The questions shaping the next generation of learning.
What should become easier and what should remain deeply human?
AI is changing what people can produce, access, analyze, and automate. The more interesting question for learning is not simply how we use the technology, but what becomes more important when the technology can do more.
I am interested in learning systems that use AI to expand human capability rather than reduce human participation. AI can help people generate possibilities, access information, receive feedback, personalize practice, and move from idea to prototype faster than ever before. But speed is not the same as learning. As AI becomes more capable, learning environments will need to become even more intentional about developing the things that remain deeply human:
The design challenge becomes: what should we allow technology to make easier so people have more capacity for the thinking that matters most?
Designing learning for a world that does not arrive in departments.
Real problems rarely introduce themselves as math problems, science problems, design problems, or business problems. They arrive messy, interconnected, and incomplete. Our learning systems have traditionally separated knowledge because it makes teaching easier to organize. But the world asks people to recombine it.
I am increasingly interested in learning experiences built around questions, challenges, and real-world contexts that require learners to pull knowledge from multiple disciplines, collaborate with people who think differently, and make decisions without knowing the answer in advance. That does not mean disciplines disappear — it means learners understand why and when to use them.
The future-ready learner is not simply someone who knows more. It is someone who can ask:
Helping people see where learning can take them.
One of the most important questions in learning is not only “What are you learning?” but “What can this help you become able to do?” Learners need opportunities to see how knowledge connects to people, professions, industries, communities, and real problems. That means bringing the outside world into learning earlier and more often:
I am interested in learning systems that help people build both capability and visibility — not only developing new skills, but being able to demonstrate what they know and imagine where those skills might lead. Career-connected learning should not narrow possibility too early. Done well, it does the opposite: it helps learners discover possibilities they did not know existed.