Don't panic about AI. Instead, learn fast

Dr. Michael Fung
Executive Director, Institute for the Future of Education (IFE)
Tecnológico de Monterrey, Mexico (Tec)

Dr. Michael Fung says universities should avoid both fear and hype as they experiment with AI.

Gavin Allen: Are we in another era of “move fast and break things"?

Dr. Michael Fung: Move fast, yes. I don't know about breaking things. Education has provided a pathway towards social mobility, knowledge advancement, solving societal problems and improving livelihoods. That role remains. But changing needs challenge universities to reinvent, re-imagine, and transform themselves.

Education needs to broaden beyond the traditional university model. Learning must become lifelong, more flexible, more focused on human and interdisciplinary skills, and more connected to the workplace. Universities also need deeper partnerships with industry, not just for funding, but to help people keep learning throughout their lives. That is our mission at IFE: to drive educational innovation and transformation to benefit diverse communities worldwide.

Gavin Allen: Why would industry want to participate at a deeper level?

Dr. Michael Fung: One motivation is to ensure they find the right talents to power their growth. Enterprises are also generators of new knowledge – look at where leading-edge AI advances are being created – so companies have a moral responsibility to help develop the human capabilities they ultimately benefit from. A rising tide lifts all boats.

Gavin Allen: Is there a danger we move from the human joy of learning to gearing it purely to jobs?

Dr. Michael Fung: Universities shouldn’t abandon nurturing the whole person, providing the broader human skills for resilience in the face of change. But industry's needs have changed. Business cycles are shorter and fewer employers are willing to spend long periods training new employees. They want the right fit from the start and can pick from a significantly expanded cohort of higher education graduates. Our graduates need to be ready both for the job, but also to live a fulfilling and productive life. Hence the need to focus on both technical competence, as well as skills such as teamwork, communication, and adaptability. Universities must help spark the curiosity that instills a culture of lifelong learning, nurturing the self-agency to find solutions to the problems they face.

Gavin Allen: Is self-agency the new model for education?

Dr. Michael Fung: Traditional education was essentially the “sage on the stage.” That may work for very young students, but it absolutely does not work for lifelong learners. It needs to shift to a more active, engaged, challenge-based model of learning: not just case studies from the past, but challenges relevant to companies right now.

At Tecnológico de Monterrey (Tec), we’ve moved to a competency-based education model. We work with more than 2,500 industry partners to embed real-world challenges into courses across different disciplines. Students solve these challenges and present their work to employers, gaining practical skills and feedback in the process.

The transformation has been complex, but the results have been positive and we continue to refine the model. Student transcripts now include not only courses and grades, but also demonstrated competencies, helping employers identify suitable candidates and allowing us to adapt the curriculum as industry needs evolve.

Gavin Allen: What's the role of technology, not just for students but for the faculty and organization too?

Dr. Michael Fung: Technology is an important enabler and AI can make certain tasks more efficient. When GenAI exploded in 2022, we decided not to ban it, but to embrace it. We convened a network of about 50 universities across the Latin American region, called the AI Global Education Network, or AIGEN. It’s conducting joint experimentation around the use of AI and publishing the results. We have 15 experiments going on now, implementing similar interventions in different classrooms across the region, and developing methodologies to measure impact.

Tec runs about 60 classroom experiments every year, to study issues such as the merits of an AI support chatbot for students. We recently surveyed 30,000 university students and faculty across Latin America and found that students are happy to use AI, but much less favorable towards having the faculty use it. But there are already high levels of AI use amongst both students and faculty in Latin America.

Gavin Allen: When considering AI, do universities need to give greater thought to the “why are we doing this?” question?

Dr. Michael Fung: “Why are we doing this?” and “How effective is it?” are both key questions. Strategy is only as good as the execution. One of the promises of AI is to create personalized learning experiences, but we need to unpack what the most effective provision of that looks like.

At Tec, we created an adaptive learning experiment with 3,000 of our 90,000 students and 80 professors across a number of courses. We wanted to see how technology can recommend learning pathways and resources to students that lead to the best learning gains. So, we created a technology option (with coaching and learning pathways recommended only by technology). We also created an instructor-only option, and an instructor-plus-technology hybrid mode.

The hybrid model produced the strongest learning gains, outperforming the instructor-only control group by about 35%, while the technology-only model lagged behind both. This year, we’ll expand the experiment to 12,000 students.

Taking such an evidence-based approach is vital to helping us better understand what drives actual learning outcomes when designing educational interventions.

Gavin Allen: Are you confident that “tech-plus-human” will always be better than any other combination or solo effort?

Dr. Michael Fung: Tech could win out if it improves by leaps and bounds and the human element becomes marginal. But learner motivation is critical. One of the reasons flipped classrooms often failed (students read the content before class) is that students didn't do the assigned reading. There’s still a way to go before technology instills motivation in learners. You can gamify the experience, but it's a very human process. A talented instructor looking students in the eye, making a passionate and engaging case, or reinforcing through praise, is much more likely to inspire motivation than AI.

Gavin Allen: So, as a leader used to technological change, do you not see AI as that much of a crossroads?

Dr. Michael Fung: There's a lot of buzz around AI – embrace it, throw everything else away, fire 50% of staff – and I think we need a dose of reality. We are at the peak of the AI expectation-hype cycle; what we need is not frenzied panic, what we need is solid evidence. In 2012, when MOOCs were rising, it was predicted that universities would no longer exist. But we’re still here and we're still adapting. We need a level-headed view: not saying that AI is not important, but also not over-hyping it. Take an evidence-based design approach.

Gavin Allen: Should AI courses be compulsory, or does that strip out the learner agency?

Dr. Michael Fung: I put AI in the same category as digital literacy. We live in a technology-enabled world where everyone needs a basic understanding of digital tools, and AI is increasingly part of that. Universities should teach AI not just to appear forward-thinking, but because these technologies are becoming part of everyday life. That responsibility also extends beyond traditional students. Universities have a broader role in helping society understand and adapt to these changes.

Gavin Allen: How do universities move beyond strategy and actually embed AI throughout their organizations?

Dr. Michael Fung: This cuts to the heart of driving change within universities. You frequently hear that managing faculty in universities is like herding cats: it’s very challenging. There are well-tested principles of change management – creating a strong, compelling vision for change, communicating and engaging actively with all stakeholders, establishing early wins, and so on – but it's surprising how few institutions embark on rigorous change management practices. They meet resistance and back off, so the changes don’t stick.

AI will go through the same thing. For example, if we want to use AI to help with assessments, it requires clear articulation: Why are we doing this? Where's the road map? What are your concerns? How do we address them? There's no shortcut – it’s a human process that requires you to really roll up your sleeves.

Gavin Allen: What’s your advice to universities still at an early stage of AI adoption?

Dr. Michael Fung: The “why” is clear: student-centered personalized learning, better employment prospects, active citizens, and so on. But there are two things for aspiring universities to think about. First, how can you start small, learn fast and scale what works – and not try to boil the ocean? Second, move toward more agile governance models. For instance, a typical curriculum refresh cycle has countless levels of well-intentioned debate and approval. We need to evolve to be much more agile, to respond to what works and embed it.

At IFE, we've created a framework that helps universities assess how prepared they are for future challenges and plan their next steps. The framework helps each institution create a practical roadmap that fits its own circumstances. We've found that change works best when universities view it as a long-term process of building skills, systems, and organizational capabilities, not as a one-time technology purchase or implementation project.

Gavin Allen: Isn’t that “move fast and break things”?

Dr. Michael Fung: I avoid the phrase “break things” because it invites resistance. But learn fast, be willing to change, and yes, there is that element of rebuilding. We don't need to panic, which drives hopelessness. But we do need to get off our rear ends and move fast and change things based on evidence. We have to go from the narrow city streets of traditional academic pathways to the wide highways of learning throughout life.