At the Rutgers Active Learning Symposium held on May 21st, 2026, Barbara Oakley delivered the keynote presentation titled, "Building Minds in the Age of AI." I had come across her work before, but the keynote was a great refresher, and I wanted to talk about a few things that stuck out to me.
Focused mode and diffuse mode
Arguably the best-known concept from Oakley’s work is how the brain has different ways of operating – a focused mode and a diffuse mode.
Focused mode is what it sounds like - and what we, as educators, usually think of when we think of learning – concentrated attention on the problem in front of you.
Diffuse mode is the looser, more relaxed background state your brain drifts into -- usually when you're doing something else.
Oakley talks about how hard problems often need both modes of thinking. She mentioned how focused mode is great for working with material you understand, but it can trap you by guiding you down the same familiar path and missing new potential paths. Diffuse mode, on the other hand, lets your brain make connections across seemingly distant ideas.
We've all gotten stuck, walked away in frustration, and suddenly had an answer surface while we were brushing our teeth or showering, and is often referred to as “shower thoughts.”
That's not a coincidence; that's diffuse mode doing its thing.
The practical takeaway for our students, and for us, is that stepping away from a problem is often part of solving it.
Helping your students make the connections needed for efficient learning
Oakley opened the second half of the keynote with a quiz: which of these helps students learn most efficiently – rereading, highlighting, retrieval practice, or making a concept map?
The answer is retrieval practice – trying to recall the material from memory.
This active recall strengthens the connections in your brain more than rereading or other methods of study.
We can help this retrieval practice with low-stakes quizzes and in-class activities. These work best when spaced out over time, allowing the brain to revisit recently acquired information over an extended period. An end-of-class quiz won’t work as well, for retrieval practice, as one administered the next week.
Creating quizzes that revisit topics from previous weeks or lessons will help reinforce these connections.
Other ways to engage both modes of thinking in a course
You can introduce harder or larger topics before diving into the details, letting students wonder how something might be connected and let those connections build as the material is explored.
In a course that meets twice a week, you might introduce a tough concept on the first day and revisit on the second, or in the case of a flipped classroom, invite your students to think about something before you meet for class, perhaps even asking them to write down a hypothesis.
You can also use low or no-stake quizzes that can be repeated to allow students to practice recalling information without the all-or-nothing stakes of a typical exam, combining this with using a question pool that pulls from previous week’s content.
Does how we learn still matter in the age of AI?
Yes! Building these links still matters – maybe more than ever!
As Oakley said in her presentation:
Without links in your own brain, you can’t think critically about Gen AI!
Oakley isn't anti-AI – she believes it can be a very useful study partner – but she does worry about people offloading cognitive work to AI. To evaluate, question, or build on an AI's answer, you need your own internal web of connections what she calls building the links.
She framed knowledge as something the brain physically constructs. A literal web of neural connections created through practice; in much the same way you would build any other muscle.
AI can be a useful tool for study – generating practice questions, explaining a concept through a fresh metaphor, or playing Socratic foil, but it works best when it builds on top of real knowledge, not as a substitute for that knowledge.
Without building these connections, AI users have no way to know if the information they're being presented is based on facts, conjecture, or pure imagination. Is the information being fed to you based on sound principles or is there a bias, possibly one placed there on purpose by the AI's programmers, that is misleading you?
If you would like to learn more, Barbara Oakley’s online course, Learning How to Learn is free to audit on Coursera, and her book, A Mind for Numbers, covers the same ground. Both are aimed at learners, but there is a lot for those of us on the teaching side too – it's hard to design good learning experiences without a clear picture of how minds work.