teacher and students follow the same discipline: start with what you have, use AI to structure and expand, apply human judgment to refine, and then create targeted outputs for deeper learning.
Assessment and feedback
Traditional assessment rewards memorisation more than creativity or collaboration. By using AI tools, schools could begin to broaden this picture. ChatGPT might analyse drafts, highlighting strengths and raising questions. NotebookLM could generate fun quizzes for developing mastery of a topic. Transcripts could provide evidence of reasoning and participation.
Together, these elements create a more balanced view of learning. Teachers remain the final arbiters, but the burden of capture and organisation is eased.
What Next?
In the startup where I work, we encourage ideas sharing. One idea that got a lot of interest from the team was Simon Sinek’s work on ‘Infinite Games’. It got interest as The Infinite Game provides a frame of reference for fast paced change. In an Infinite Game, there are no winners or losers, the aim of the game, which our professions place us in, is to stay in the game for as long as we want to. This perspective encourages openness and a creative playful mindset, which rewards trying things and learning.
The adoption of AI centred practices at work find people optimally motivated by the opportunity of AI enhancing outcomes for them and at the same time as the threat of their knowledge working being hollowed out and a loss of relevance. Adoption in schools will depend on finding a curiosity and cultural interest. By joining this Infinite Game, schools will need to find access to the right tools, devices for students, agreed policies on ethical AI use and teachers may need training in prompting, facilitation, and lesson design.
Experience tell me that a phased approach may work best: start small, experiment in one area - a subject or year group, and scale from there. Equity of access is essential if benefits are to be shared fairly.
Conclusion
From what I learned researching this article, education has always evolved, each time expanding the scope of what learning could be. AI now presents new opportunities. It challenges the assumption that the teacher is the smartest in the room, but it also provides tools to deepen enquiry, capture dialogue, and support assessment.
The opportunity is not to replace teachers or discard the materials they have built, but to amplify their work and free them to focus on the parts of teaching that are most human. Students, meanwhile, learn the skills that will matter most: curiosity, critical thinking, creativity, collaboration, and the ability to evaluate and apply AI outputs responsibly.
If schools, like some workplaces embrace these possibilities, they could quickly become more adaptive, modern, and flexible — preparing students not only for exams, but for a world in which AI is always present, and where the most valuable human contribution will be to bring meaning, creativity, and connection.