DeepMind CEO Demis Hassabis says learning how to learn is the next generation's most needed skill

Learning How to Learn: What Educators Need to Prioritize Next

At the Odeon of Herodes Atticus in Athens, Demis Hassabis, CEO of Google DeepMind and 2024 Nobel laureate, made a clear call to education leaders: the core skill students need is learning how to learn. With AI changing tools and job demands week by week, content expires quickly. Meta-skills endure.

Hassabis suggested artificial general intelligence could arrive within a decade. Big gains are likely-and so are risks. Greek Prime Minister Kyriakos Mitsotakis added a warning: if people don't see personal benefit and wealth pools around a few companies, expect backlash.

What "learning how to learn" looks like in practice

• Metacognition: Teach students to plan, monitor, and adjust how they study. Short weekly reflections: What worked? What didn't? What's the next move?
• Retrieval over review: Use low-stakes quizzes, oral checks, and one-page recalls. Retrieval strengthens memory far more than re-reading.
• Spaced and interleaved practice: Spread practice and mix problem types. Build long-term retention and transfer.
• Deliberate practice: Break skills into sub-skills. Give specific feedback loops with clear criteria of success.
• Learning with AI: Treat AI as a thinking partner, not an answer machine. Require students to show prompts, iterations, and reasoning traces.
• Transfer across domains: Have students apply one method (e.g., Fermi estimates, causal maps) across science, history, and design.

Program moves you can implement this year

• AI across the curriculum: Add one authentic AI use case per course (summarization critique in literature, code review in CS, data cleanup in science).
• Prompting and critique: Teach prompt patterns (role, constraints, examples) and require students to compare outputs, cite sources, and flag hallucinations.
• Assessment upgrade: More oral defenses, live problem-solving, version histories, and portfolios. Grade process and evidence, not just final answers.
• Micro-credentials: Offer short badges in data literacy, AI safety, prompt design, and automation. Stack them into course credit.
• Teacher PD sprints: Monthly 60-minute labs: one tool, one workflow, one shared template. Produce a classroom-ready artifact every session.
• Academic integrity policy: Define permitted use, required disclosures, and consequences. Provide examples of good and poor AI use.
• Tool audits: Review privacy, bias, data retention, and cost before adoption. Prefer tools with clear FERPA/GDPR language.

Equity and trust: show personal benefit

Mitsotakis cautioned that concentrated gains fuel inequality. Schools can counter that by showing concrete wins for every learner. Track and publish outcomes: time saved, feedback quality, and student growth across groups-not just top performers.

• Offer device access and offline options. Pair AI use with foundational skill building.
• Run family workshops on safe, effective AI use. Explain data practices in plain language.
• Use AI to extend feedback and tutoring time, then monitor impact on struggling students.

Why this matters now

Hassabis' point was blunt: the only certainty is change. Curriculum locked to a static content list falls behind. Programs built on meta-skills, authentic tasks, and continuous upskilling stay relevant.

Context on AI and science progress

Hassabis' team helped crack protein folding prediction, accelerating biomedical research. For background on that scientific step, see DeepMind's overview of AlphaFold here. For policy framing, the OECD's work on AI and education offers a useful reference here.

Quick start: templates and courses

• Create a "learning audit" template: goals, strategies tried, retrieval schedule, AI prompts used, and evidence of improvement. Use it weekly.
• Build a shared prompt library with examples for lesson planning, rubric drafting, and feedback scripts.

If you need structured upskilling for faculty or by role, explore courses by job and the latest AI courses.

Key takeaways for education leaders

• Prioritize meta-skills over static content: metacognition, retrieval, spacing, transfer.
• Normalize AI as a partner with documented process, citations, and critique.
• Shift assessment to demonstrations of thinking and authentic work.
• Invest in teacher workflows that save time and improve feedback quality.
• Measure equity impacts and communicate clear benefits to students and families.

Change is coming either way. Build learners who can keep learning.