Driving Enterprise Impact Through Scalable AI Adoption

20 Feb 2026 12:00h - 13:00h

Session at a glance

Summary

This town hall discussion at the World Economic Forum explored the evolving dilemmas around knowledge and education in the age of artificial intelligence. The panel featured Professor Debbie Prentice, Vice Chancellor of the University of Cambridge, alongside business leaders Aidan Gomez, CEO of Cohere (an enterprise AI company), and Hugo Sarazen, CEO of Udemy (an online learning platform). The conversation began with a poll asking what has become the scarcest resource in our era of instant AI answers, with critical thinking and sustained human attention emerging as top concerns among audience members.

The panelists discussed how AI is fundamentally changing the educational landscape by democratizing access to knowledge and creating “millions of polymaths” through large language models. However, they identified significant challenges, including the risk of false mastery where students believe they understand concepts without truly grasping them, and the difficulty of maintaining deep learning when answers come so easily. Gomez emphasized the importance of testing students without AI tools to assess genuine understanding, while Sarazen highlighted AI’s potential to personalize learning experiences and provide one-on-one tutoring at scale.

The discussion revealed a consensus that humans must focus on developing skills that AI cannot replicate: asking the right questions, critical thinking, and quality assessment of AI-generated content. The panelists argued that while AI excels at the “middle part” of problem-solving, humans remain essential for framing problems and evaluating solutions. They also addressed concerns about the future of traditional education, with debates about whether university degrees should be “unbundled” and how institutions must adapt to rapidly changing skill demands in the workforce. The conversation concluded with recognition that this technological transformation requires ongoing dialogue about preserving human agency and expertise while harnessing AI’s educational potential.

Keypoints

Major Discussion Points:

– The scarcest resource in an AI-driven world: The panel explored what becomes most valuable when AI provides instant answers – with audience polling showing critical thinking and sustained attention as top concerns, while panelists emphasized deep mastery, self-knowledge, and the ability to ask the right questions.

– The evolution from online learning to AI-powered education: Discussion of how education has progressed from MOOCs and traditional online courses to personalized, adaptive AI tutoring systems that can provide one-on-one coaching at scale, addressing the historical “Bloom two-sigma problem” of individualized instruction.

– The role of humans in AI-enhanced education: Debate over where human intervention remains essential, with consensus that humans excel at the “front end” (asking critical questions, providing context) and “back end” (quality assurance, validation) while AI handles the middle processing, and the continued importance of human teachers as storytellers and motivators.

– Testing and assessment challenges: Extensive discussion of how to evaluate learning when AI tools are readily available, including the debate over testing “with or without the calculator,” the difficulty of detecting AI-generated work, and the need for new methods to assess genuine human understanding and skill retention.

– The future of expertise and institutional authority: Examination of how traditional sources of knowledge and authority (universities, libraries, expert credentials) are being challenged by AI “polymaths” that can access vast knowledge across domains, and the implications for trust, explainability, and the bundled university experience.

Overall Purpose:

The discussion aimed to explore the fundamental challenges and opportunities that AI presents to education and knowledge systems, bringing together perspectives from traditional academia (Cambridge University) and commercial AI/education technology companies to examine how learning, teaching, and credentialing might evolve in an AI-dominated landscape.

Overall Tone:

The tone was thoughtful and exploratory rather than alarmist, with participants acknowledging both the transformative potential and genuine risks of AI in education. While there were moments of concern about losing human agency and the challenges of maintaining educational quality, the overall discussion remained optimistic about AI’s potential to enhance rather than replace human learning. The conversation was collaborative, with panelists building on each other’s ideas rather than presenting opposing viewpoints, and maintained an academic yet accessible tone appropriate for the World Economic Forum setting.

Speakers

Speakers from the provided list:

– Debbie Prentice – Professor and Vice Chancellor of the University of Cambridge, representing the not-for-profit education sector

– Aidan Gomez – Co-founder and Chief Executive Officer of Cohere, an enterprise AI company developing advanced language models for business use

– Hugo Sarazen – President and Chief Executive Officer of Udemy, which provides business and leadership development courses (including AI courses) to organizations worldwide across various sectors including financial services, higher education, government, manufacturing, and technology

– Audience – Multiple audience members who asked questions during the town hall discussion

Additional speakers:

– Anna Van Niels – Director of the Livium Trust

– Nathaniel – Runs an education company in Australia

– Pranjal Sharma – Author and analyst from India

– Kian – CEO of an AI company called Workera

Full session report

This panel discussion brought together three distinct perspectives on the evolving challenges of knowledge and education in the artificial intelligence era. Professor Debbie Prentice, Vice Chancellor of the University of Cambridge, served as moderator while also representing traditional academic institutions. Aidan Gomez, CEO of Cohere (which builds large language models for enterprise with a focus on security), and Hugo Sarazen, CEO of Udemy (an online learning platform with 250,000 courses, 80 million learners, 17,000 large enterprise clients, and 85,000 instructors), offered insights from the commercial education technology sector. Their conversation revealed both the transformative potential and fundamental risks that AI presents to how we learn, teach, and validate knowledge.

The Scarcity Paradox: What Becomes Rare When Information is Abundant

The discussion opened with an audience poll asking what becomes the scarcest resource when AI provides instant answers to virtually any query. The results showed critical thinking and sustained human attention as the primary concerns among participants. However, the panellists offered more nuanced perspectives on this fundamental question.

Hugo Sarazen framed the challenge through Herbert Simon’s observation that “when you have a wealth of information, you have a poverty of attention.” He noted that AI is essentially creating “millions and millions of polymaths” in every data centre, democratising access to knowledge while creating new forms of scarcity around human cognitive resources.

Aidan Gomez identified a particularly subtle risk: the false sense of mastery that AI can create. He argued that large language models can “fool you into thinking that you understand something when you don’t,” representing a core psychological risk as these systems become integrated into educational environments. This insight is particularly significant coming from someone who builds these systems, acknowledging their fundamental limitation in creating genuine understanding versus surface-level familiarity.

Debbie Prentice rejected all five options in the poll, instead emphasising the importance of self-knowledge for learners. She argued that much of what we learn comes from recognising what is difficult and compelling, but when AI makes everything appear easy, learners lose these crucial cues for self-understanding. This represents a meta-cognitive challenge that goes beyond simple knowledge acquisition to the fundamental ability to assess one’s own competence and interests.

The Transformation of Online Learning Through AI

Hugo Sarazen described a dramatic shift in his approach to education technology, revealing that he was “exiting online learning” when he joined Udemy and pivoting entirely toward AI-powered solutions. Drawing from conversations with CHROs and heads of learning and development, he identified critical shortcomings in traditional online education. While these platforms successfully addressed accessibility and cost barriers, they failed to solve fundamental problems around personalisation and return on investment measurement.

The current paradigm shift, according to Sarazen, involves moving toward AI platforms that can provide “in the flow of work learning” with adaptive, bite-sized content and measurable skill deployment. He referenced the “Bloom two-sigma problem” – research showing that one-on-one coaching produces dramatically better learning outcomes than classroom instruction, but at prohibitive economic cost. AI now makes this level of personalisation economically viable at scale.

Sarazen provided concrete examples of AI applications, including role-playing systems that allow sales professionals to practice with virtual customers or call centre agents to train on common scenarios. These applications demonstrate AI’s potential to provide safe, scalable environments for skill development through simulation and immediate feedback.

Aidan Gomez supported this vision while emphasising AI’s ability to tailor educational experiences to individual learning styles and preferences. Rather than generic offerings, AI can provide targeted, scalable approaches that maintain engagement through personalisation, representing a fundamental shift from teaching to the average student toward truly individualised instruction.

The Strategic Framework: Where Humans Maintain Competitive Advantage

Hugo Sarazen presented a business process framework for understanding human competitive advantage in an AI-dominated world, dividing cognitive work into three components: the front-end (problem formulation and asking the right questions), the middle (solution generation and processing), and the back-end (quality assurance and validation). His central thesis was that “the middle part, you don’t have a competitive advantage. You will be outgunned” by AI systems that are “polymaths by design.”

This framework provided a strategic roadmap for human adaptation, suggesting that individuals and institutions should focus their development efforts on critical thinking, question formulation, and quality assessment rather than competing with AI in information processing and solution generation. The implications for curriculum development are profound, suggesting a need to move away from information retention toward skills in problem identification and solution evaluation.

The Challenge of Testing and Assessment in an AI World

A significant portion of the discussion addressed the fundamental challenge of educational assessment when AI can complete many traditional academic tasks. Aidan Gomez emphasised the continued importance of testing human capabilities without AI assistance, arguing that while AI should be treated as a tool like a calculator, educational assessment must include rigorous evaluation of what humans can accomplish independently. This “gold standard test of what you have learned and retained” becomes increasingly critical as students can more easily navigate through educational systems with AI assistance.

However, the detection of AI-generated work proves particularly difficult. Gomez noted that many detection tools are “total scams” with high error rates, though he mentioned that technical solutions exist, such as embedding subtle linguistic markers in AI-generated text that would be invisible to users but detectable by institutions.

The conversation also highlighted a concerning generational gap: senior professionals can evaluate AI outputs based on their experience, while junior employees may lack the expertise to assess AI-generated work critically. This creates a potential training gap where the next generation of leaders may not develop the critical thinking skills necessary to manage AI systems effectively.

Trust, Authority, and the Problem of Explainability

A recurring theme was the erosion of traditional sources of authority and the challenge of maintaining trust in an AI-mediated knowledge environment. Hugo Sarazen highlighted that most large language models don’t explain where their answers come from, creating a fundamental problem for learning and decision-making. He described current models as essentially “huge matrices” with “weights assigned to different things” – statistical models rather than logical reasoning systems.

Aidan Gomez offered a more optimistic perspective, describing recent evolution toward “reasoning models” that engage in internal monologue before responding. However, he characterised these as still “primitive” and “a year old,” acknowledging significant limitations. He also mentioned techniques like retrieval-augmented generation (RAG) that allow models to cite specific sources, providing some degree of auditability.

Both speakers acknowledged that substantial investment in explainability research remains necessary, particularly for educational applications where understanding the reasoning process is often as important as the final answer.

The Future of Universities and Educational Institutions

The discussion touched on fundamental questions about the future of traditional educational institutions. Hugo Sarazen offered what he acknowledged as a “maybe controversial” analysis of higher education as a “convenient bundle” that combines learning, accreditation, rites of passage, and research functions. He questioned whether all these components need to remain packaged together given changing economics and AI capabilities.

Debbie Prentice defended the continued value of universities, arguing that they serve “so much more than provide knowledge.” She emphasised that universities teach fundamentally important skills like critical thinking at a crucial developmental moment in students’ lives, functions that may not be easily replicated by AI-powered alternatives. She noted the different pressures between for-profit and not-for-profit educational sectors in adapting to these changes.

The tension between these perspectives reflects broader questions about educational credentialing in an AI world. When AI can provide instant access to information and sophisticated analysis, the value proposition of traditional degrees becomes less clear, yet the social, developmental, and critical thinking functions of higher education may become more rather than less important.

Practical Implementation and Emerging Models

The panellists discussed various practical applications and emerging educational models. Hugo Sarazen mentioned the Alpha School as a “fascinating” example of new educational approaches, though he didn’t elaborate extensively on its specific features.

The conversation also addressed questions about AI in physical classrooms and drew parallels to Australia’s recent social media ban for under-16s, exploring how society might regulate AI access for educational purposes.

Audience members raised concerns about creating “applied knowledge delivered in the right way to the right people at the right time” for employability across all job categories, reflecting broader concerns about technological unemployment and the need for continuous reskilling as AI capabilities expand.

Unresolved Tensions and Future Directions

The discussion concluded with several unresolved tensions that reflect broader societal challenges in adapting to AI. The question of whether to test students with or without AI tools remains contentious, with valid arguments on both sides. Similarly, the appropriate balance between personalised AI tutoring and human instruction continues to evolve.

The conversation highlighted the need for continued research and development in several areas: better benchmarks for evaluating AI teaching effectiveness, improved explainability in AI systems, and more sophisticated methods for measuring learning outcomes. The development of specialised, trusted AI models trained on verified expertise rather than general internet data represents another important direction.

Perhaps most fundamentally, the discussion raised questions about maintaining human agency and critical thinking capabilities as AI systems become more sophisticated and ubiquitous. The risk that society might lose the ability to question and validate AI outputs represents a challenge that extends far beyond education to democratic governance and human autonomy.

The panellists agreed that rather than viewing AI as a replacement for human teachers, the future likely involves an augmentation model where AI enhances human capabilities in storytelling, motivation, and personalised instruction. However, significant work remains in developing the frameworks, policies, and practices necessary to realise this vision while preserving the essential human elements of education and learning.

Session transcript

Debbie Prentice

Good afternoon, everyone, and thank you for joining this town hall discussion where we will be talking about a topic that university and education leaders are all buzzing about, which is namely dilemmas around knowledge. This has been a topic for us since schools were first invented, libraries were first invented, and it’s still with us today. It’s extremely relevant today in an age in which AI is changing, making knowledge available broadly to everybody all the time. But it doesn’t mean that there aren’t still dilemmas around knowledge, and we’re going to probe these today. I’m Professor Debbie Prentice, and I’m the Vice Chancellor of the University of Cambridge. I’m very pleased to introduce you to our panelists for this session.

So we have Aidan Gomez, who is the co -founder and chief executive officer of Cohere, an enterprise AI company developing advanced language models for use by business. And we also welcome Hugo Sarazen, who is president and chairperson. Chief Executive Officer of Udemy, which provides a wide range of business and leadership development courses, including AI courses, to businesses and organizations around the world in fields such as financial services, higher education, government, manufacturing, and technology. We have some fascinating questions to discuss this afternoon around knowledge, misinformation, AI, attention spans, and even the nature of expertise. And we’re going to bring the audience in early and often, so I hope that you’ll all participate with us. We, as panelists, come from very different perspectives.

Aidan and Hugo run very successful businesses selling a product. They are from the for -profit educational technology sector, and I’m from the not -for -profit sector. So there are different pressures, different opportunities, different challenges that we face in this space. Before we get started with… Before we get started with our panel discussion, I’d like to remind the online audience that… If you are sharing with us through your social channels, you should use the hashtag, hashtag WEF26. And whether you’re joining online today or here in person, and it’s great to see so many of you here. Thank you so much for coming. Please feel free to get involved in the session by reacting to the questions we discuss in our conversation and also by submitting questions to panelists via the Slido app.

Okay? Okay, so our first question is, in a world of instant answers and AI assistance, what is becoming the scarcest resource? Okay, the answers are from a list of options. Is it sustained human attention, independent judgment and critical thinking, deep understanding and mastery, motivation to learn in the first place, or trust in what we know and who to believe? And actually I said or. That could be and. You can choose as many of these as you. as you want. Okay? So you can see on the screen, actually, as people are responding via the Slido app, but I want to ask our panelists, what would you say? So you can see the answers on the screen.

What would you say, Hugo?

Hugo Sarazen

Well, I think it’s a complicated question, and I think there’s a lot of all of the above. If you take a historical perspective, knowledge was scarce. That was a source of power. Our countries fought for that. And we also had experts that built knowledge over time, but very few polymath. Very few. Those ones that were were very, very, very important. Now today, you have LLMs that can learn everything, and they can learn across different domains, and they can become the polymath. So every data center, every time we say there’s a new infrastructure that’s being added, we’re adding millions and millions, millions of polymath. And that becomes a democratization. of that knowledge. The problem is, and there’s an amazing quote from Herbert Simon, when you have a wealth of information, you have a poverty of attention.

And I think that’s what’s happening for a lot of learners, and that’s why traditional methods need to change. And we’re going to come up and talk, I’m sure, about how learning needs to evolve, what the process, what’s the role of traditional institution in changing, what’s the role corporation need to, and what individual needs to do. So I think attention is one big component. The second is a lot of, when you go to LLM and AI and you ask for a question, it will give you an answer. It will feel very comfortable with that answer. It doesn’t explain. Explainability in AI is a whole field, a whole domain, and most of these LLMs don’t give you that.

So if you have a society that begins to rely on product, that give you an answer but don’t tell you where that answer is, answer came from how do you learn and what do you have in terms of trust so i think the trust piece is also equally important so i’ll stop at that we can go well further but

Aidan Gomez

yeah i was looking at the uh the poll up there and i for whatever reason the first one that came to me was deep mastery which seems to be the most unpopular choice so i think um you know when you exist in a world where it’s so fast and easy to get answers to whatever question you might have or to get a very surface level answer to even a complex question like whatever like how does quantum mechanics work it’ll give you a four paragraph response um but that’s not deep understanding of the subject matter and so i think llms can’t chat bots they can fool you into thinking that you understand something when you don’t and i view that as a core risk as we integrate these LLMs into an education environment, is this false sense of mastery or understanding.

We can discuss the different solutions to that. I think that testing is essential to it. The idea that you need to take away the tool and see what the human alone understands and has retained. The ability for you to assess depth has to take away those tools. I think that is, from my perspective, what’s most at risk.

Debbie Prentice

That’s interesting. My answer is a variant on yours. I, of course, wanted to reject all five. But I think it’s because of where I come from, coming from the university sector. I wanted to say self -knowledge for the learner. It’s part of what you’re saying. You don’t know if you’re mastered and you don’t know if you’re interested in it. You don’t know if you get it. It comes to you. So much of what you learn, so much of what you learn, what you learn comes from what is difficult and what is compelling. So for those cues to no longer be actually useful cues for self -understanding means how will you even know, but that’s my answer anyway.

So we can see what the – whoops, it went away. I think critical thinking was the one that won out at the end. It looked like critical thinking was actually the audience preferred. We can keep coming back to this, but I want to use this as a jumping – oh, there we go. Okay, yeah, critical thinking and then sustained attention. They were neck and neck for most of the time, yeah, and then trust and then deep mastery, right. That’s interesting. So I want to talk a little bit about each of what you do. So we can start with you, Hugo. Tell us about Udemy.

Hugo Sarazen

So Udemy is a 15 -year -old company that, at the time, did a – pretty cool thing around introducing online learning. It was a great innovation to change accessibility and the cost of reaching out to millions and millions of people and created a creator economy around that. So we now have 250 ,000 courses, 80 million learners on a regular basis. We serve 17 ,000 large enterprise. We have 85 ,000 instructors that kind of come to this marketplace to offer their wear. They’re very deeply committed. They know stuff and they want to share it to the world. And we do it in about 40 % of our revenues are in the U.S. The rest is around the world.

So we’re in tons of languages, 46 plus. And the funny story, I’ve only been in the world for less than a year. When I came in my first on -haul and the people who may be listening online who were working on this, they were like, oh, I’m going to do this. I’m going to do this. I’m going to do this. I’m going to do this. I’m going to do this. I’m going to do this. I’m going to do this. town hall, I came in and I said, we’re going to exit online learning. That is a wonderful innovation. It did a bunch of great things, but it doesn’t solve the problem of today. And with AI, we can do so many different things.

So I want to make a hard pivot of the business toward becoming an AI platform to reskill the workforce of the future. And we can talk about that. And I don’t want to take too much time, but there’s a lot of ways you can use AI to do some of the things you were suggesting to kind of help build the mastery, how to do assessment using AI, how to use AI role play to immerse people. And it also does the thing that I think is so, so important. Traditional online learning and actually traditional learning. You’re an instructor and you teach to the average, right? You create your curriculum and you think you’re going to hit like the most of the people.

You can’t get for the super fast. You can’t get for the super slow. You’re on online learning. And then different people have different starting points, and we don’t have an easy way to accommodate that. Now with AI, you can do a quick assessment. You can break apart the class. You can have feedback loop and reinforce that in a very, very powerful way. And I think that’s one of the things that’s going to emerge of using AI to kind of re -skill the workforce. It’s going to build on that previous generation of online learning to do something pretty remarkable and quite different moving forward.

Debbie Prentice

Thank you. Aidan?

Aidan Gomez

Yes, so Cohere builds large language models. So we’re one of the developers of this core piece of technology that powers things like ChatGPT and all these different applications. We’re focused purely on the enterprise side of house, and so we work with businesses to put those models to work inside the organization. We give them access to internal data and systems that the humans have access to. And then we teach or we work with our customer to teach. the workforce to shift their role from being the ones individually doing the work to managing a team of these models or agents to carry out that work. Our big differentiator is on the security side. So there’s no data exiting our customer’s perimeter.

Instead, we send all of our models and software to them, and they keep it self -contained. Yeah.

Debbie Prentice

So you have certain customers who will only subscribe to you, right?

Aidan Gomez

Yeah. Certainly critical industries, financial services, telco, healthcare, and then, of course, government applications as well. Anything that’s a national security concern, and arguably education is within that remit, that’s a place that we do extremely well.

Debbie Prentice

That’s interesting. So, Hugo, what can we learn from the arc of progress from MOOCs and… online education to now AI -driven?

Hugo Sarazen

I think a few things. The first one is, you know, if you look at the traditional learning processes and methods that we had, there was a void. And that’s why online learning took off and that’s why there’s a whole industry. And it addressed a bunch of problems around, you know, getting to skills, specific skills, and also getting to certification and then helping organization rescale. So that was a very, very, very powerful thing. What is now becoming a lot more a priority, and in the last six months, I spent an enormous amount of time, I spoke to 400 CHROs and head of learning and development in a large enterprise. So the pattern that I saw is they had an enormous proliferation of tools and things that were bought during the pandemic.

During the COVID era. very few could explain the ROI. How do you measure the ROI of learning? It’s a really good question. And everybody kind of defaulted to, did they take the class? Did they complete the class? Hours of learning. And as a business leader, it’s not particularly helpful. And it gets even worse. When they get certification in Google Cloud or AWS or Cyber something, to know that you’ve certified yourself two years ago, I’m a business leader. I want to know, are you current? Are you relevant today? So I think the arc now is moving in the enterprise to an ability to do in the flow of work learning, do it at bite size, do it in an adaptive way, and then we can come back to what adaptive means, and with an ROI, an ability to measure what skills people are deploying in real time.

So you’re now beginning to create a workforce management tool that is powered by an operating learning system.

Debbie Prentice

so Aidan you said that you said that you were not as worried about uh sustained human attention as you were some of the others how does Coherence solve the attention problem

Aidan Gomez

um well I mean I don’t know if Coherence solves the attention problem I think it it’s definitely a concern there’s lots of pressures on our attention span I think um social media short -form content is uh driving a lot of that um I’m certainly on the receiving end of that you know after 30 seconds because of TikTok my attention span ends and I need to talk about something else and also just the way that we do business now are in these short 30 minute meetings where you completely swap context and so I think those are difficult challenges not related to AI that are still applying pressure on human attention span um but it has a pretty good impact on the the pretty strong consequence on how people learn and how students can learn when they’re constantly being distracted when they to sit with material over time.

I think AI can perhaps assist in resolving that by its ability to personalize the experience to the individual and engage them more effectively. And so if you have a generic education offering, which, you know, bores some part of the population, excites the other, you’re missing, you’re underserving that population that gets bored. But if we can have a very targeted, scalable approach for each individual, giving them something that’s engaging, exciting, if they are auditory learners or visual learners, we can tailor it to them and hopefully keep their attention better than we might otherwise would. So AI might be part of the solution as opposed to the source of the problem.

Debbie Prentice

Yugo, does your vision of AI comport with that?

Hugo Sarazen

It completely matches. And I think, you know, there is a well -known piece of research from the 80s from a university, a Chicago professor. It’s the Bloom two -sigma problem. And they did some research where they looked at the ability to learn with one -on -one coaching. It was two -sigma higher than the classroom. But the economics of doing that was not there. That’s why we have these big classrooms, and that’s why there are bigger classrooms for first years. It doesn’t deliver the same learning experience. Now, to Aidan’s point, with AI, you can personalize the experience. You can adapt it, and you can create feedback loops that a professor cannot today. You’ve got 40 students.

You cannot pick up who’s not easily. Some teachers are amazing, and they have the ability to do incredible things. But now you have the ability to have that feedback. So I think we’re going to see a lot of AI expert tutors and coaches that will have context and that will have been trained. on a body of knowledge that is hopefully trusted, hopefully accurate, and will help in the way that you like to learn. So if you’re an auditory learner, we’re going to give it to you that way. And if you’re a visual, we’ll give it to you that way. I think that’s a really exciting and promising world we’re entering from that point of view.

So we’re going to go to questions from the audience in just a second. So start thinking about your question. I’m just going to ask one more question of our panelists myself, which is where do humans fit in in this brave new world of AI -based education? I think all of us who are educators know that at some point we need human intervention in the process, even with the most fabulous technology. Where do you think they need to come in?

Aidan Gomez

I think they’re the customer. So they’re the ones that we’re serving with this technology. And so we need to be able to serve them. We need to create. the best possible product for them. If we just do surface -level education that’s very confirmatory, oh yeah, you’ve got it, great, you know, a bit sycophantic, then they won’t be effective in the real world when they actually enter the job market. And so there’s a burden on us as product creators to create the most effective product to teach people skills and give them knowledge. And I think that AI is actually an incredibly effective tool towards that. But I do still believe that it’s a tool. It’s like a calculator.

It’s something that you can lean on to give you faster answers, more thorough answers. But we still need to ground ourselves in the human without the tool. And so testing becomes, it’s always been important, of course, but I think it becomes absolutely critical now because you can fake your way through an education system much more easily. And so having very strict testing regimens is going to be essential.

Hugo Sarazen

I have a variation on this I do think the teachers, the instructors are partly the customers but I do think they need to be in the loop they’re amazing storytellers they have a way if I ask anybody in this room who was your favorite teacher in high school and I pause for 5 seconds, there’s somebody in your mind right now what was special about that person and you cannot replicate that but you can augment that you can make that person now be able to maybe teach you on something that they were not like my favorite teacher in high school was a physics teacher I loved the way he presented, I loved the way he engaged and it was so motivating my chemistry teacher was not that but now I can augment with AI and have the voice, not just the voice but the way he thought, the way he presented the information apply to a different topic.

And I think that gets pretty exciting as well. You may finally understand chemistry. I may finally understand chemistry. I stayed away from chemistry because of that. But physics I love.

Debbie Prentice

Okay, I wanna open up to questions from the audience. So I will call on you the old fashioned way. If you raise your hand. Oh, you have to, sorry, you have to speak into my ear.

Audience

Anna Van Niels, director of the Livium Trust. I guess learning is a bit like working out it’s got to hurt to be effective. How do you think AI enabled tech of various kinds can help with that motivation issue? You’ve talked about the teacher being the one that absolutely the motivates, but a lot of the systems we’re talking about in the workplace, et cetera, you’re not gonna have that human in the loop. So can we do things with AI and tech that could prompt that?

Hugo Sarazen

Yeah, I’m gonna offer a few suggestions. And this is not like future, this exists today. So you can do AI role -playing and you can do AI role -playing. in a way that makes you go through the learning process. And I’m going to use a business example. So if you’re a new salesperson and you have a new product that you need to sell, you can load up the specs of that product into an AI role play and practice selling to a person. And there will be a rubric against which we’re going to score you. And we’re going to discover whether or not you are competent at selling this product that you’re responsible for. So that’s a business example.

I can do the same thing in a call center. You know, we have one of the largest call center outsourcers. There are 20 ,000 call center agents they need to onboard every month. That is incredibly complicated. But now you can load, you know, the most common error cause, the most common tickets, the product specs. And instead of taking three weeks to onboard somebody, through the process of learning, of experimenting, you can load up the specs of the product that you need to sell. You can do a role play and get to accelerate that learning by doing a lot of practice. So it’s simulation. So that’s one powerful example. I think the other one is AI can give you feedback and monitor the progress you’re making in a way that we can bring you back to that point in the gym where you’re struggling with whatever exercise you’re doing.

We’re going to make you do that exercise more and more and get that repetition in a way that reinforces the gap that you have.

Audience

Hi, I’m Nathaniel. I run an education company in Australia. Now, as a region, Australia has an interesting relationship with technology. As many of you may know, we’ve just recently had a social media ban for young people under 16. And in a similar vein, we don’t really have a good consensus around the role of AI. So my question is, what do you believe the role is for AI in physical classrooms? And what would you say to people who might be on the side of banning versus not banning it?

Aidan Gomez

Yeah, I think I’m interested to hear your answer. But from my side, I think it’s a tool like a calculator. I think also a duty of the education system now is to teach people how to use this AI, how to engage with it, how to most effectively use that tool. And so it certainly should exist as part of the classroom and as part of schooling. But like I said, it can become a crutch and it can be used to cheat. And so we have to come up with ways to ensure that students aren’t misusing it or using it in the ways that are unproductive to their learning. I’m excited to hear your answer.

Hugo Sarazen

I’ve got two -part answer. The first one is any business process or any endeavor, you have the problem statement asking the right question, you have the solving, and then you have the quality assurance in the back. It’s a feedback loop that you go through a circle all the time. And education is… No different. What AI does well is that middle part. It doesn’t do a whole lot in the front end and the back end. So what we need to teach young students and adults is how to ask the right question. The critical thinking, I love that it came out at the very top. Super, super important. But you can, as you said, the calculator is a calculator.

The fact that I can’t do multiplication table all the way to 100 is not that relevant for my day -to -day job. But the fact that I can be critical in my thinking, I can summarize, I can contextualize, I think those are the skills you want. Second part, for those who are curious, I have no relationship, but I am just fascinated. There’s a school in the U.S. called Alpha School. And they’ve got a really powerful model. They are using AI. They are encouraging students to use AI. And they are demonstrating that I’m going to get all the stats wrong, but they get two. the learning in half the time or three times the learning half the time and then the kids in the afternoon they go learn and learn how to be a civic leader or you know a leader in all sorts of other contexts instead of spending all their time where you know historically you would have learned you know various dates it’s not that relevant to know the dates of specific things but it’s relevant to understand the context of those events and I think that’s where we can focus a lot of the effort

Audience

Thank you Terrific topic to be discussed at Davos I’m Pranjal Sharma I’m from India I’m an author and analyst we’re looking at a lot of the micro pieces but I’d like to focus on the macro we have a situation today where we’re all skilled up but nowhere to go right last year I think ILO says 7 million fewer jobs were created not to mention the existing jobs that disappeared So there is a cry from the industry. Firstly, they don’t know who to hire and why to hire and what to hire, and they don’t even know what to test that credentials on. The second part is there’s a huge disconnect between what they want and what academia is offering.

Plus, the concept of a degree shouldn’t exist, and even continuous learning in terms of applied knowledge is missing. So I think the core phrase to be used here is applied knowledge. How do you create information for a person to be able to earn a livelihood, irrespective of white, gray, blue collar? And I think that’s the gap of applied knowledge delivered in the right way to the right people at the right time.

Aidan Gomez

From a labor market perspective, I think there’s a good case to be concerned about the impact of AI and what might happen, and reskilling is going to be an essential component of that. Thank you. The mismatch in the market between what education institutions are offering and what the market is demanding, I think that is a major issue that we need to figure out how to solve. I think AI can be a part of speeding up delivery of new programs and courses and keeping up with changes in demand much faster than we have in the past. The process of scaling up educational infrastructure to meet a shift in market demand has been historically extremely slow and laborious.

But with AI, we’re able to create programs much faster. The models are infinitely scalable. They’re always awake 24 -7. They never get annoyed at the student. So we have these incredibly compelling tutors to deploy at scale against the problem of teaching the population the skills that we need. But I think the issue might be in identifying the skills that we need, and that’s still going to have to come first. From us, the humans, the business leaders. the policymakers. So that might be the core constraint. We need a direction to be set against to start building the solution.

Debbie Prentice

I think too, I mean, what I would say is I think that, you know, universities aren’t teaching to what businesses need necessarily. We’re teaching things that we believe are fundamentally important, and I would defend that. I mean, we’re teaching critical thinking, and we’re teaching deep mastery, and we’re teaching them to people at a critical moment in their lives, most of them, where they actually really need to have a go and learn these skills. They may need additional skills when they go out into the workplace, and that, as far as I’m concerned, is what the kinds of products that you’re talking about are for. Good, thank you. Let’s go back to the critical thinking because now in the university the students widely use the AI assistance and get the instant answer.

In that case, how can we teach them to increase their capability of critical thinking to make factual check, logical check, scientific check, ethical check to the instant answer they got from models?

Hugo Sarazen

conclusion. The AI will outdo the human. So where we can be competitively differentiated versus the AI is in the front and in the back end. So we need to adapt the curriculum to make sure that people are asking the right questions with the right context. And it is critical thinking. It is critical thinking, but we need to expand and we need to have a better way to evaluate the level of critical thinking these students have when they hit the workforce so that you can evaluate. And then the same on assessing. I mean, AI is marvelous right now. It generates codes like there’s no tomorrow, but it’s mostly garbage. It is, you know, we have bottlenecks and quality assurance in the back end.

So how do you kind of create the new tools and you teach people to have, you know, the critical thinking to see if this is using the right library, is it using the right pattern? Is it using the right data? I think that’s one of the core, you know, change that. academic institution, organization like me, an individual need to do, as you do your self -development, you need to kind of really lean into this ability to ask the right question. Because the middle part, you don’t have a competitive advantage. You will be outgunned. And the thing that is even more crazy, historically, like people did PhD. I have a PhD. I went like super deep on one little topic and I got buried somewhere in the sinkhole.

And it took my entire body of effort to get there. And to be a polymath is very hard. To be able to understand, I know nothing about chemistry. I know nothing about biology, psychology. My dad did that. So I got something rubbed up on me, maybe. But AI is a polymath by design. It has the data set across all of that. So the middle part is a foregone conclusion, folks. You need to get, get good at the front and the back end.

Aidan Gomez

Yeah, I was going to say another thing, which is teaching is a skill in the same way coding is a skill or doing math is a skill. And so it’s a core capability that we as model developers need to invest in. And it’s not something that is easily benchmarked. And it’s not something that is accurately tracked at the moment. But I think the more this rolls out, I mean, it’s already in the hands of every student on the face of the planet. It’s going to become imperative that we’re able to track the performance of models in teaching tasks to ensure that they’re actually effective and improve that over time. That’s just so like a technical level that is not done presently.

I don’t know of a teaching benchmark, but I can point to probably 30 code ones, 50 math ones, you know, biology, et cetera.

Audience

All right. it happens from time to time I think that psychology is rubbing off well when you say AI is a polymath by design, it’s a brilliant thought you know, it was you articulated it very well which also means that by definition humans cannot compete so we basically have to end the session and say that doom is nigh

Hugo Sarazen

well, I don’t think so I mean, I’m more optimistic so the polymath thing is real I mean, if you do, again, historical perspective he who had Leonardo da Vinci on his team had an advantage to build a war machine or a better court or whatever now there’s going to be a similar debate, like who assembles these polymath AI thingy has an advantage that is a foregone conclusion, that’s why there’s all these battles for a But I think we cannot, as the human race, give up that ability to influence. I think that we made a point, I think you did at the very beginning. Like, these models typically are not designed, though some of them can be designed, to explain their reasoning.

So if as a society we begin to rely on this thing that is super facile, that gives us an answer, and we don’t have the questioning, and we don’t kind of do the checking and the validating, we lose agency on important decision. And I think that is one of the things that we need to focus on deeply as a society. It also leads to the guardrail, the ethical things, and all that other stuff. We need to go there, because in the middle, it’s going to come up with answers that will be amazing in biology, and will solve things in biology, because I got trained in English language, I don’t know. But it’s going to be pretty wild, but we cannot lose agency around this polymath.

I mean, every data center is going to have… hundreds of millions of polymaths in there.

Audience

yeah I just want to shed a thinking I believe there’s a type of paradox within companies about this critical thinking let me say it this way we senior professionals we know how to judge what the AI is doing so I ask them one day for the AI to model whatever and I could judge my juniors they were not able to judge because they don’t have the experience so but to some extent I could fire them because I don’t need them anymore because of these AI technologies but maybe there will be a gap so at some point in time AI can enhance a lot what I do but if you don’t train let’s say the new generation the junior who will be the future who will be in the future able to do this critical thinking on what AI is doing I don’t have the answers obviously companies need to take efficiency and we need to do our best to reduce cost whatever but I think it’s something we as a society will have to think a lot about

Debbie Prentice

it’s fair thank you here we’ve got one here you wanted you were up right yeah i didn’t just call call you

Audience

hi thank you for your insights i’m i’m kian i’m the CEO of an AI company called workera um i really like what you said even on um testing the human and i think in in the world of testing right now there’s almost two camps one that says you can test them with the calculator we can test them without the calculator and there’s also overlaid on top of it the risks of proctoring and understanding um who’s cheating who’s not cheating and what can you tell about it so how are you thinking about that idea of testing with or without the calculator

Aidan Gomez

yeah the uh the cheat like can you tell whether a piece of text was written by AI it’s really tough a lot of the detectors out there are total scams they’ll say 100 % AI even when it’s not used at all so they’re extremely overconfident very high error rate on both sides uh false positive false negative. And but the answer to that question is, you can. Like, you can insert into language models subtle cues to indicate for the reader, this was written by an AI. You can not sample from natural language, language that I’m drawing from right now. You can sample from a slightly shifted distribution and use certain words much more than any normal like any human would use.

And then as soon as those words appear, you have a good piece of evidence that this was written by a language model. And so us language modeling companies do that. We shift the distribution of the language model so that when its text gets read, we have some ability to say, you know, I can assign a likelihood that that was generated by my model. So you can detect that to some extent, but many of the tools are scams. And so I think we need to make better tools and put them in the hands of educators more readily. Thank you. On testing with and without the calculator, I have a pretty strong focus on without the calculator.

I think everything needs to be ripped away, and you, standing alone as yourself, need to prove your knowledge. That is like the gold standard test of what you have learned retained. But of course, like I was saying earlier, using the language model is a skill itself, and we should have space to test that, in which case, of course, you’re going to need the LLM in the loop.

Debbie Prentice

Let me seize the chair’s prerogative here to ask, because I’m curious what you would both say to this question. What happens in this brave new world of polymaths and not showing your work and not explaining your answer to expertise or authority? So, you know, we have at Cambridge, you know, library after library of big books that tell you the truth, or that was always the… the idea, right? You would go look it up somewhere. What do you do in a world in which looking it up is no longer… there’s not a dictionary, there’s not a truth?

Hugo Sarazen

I’ll start. I think most technology go back and forth. There’s a pendulum. We’re in the pendulum that bigger is better. We’re throwing everything under the sun. Every Reddit quote is now part of training every large language model. And that is good. It’s going to give you an average answer for average problem. Now, over time, I think we’re going to come back and say, you do need specialized, trusted, and we need to have confidence that we did use the right source. And I think there will be a space for that. At least I want to hope that that will be the case, that we’re going to come back and we’re going to have these specialized model that will not only be rag, but they are going to be defined from scratch with the right intent.

And they don’t need to be a zillion, trillion function points and whatever. I mean, they just need to be trained on the expertise. And then you do need to trust it. It’s going to be incredibly important. I think we also need a lot of research on explainability. And Ben Gio at the University of Montreal, one of the guys who got the Turin Awards, has been very vocal around this. We need to kind of go back and explain a lot more. These are statistical models. This is all this is. These are huge matrices, and they’re like weights assigned to different things. So this is not a piece of software where you say if, then, this, that.

This is just statistics. So it, on average, gives good answers. But it depends on the data. And you need to come back and put a bunch of tools to put the explainability into the model. And there are ways to do it. It’s not yet super advanced. And I think we need to invest in that so that we do have the confidence, build a trust. And I do think it’s part of the learning. The learning question you have. Because if the models are black box, you lose. the ability to learn from their deduction process, which doesn’t exist. It’s just a statistical model. There’s no deduction. So anyway, those are my two ideas.

Aidan Gomez

Yeah, over the course of last year, there was a paradigm shift in the type of model that gets to use now. We don’t just use input -output direct response models like you were alluding to. Every model now is a reasoning model. And so before it actually responds, it has an internal monologue where it thinks through the problem, tries to reason about it, and then delivers a response. It is primitive. It’s a year old, but it’s getting much better. And so I think exposing that to the user and showing these chains of thought, this reasoning is an important solution. And then like you say, RAG, which is retrieval augmented generation, where the model isn’t just drawing on its own knowledge, but it’s actually making direct and specific reference to external knowledge.

So we can plug it in into the Cambridge library. I went to Oxford, so the Bodley. And it can cite directly back from those sources. And that provides some degree of both reasoning and RAG provides some degree of auditability. So you can have a little bit more confidence in the response because you can check its work.

Debbie Prentice

Just out of curiosity, what’s driving that? What’s driving the need for reasoning?

Aidan Gomez

Because the models were brittle. They would very confidently answer with the wrong solution. And it turns out humans don’t put the same amount of energy into answering every question. But that was the prior expectation on these models. You would ask them, what’s 1 plus 1? And it would immediately respond and put the same amount of effort into answering that question. And you would ask it to prove some unsolved Erdos problem or something. And it would put the same amount of effort as 1 plus 1 into that. That was obvious. You know, there are some problems that we should spend. days, weeks, months, years, decades, putting effort in to solve, and there are others that can be responded to instantly.

It’s just a better, more robust intelligence.

Debbie Prentice

That’s fascinating. We have time for one more question. Anything pressing in there?

Audience

Thank you. Yeah, I’m very interested to ask the question of just circling back to the beginning where we said we have like public sector university as well as a technology, a tech platform being in the same room. The question I have on my mind is that with right now, like in the U.S. especially, education cost is so astronomically high and prohibitive. Lots of people are saying the narrative goes as like there’s no point going to university anymore. And I would see in that world, there would be a lot of attention turned to online education. I think we’re all very familiar with Udemy. What is the gaps between an online education and an accredited college or an elite college?

Has there ever been customer or market demand for online education to move towards a model or imitate a traditional college experience? Has that ever surfaced as a need? And just comparing the gaps there.

Hugo Sarazen

I’m going to say something maybe controversial, but it’s fun. The university degree is a bundle. It’s a convenient bundle that as a society we chose to create. So you learn something, you get an accreditation, and you get a degree. have a rite of passage. You know, these kids are at a moment, they leave home, they go, and they, and that bundle is a convenient, and we bundle that with research, because the same people could now pass on their knowledge to others. It is a convenient bundle as a society. It has worked well for, you know, a long time. Oxford and Cambridge are examples of long -standing institutions that had a version of this bundle. It changes over time.

Is it time to revisit whether all of these components need to fit together because of the economics and what AI can do to change the economic of delivery? Maybe. I think the second…

Debbie Prentice

Think it quickly.

Hugo Sarazen

Yeah, quickly. And the second piece is just the adaptability. If you have the labor market that moves so fast, you’re now going to begin to put more weight on the addressing a specific need for a specific skill. So I think that is a reality in addition to that potential unbundling of that whole experience.

Debbie Prentice

You have a good word for the university. I’m actually interested to hear from the university’s perspective. Then I’ll just end by saying I think that they are currently serving very different functions. Right now, university does so much more than provide knowledge that it still is worth its weight in gold, and it is gold. But we’ll see how the space develops. With that, I’m getting all kinds of signals from the producers, so we’ve got to end it. But thank you very much. Thank you for your questions, and thank you to our panelists. Thank you. To be continued… To be continued… Thank you. Thank you.

Hugo Sarazen

Speech speed

170 words per minute

Speech length

Future of traditional university degrees versus online/AI education

Topics

Social and economic development | Artificial intelligence

Aidan Gomez

Speech speed

166 words per minute

Speech length

AI as a catalyst for personalized, adaptive learning

Topics

Artificial intelligence | The digital economy

Debbie Prentice

Speech speed

124 words per minute

Speech length

Future of traditional university degrees versus online/AI education

Topics

Social and economic development | Capacity development

Audience

Speech speed

164 words per minute

Speech length

Future of traditional university degrees versus online/AI education

Topics

Social and economic development | Artificial intelligence

Agreements

Capacity development | Human rights and the ethical dimensions of the information society | Artificial intelligence

There is a significant skills gap between what educational institutions provide and what the job market needs

Speakers

– Hugo Sarazen
– Aidan Gomez
– Audience

Arguments

Traditional online learning addressed accessibility but failed to solve personalization and ROI measurement problems

There’s a major mismatch between what educational institutions offer and what the job market demands

Applied knowledge delivered at the right time to the right people is crucial for employability across all job types

Summary

All speakers acknowledge a disconnect between traditional education offerings and actual workforce needs, with emphasis on the need for more practical, applied knowledge and better measurement of learning outcomes.

Topics

Social and economic development | Capacity development | The digital economy

Trust and explainability in AI systems are crucial concerns that need addressing

Speakers

– Hugo Sarazen
– Aidan Gomez

Arguments

Trust is equally important since LLMs give answers without explaining their sources

Reasoning models and retrieval-augmented generation provide auditability by showing chains of thought and citing sources

Summary

Both speakers recognize the critical importance of making AI systems more transparent and trustworthy through better explainability and source attribution, though they approach solutions differently.

Topics

Artificial intelligence | Building confidence and security in the use of ICTs | Data governance

Capacity development | Human rights and the ethical dimensions of the information society | Artificial intelligence

Unexpected consensus

Arguments

Sustained human attention is becoming scarce due to information wealth creating poverty of attention

Deep understanding and mastery are at risk as LLMs can fool people into thinking they understand when they don’t

Self-knowledge for learners is critical as they lose cues about what is difficult and compelling

Summary

The speakers identified different primary concerns: Hugo focused on attention scarcity due to information overload, Aidan emphasized the risk of false mastery, and Debbie highlighted the loss of self-awareness in learning

Topics

Artificial intelligence | Capacity development | Human rights and the ethical dimensions of the information society

Testing approach with or without AI tools

Speakers

– Aidan Gomez
– Hugo Sarazen

Arguments

AI should be treated as a tool like a calculator, but testing without the tool is essential to assess true human understanding

AI role-playing and simulation can accelerate learning through practice and immediate feedback

Summary

Aidan strongly advocates for testing without AI tools to assess true human knowledge retention, while Hugo emphasizes using AI for enhanced learning through simulation and role-playing during the learning process

Topics

Artificial intelligence | Capacity development | Social and economic development

University education versus market-driven skills training

Speakers

– Debbie Prentice
– Hugo Sarazen
– Audience

Arguments

Universities teach fundamentally important skills like critical thinking rather than just business-specific needs

Traditional online learning addressed accessibility but failed to solve personalization and ROI measurement problems

Applied knowledge delivered at the right time to the right people is crucial for employability across all job types

Summary

Debbie defends universities’ focus on fundamental skills like critical thinking, while Hugo and audience members emphasize the need for practical, market-relevant skills with measurable ROI

Topics

Social and economic development | Capacity development | The digital economy

Unexpected differences

The role of human teachers and instructors in AI-powered education

Speakers

– Aidan Gomez
– Hugo Sarazen

Arguments

AI should be treated as a tool like a calculator, but testing without the tool is essential to assess true human understanding

AI can personalize learning experiences and create feedback loops that traditional teaching cannot achieve at scale

Explanation

While both speakers are from the AI/tech industry, they had different views on human involvement – Aidan sees humans primarily as customers to be served by AI tools, while Hugo sees teachers as essential partners who can be augmented by AI to extend their unique storytelling and engagement abilities

Topics

Artificial intelligence | Capacity development | Social and economic development

The future of traditional educational institutions

Speakers

– Debbie Prentice
– Hugo Sarazen

Arguments

Universities serve broader functions beyond knowledge transfer and remain valuable despite AI advances

Traditional online learning addressed accessibility but failed to solve personalization and ROI measurement problems

Explanation

Despite coming from different sectors (academic vs. business), there was less conflict than expected, with Hugo actually acknowledging the value of the university ‘bundle’ while suggesting it might need unbundling, and Debbie recognizing that additional workplace skills can come from other platforms

Topics

Social and economic development | Capacity development | The digital economy

Overall assessment

Summary

The main disagreements centered on educational priorities (fundamental vs. applied skills), assessment methods (with vs. without AI tools), and the primary risks of AI in education (attention, mastery, or self-knowledge). Despite different sectoral backgrounds, speakers showed surprising alignment on the need for critical thinking and human agency.

Disagreement level

Moderate disagreement with significant areas of convergence. The disagreements reflect different priorities and perspectives rather than fundamental incompatibilities, suggesting potential for collaborative solutions that address multiple concerns simultaneously.

Partial agreements

All speakers agree that critical thinking and human judgment are essential, but they disagree on implementation – Hugo focuses on front-end questioning and back-end quality assurance, Aidan emphasizes strict testing without AI tools, and Debbie advocates for curriculum adaptation in universities

Speakers

– Hugo Sarazen
– Aidan Gomez
– Debbie Prentice

Arguments

Humans must focus on asking the right questions and quality assurance since AI will dominate the middle execution phase

Testing and assessment become absolutely critical as students can more easily fake their way through education

Universities must adapt curricula to emphasize critical thinking and evaluation skills over information retention

Topics

Artificial intelligence | Capacity development | Human rights and the ethical dimensions of the information society

Both agree on AI’s potential for personalized education, but Hugo emphasizes practical business applications and ROI measurement, while Aidan focuses on maintaining educational integrity through proper assessment

Speakers

– Hugo Sarazen
– Aidan Gomez

Arguments

AI can personalize learning experiences and create feedback loops that traditional teaching cannot achieve at scale

The shift moves from generic education offerings to highly targeted, scalable approaches for individuals

Topics

Artificial intelligence | Capacity development | Social and economic development

Both recognize the need for more trustworthy and explainable AI systems, but Hugo emphasizes the need for specialized models trained on trusted sources, while Aidan focuses on technical solutions like reasoning models and RAG for auditability

Speakers

– Hugo Sarazen
– Aidan Gomez

Arguments

There will be a pendulum swing back toward specialized, trusted models trained on verified expertise rather than general internet data

Reasoning models and retrieval-augmented generation provide auditability by showing chains of thought and citing sources

Topics

Artificial intelligence | Data governance | Building confidence and security in the use of ICTs

Capacity development | Human rights and the ethical dimensions of the information society | Artificial intelligence

Takeaways

Key takeaways

In an AI-driven world, critical thinking and sustained human attention are becoming the scarcest resources, with deep mastery at risk as AI can create false confidence in understanding

AI should be treated as a tool like a calculator, but testing without AI assistance is essential to assess true human learning and retention

The future of education lies in AI-powered personalization that can provide one-on-one tutoring experiences at scale, addressing the Bloom two-sigma problem economically

Humans must focus on the ‘front-end’ (asking right questions, critical thinking) and ‘back-end’ (quality assurance, validation) of problem-solving, as AI will dominate the middle execution phase

There’s a fundamental mismatch between what educational institutions offer and what the job market demands, requiring faster adaptation and applied knowledge delivery

AI models are evolving from simple input-output systems to reasoning models that show their thought processes, improving auditability and trust

Traditional university education serves broader functions beyond knowledge transfer (socialization, critical thinking development, research) that remain valuable despite AI advances

Society risks losing agency over important decisions if we become overly reliant on AI without maintaining questioning and validation capabilities

Resolutions and action items

Educational institutions need to adapt curricula to emphasize critical thinking, question-asking, and evaluation skills over information retention

Better tools for detecting AI-generated content need to be developed and made available to educators

Investment in AI explainability research is needed to build trust and enable learning from AI reasoning processes

Testing regimens must become more rigorous and include both with-AI and without-AI assessments

Development of specialized, trusted AI models trained on verified expertise rather than general internet data

Creation of better benchmarks for measuring AI teaching effectiveness, similar to existing coding and math benchmarks

Unresolved issues

How to measure return on investment (ROI) for learning and skill development beyond completion rates and hours spent

The generational gap where senior professionals can judge AI output while juniors cannot, potentially creating a future shortage of critical thinkers

Whether the traditional university ‘bundle’ (learning, accreditation, socialization, research) should be unbundled given economic pressures and AI capabilities

How to maintain human expertise and authority in a world where traditional sources of truth (libraries, dictionaries) are being replaced by AI systems

The challenge of keeping educational content current when skills and market demands change rapidly

How to ensure AI systems provide trustworthy, explainable answers rather than statistically average responses

Suggested compromises

Treating AI as an augmentation tool for teachers rather than a replacement, allowing favorite teaching styles to be applied across different subjects

Implementing a hybrid approach where AI handles personalization and feedback while humans provide motivation, storytelling, and critical oversight

Using AI for specialized skill training and simulation while maintaining traditional education for fundamental critical thinking and socialization

This provides a universal framework for understanding AI’s role across all domains, not just education. It’s insightful because it clearly delineates where humans remain essential while acknowledging AI’s strengths.

Impact

This comment provided practical guidance for educators and students about where to focus their development efforts. It answered the audience’s concerns about human relevance and gave concrete direction for curriculum development and skill building.

Overall assessment

Explanation

Hugo emphasized the critical importance of ensuring humans don’t lose the ability to influence important decisions as AI systems become more sophisticated and widely adopted.

Disclaimer: This is not an official session record. DiploAI generates these resources from audiovisual recordings, and they are presented as-is, including potential errors. Due to logistical challenges, such as discrepancies in audio/video or transcripts, names may be misspelled. We strive for accuracy to the best of our ability.