Why Learning to Learn Matters More Than Ever in the Age of AI
At a lifelong learning festival hosted by the National University of Singapore (NUS), speakers argued that as AI takes over routine tasks, human judgement, curiosity and the habit of continuous learning matter more. The ideas are reasonable, but they come from an event organized by a university, so they are opinions and not evidence about how work will actually change.
In short: At a lifelong learning festival hosted by the National University of Singapore (NUS), speakers argued that as AI takes over routine tasks, human judgement, curiosity and the habit of continuous learning matter more. The ideas are reasonable, but they come from an event organized by a university, so they are opinions and not evidence about how work will actually change.
If AI can handle the routine work faster than we can, what is left for people to be good at? That question ran through a recent festival of educators and industry leaders.
What happened, in plain words
At the NUS Lifelong Learning Festival 2026, speakers discussed how AI is reshaping work and education. A common thread was that AI raises the baseline of what beginners can do, which makes human qualities such as critical thinking, sound judgement and a willingness to keep learning more valuable. The speakers also argued that universities and workplaces need to adapt to help people build new skills and rethink their jobs.
Key points
- Learning to learn: NUS President Tan Eng Chye said AI can supply answers, but human learning involves asking questions, making mistakes and trying again.
- Redesigning work: Professor Tulika Mitra noted that AI makes knowledge and coding more accessible. She said this should push people to design new workflows rather than simply speed up old tasks.
- Human accountability: Professor Simon Chesterman compared generative AI to a smart intern who needs supervision. Humans, he said, must stay responsible for the results.
- Shifting careers: Aileen Tan suggested workers may need to pivot into different fields several times instead of following one straight career path.
Terms explained
- Generative AI — Technology that creates new content such as text, code or images on request. Example: A tool that drafts an email when you type a prompt.
- Large language models — Systems trained on huge amounts of text so they can understand and produce human-like language. Example: A chatbot that answers questions in fluent conversation.
- No-code programming — Giving instructions to a computer or robot through simple visual steps or plain language instead of traditional code. Example: Telling a device what to do in everyday words rather than in a programming language.
Critical analysis
What is encouraging. The emphasis on accountability is the strongest point. Treating AI as an intern whose work must be checked is a practical framing, and it keeps responsibility with people. The call to redesign workflows, not just automate old ones, also reflects how technology tends to pay off in practice.
What deserves caution:
- A festival is not a study. These are views from invited speakers, not research findings. Nothing here shows that learning-to-learn skills protect workers from disruption.
- Institutional interest. A university’s lifelong learning event has a natural incentive to conclude that more education is the answer. That doesn’t make it wrong, but it is worth keeping in mind.
- “Raising the baseline” cuts both ways. If AI lets novices do more, it can also make entry-level skills less scarce. That may squeeze junior roles, which are where people usually learn the craft. The festival framing leans on the upside.
- Who gets to adapt?. Advice to pivot careers several times assumes time, money and access to retraining. Workers with fewer resources may find this much harder, and the source does not address that.
- Vague on the “human edge.”. Critical thinking and judgement are hard to measure and teach, and AI keeps improving at tasks once thought uniquely human. The argument may need updating as capabilities change.
Bottom line: Thoughtful, widely shared advice, but it is a set of opinions with little data behind it. Readers should treat it as a perspective, not a forecast.
What we still don't know
How different industries will actually change, which skills will prove most durable, and whether the proposed education and workplace reforms will help workers in practice. The source reports festival discussions and offers no data or guarantees about career outcomes.
Based on reporting from National University of Singapore. This is an independent explainer, written in our own words with AI assistance; National University of Singapore has not reviewed or endorsed it. Read the original for the full details.