By Wilson Wong, The Chinese University of Hong Kong, Hong Kong.
A wake-up call in AI education
When my colleagues and I began researching the impact of generative AI (GAI) on higher education, I expected to see a digital revolution underway, including revamped core curricula, flourishing ethics courses and creative problem-solving at the heart of learning. However, in our recently published study “The future of learning or the future of dividing? Exploring the impact of general artificial intelligence on higher education” (with A. Aristidou & K. Scheuermann) in Data & Policy, what we found was far more sobering. Across Asia’s top universities, which are institutions often praised as leaders in innovation in teaching and learning, the integration of GAI is selective, inconsistent and, in many cases, surprisingly superficial.
Yes, AI is entering classrooms. Yes, students are using tools like ChatGPT. And yes, policy conversations are happening. But the transformation we need has not yet taken root.
The uneven landscape of GAI in higher education
In our study, we analysed GAI-related policies and undergraduate curricula from the 25 top-ranked universities in Asia in QS and THE. What we found was a fragmented picture: only 48% of these institutions had any explicit GAI policies. And when we dug into the core curricula, the mandatory courses all students must take, we found just one university offering a well-rounded foundation that included technical literacy, human–AI collaboration, creativity and ethical reasoning.
Most universities are still treating AI education as a narrow technical issue, an elective here, a coding module there. But this approach misses the point. GAI is not just another digital tool. It is a paradigm shift that is reshaping the future of work, the nature of learning, and the very essence of what it means to be skilled in the 21st century.
The blind spot: human distinctive capacities
Here is the crux of the matter: AI is already better than us at doing “AI things”. If the goal of AI education is to produce graduates who can write code or prompt ChatGPT fluently, we are setting a very low bar – one that AI itself can already clear. What universities should be asking is: what can humans do that AI can’t? And how can we help students cultivate those qualities?
This is the blind spot in current AI education. While technical knowledge is useful, it is not sufficient. The real value lies in human-distinctive capacities – abilities that make us irreplaceable in an AI-driven world: creative thinking, critical analysis, emotional intelligence, ethical judgment and the capacity to collaborate with intelligent systems in meaningful ways. These are not just “soft skills”; they are survival skills in the age of GAI.
Rethinking what it means to be “AI-Ready”
We are not suggesting abandoning technical training. Of course, students need to understand how AI works. But that knowledge must be embedded within a broader, interdisciplinary curriculum that treats AI not just as a tool but as a societal force. Students need to learn how to:
- Work with AI rather than being replaced by it.
- Analyse the biases and ethical dilemmas embedded in AI systems.
- Lead teams and make decisions in AI-augmented environments.
- Communicate ideas, solve problems and design solutions that machines cannot.
This means rethinking our teaching methods. Group projects, simulations, debates and collaborative storytelling are just a few ways we can create learning environments where human creativity and AI capabilities complement each other.
The risk of a divided future
Furthermore, if we do not act fast enough, we risk creating a future of learning that mirrors existing social divides – where only elite institutions equip students with the full spectrum of AI-era skills while others fall behind. The universities we studied vary widely in their readiness for the GAI era. Some are experimenting with innovative policies. Others have not even begun the conversation. Without coordinated efforts and shared frameworks, we will see increasing divergence, not convergence, in the quality of AI education.
So where do we go from here? We need a new vision for AI education, one that puts human agency at its centre. This means:
- Institutional frameworks for responsible GAI use in teaching and research.
- Mandatory curriculum reforms that go beyond coding to include ethics, creativity and human-AI collaboration.
- Faculty development to empower educators to integrate GAI meaningfully.
- Cross-disciplinary and international collaboration for sharing and scaling up best practices.
Most importantly, we need to recognise that preparing students for an AI future is not about teaching them to be machines; it is about helping them be more human. If we want students to thrive in the GAI era, we must stop treating AI education as a technical checkbox and start nurturing the skills and values that make us uniquely human.
Wilson Wong, The Chinese University of Hong Kong, Hong Kong.
