How does the brain learn? And why don’t we teach that way?

For decades, higher education has relied on a familiar model: expert-led lectures, content-heavy curricula and assessments designed to measure recall. While this approach has produced graduates with strong theoretical knowledge, it increasingly falls short in developing deep understanding, adaptability and real-world competence.

Insights from neuroscience and learning science now make one point clear: the way we teach often conflicts with how the brain learns. The challenge for universities and technical institutions is no longer whether or not the system needs changing, but how to redesign learning in ways that are both evidence-based and scalable.

How the brain learns: experience before explanation

Neuroscience research consistently shows that learning is strongest when it is active, contextual and experiential. The brain forms durable neural pathways when learners interact with problems, experiment with solutions, receive feedback and reflect on outcomes.

Traditional lecture-dominated instruction primarily activates language and short-term memory systems. In contrast, experiential learning engages multiple brain regions simultaneously: sensory processing, motor control, decision-making and emotional engagement. This multi-path activation is what transforms information into understanding.

• Are we teaching information or developing understanding?
• Teaching students to assess their work and why it matters beyond university
• Embracing co-creation through experiential learning

Crucially, learning precedes language. Humans – from early childhood onward – grasp cause and effect, risk, patterns and systems long before they can formally articulate them. Explanation and terminology are most effective when they arrive after learners have already encountered a concept in action.

Why explanation-first models struggle

Many higher education challenges stem from reversing the natural learning sequence. Students are introduced to abstract models, equations or frameworks before they have experienced the problems those tools are designed to solve. This often results in surface learning: content that is memorised for assessment, but quickly forgotten, or poorly transferred to practice.

This is particularly evident in STEM (science, technology, engineering and mathematics), computing and professional programmes. Students may “know” a formula, a programming syntax or a security concept, yet struggle to apply it in unfamiliar or real-world contexts. The issue is not student ability, but instructional design.

A more brain-aligned sequence looks different:

Experience → observation → discussion → explanation → theory

This approach does not remove rigour. In fact, it increases it by anchoring theory to lived cognitive experience.

Play, experimentation and applied understanding

Experiential learning is often misunderstood as informal or unstructured. But in reality, it can be highly intentional and academically robust. Consider how learners intuitively understand physics long before formal instruction: speed through motion, force through resistance, action–reaction through physical interaction.

The same principle applies in higher education. Simulations, labs, case scenarios, prototyping and applied challenges allow students to internalise complex systems before formal models are introduced. Theory then becomes a tool for explanation and optimisation, rather than an abstract starting point.

Nowhere is this shift more urgent than in technical and professional disciplines.

• Programming is learned more effectively when students start by making systems work, debugging behaviour and observing outcomes, before naming constructs such as loops or variables.
• Cybersecurity understanding deepens when learners experience simulated breaches, misconfigurations and mitigations before studying formal threat models.
• Artificial intelligence becomes clearer when students train simple models, observe bias and data dependency, and then explore the theory behind machine learning.

In each case, competence emerges from interaction, not exposition.

Redefining the role of the educator

This model requires a fundamental shift in academic roles. Educators move from being primary transmitters of information to designers of learning experiences. Their expertise is expressed through:

• Structuring meaningful challenges
• Curating environments for safe experimentation
• Guiding reflection and conceptualisation
• Connecting experience to disciplinary frameworks.

This turns students into active participants: testing ideas, making decisions and constructing understanding rather than passively receiving content.

A case study in applied transformation

At my institution, this experience-first philosophy has been embedded across technical and vocational pathways. Curricula are designed around laboratories, workshops, simulations and project-based learning, with theory introduced to explain and refine what students have already encountered.

Physics is taught through measurement and motion; engineering through assembly and system design; digital disciplines through applied projects, cybersecurity labs and generative artificial intelligence experimentation. Across programmes, students engage with real tools, real constraints and real decision-making early in their learning journey.

The results have been tangible. Graduates demonstrate stronger practical competence, confidence and readiness for further study or employment. Importantly, community trust has grown alongside educational outcomes: student enrolment has increased by more than 400 per cent over five years. This has been driven not by marketing, but by perceived value and graduate capability.

What educators can take forward

Redesigning learning around brain-aligned principles does not require abandoning academic standards. It requires rethinking sequencing, emphasis and roles. Practical steps institutions can take include:

• Introducing experiential components before formal theory
• Embedding simulation and problem-based learning early in programmes
• Designing assessments that reward application, not recall
• Investing in faculty development focused on learning design
• Aligning curricula with real-world systems and uncertainty.

Return learning to its natural order

Neuroscience does not argue against explanation or theory; it clarifies when they are most effective. When experience comes first, explanation sticks. When learners act before they abstract, understanding deepens.

For higher education facing rapid technological and societal change, the message is clear: teaching that prioritises transmission over experience limits learning. Teaching that aligns with how the brain works builds capability, resilience and transfer.

The future of effective higher education lies not in saying more, but in designing better experiences for students to learn from.

Mussab Aswad is academic vice-principal at the Nasser Centre for Science and Technology.

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