Learning in the age of AI: A teaching event that met the moment
30 April 2025Image credit: Barbara Oakley
International expert Barbara Oakley, with education researcher Michael Johnston, led a sold-out series that revealed just how much there is still to learn—about learning itself.
During the mid-semester break in April 2025, the University welcomed world-leading learning science expert, Professor Barbara Oakley, and New Zealand education researcher Dr Michael Johnston for a sold-out in-person masterclass, followed by a three-part online workshop series exploring how we learn and how generative AI can support university teaching.
This event series was organised by Ranga Auaha Ako | Learning and Teaching Design as part of the Professional Learning Series for teachers.
“The session I attended yesterday, presented by Barbara Oakley and Michael Johnston, was outstanding—both highly engaging and deeply relevant for our academic community. My colleagues and I found it exceptionally useful.”
Positive staff feedback after a full-house event
The flagship opening event brought together over 170 staff (74 in-person and 96 via livestream). The masterclass quickly reached capacity, underscoring the strong appetite for evidence-based insights into learning, teaching, and educational innovation.
Feedback from staff has been overwhelmingly positive. Participants described the sessions as engaging and relevant, highlighting the value of learning directly from world-leading experts in cognitive science.
“The session was excellent: a speaker who challenged us to do better, and a real reminder of how valuable in-person events are for building connections across the teaching community. More of this, please!”
The momentum continued with three one-hour online workshops of extended learning, The Science of Learning and AI: How the Brain Builds Knowledge; Inspiring Frameworks for Learning; and AI as a Thinking Partner: Enhancing Learning, Avoiding Shortcuts.
The first two online sessions attracted 81 and 85 participants respectively, while the final session concluded the series on Thursday 1 May, with 54 registered participants.
A much-needed exploration of how we learn
Although learning sits at the heart of academia, many students—and educators—have had limited exposure to the neuroscience of how learning occurs. Few students are explicitly taught how to learn, and cognitive science foundations can often be absent from traditional teacher education.
This series addressed those gaps, offering practical, research-informed strategies to foster deep, self-regulated learning and integrate generative AI thoughtfully into teaching practice. The sessions challenged familiar assumptions about how learning works and invited reflection on how teaching practice can better support deep, self-regulated learning in a Gen-AI-enhanced world.
Session overview and recordings
If you were unable to attend, or would like to revisit key insights, edited video recordings and presentation slides are available below.
Masterclass – Learning How to Learn and Learning with AI: Cognitive Science, Self-Regulation, and AI as a Thinking Partner
Part One: Barbara Oakley (1hr 20min)
What does it really take for knowledge to stick? In Part One of this masterclass, Barbara Oakley explored how learning depends on a dynamic interplay between focused attention and diffuse, reflective thinking—and why teaching strategies must respect this cognitive rhythm. She argued for the power of retrieval practice, spaced repetition, and metaphorical thinking in strengthening neural connections, while warning against relying solely on surface engagement. Oakley also challenged assumptions about generative AI, positioning it not as a replacement for thinking but as a catalyst for deeper curiosity and critical reflection.
Part Two: Michael Johnston (1hr 3min)
In the second half of the masterclass, Michael Johnston examined how feedback shapes learning and what this means for the use of Gen-AI in education. He emphasised the need for well-sequenced, teacher-guided instruction, cautioning that students must build foundational schemas through practice before AI can support their learning effectively. Drawing on educational psychology and curriculum theory, he highlighted the risks of relying on AI too early, and underscored that disciplinary knowledge, human connection, and cognitive effort remain central to meaningful learning.
Online Workshop 1: The Science of Learning and AI – How the Brain Builds Knowledge
By Barbara Oakley (1hr)
Is ‘active learning’ always as effective as it sounds? Oakley took a critical look at this question, arguing that structured guidance, retrieval practice, and direct instruction remain the real foundations of expert learning. She showed how techniques such as spaced repetition and interleaving push students beyond shallow familiarity to true mastery. The session also unpacked how differences in working memory—and the realities of neurodiversity—demand that we rethink how we support students, especially as AI tools become more embedded in learning environments.
Watch the recording to reflect on how a deeper understanding of cognitive rhythms can guide smarter course and assessment design.
Online Workshop 2: Inspiring Frameworks for Learning
By Barbara Oakley (1hr)
Motivation is not magic; it grows from deliberate design. In this session, Oakley explored how curiosity, structured challenge, and repeated retrieval work together to build resilient learners who can thrive in complex, AI-rich environments. She challenged educators to think carefully about how to scaffold cognitive development, particularly for neurodiverse students. Throughout the workshop, she returned to a central theme: if we want students to build durable, flexible knowledge, we must go beyond surface engagement and design for deep learning from the start.
Online Workshop 3: AI as a Thinking Partner – Enhancing Learning, Avoiding Shortcuts
By Michael Johnston (58min)
This final session focused on practical ways educators can use Gen-AI to support student learning without undermining foundational skill development. Johnston shared classroom-based examples—from concept formation to writing support—that illustrated how Gen-AI might help students clarify ideas, receive feedback, or extend thinking when used deliberately and with clear pedagogical intent. Emphasising the importance of teacher judgment, he argued that AI can complement teaching when it’s introduced at the right stage, but should never replace the modelling, scaffolding, and relational feedback that underpin meaningful learning.
Looking ahead
As you reflect on the insights from this series, how might they reshape your approaches to teaching and learning? What new possibilities emerge when you combine the science of learning with the thoughtful integration of generative AI? And how can you ensure all students are empowered not only with knowledge of what to learn, but also how to learn?
We invite teachers to engage with the session recordings and resources and to reflect critically on these questions, and to consider how our Signature Pedagogical Practices can influence your own teaching context.
Book a consultation with an expert learning designer through TeachWell Consult to explore ideas, strengthen your course design, or arrange a peer review of teaching.