Education and learning-system work supporting workforce readiness, AI literacy, and institutional capability-building.
Analysis
Generative AI is no longer a speculative “future of education” concept—it is actively reshaping how students learn, how teachers assess, and how institutions define academic integrity.
Malaysia has moved beyond the stage of asking whether AI belongs in the national education conversation. It is already there. The more useful question now is whether Malaysia can convert policy moveme…
This article draws on a conversation with Yiwen Zhang, curator of the Global Shapers Community Beijing Hub and a PhD researcher at the London School of Economics and Political Science (LSE), alongside the Observatory’s broader focus on regional AI education developments in China.
As AI reshapes education, girls in underserved communities risk being left further behind. This article looks at the realities of access in Nairobi and how Telenovation is creating meaningful pathways into technology and opportunity.
We are launching a Global Call for Governing with AI, inviting governments to share AI use cases, policy initiatives, and implementation tools.
Mid-sized economies can preserve AI sovereignty through multinational cooperation, pooling talent and compute to reduce dependency on dominant AI powers.
Publications
China’s AI education transition illustrates a broader global shift: the central challenge is no longer whether schools can access AI tools, but whether education systems can redesign institutions fast enough to use them well. This brief examines how assessment pressure, teacher readiness, governance capacity, and uneven implementation shape China’s AI education pathway. By connecting China’s case with comparative insights from five countries, it argues that meaningful AI adoption requires moving beyond pilots and technology enthusiasm toward institutional change, evidence systems, and human-centered implementation.
Drawing on the latest Observatory Survey of Adult Skills (PIAAC), this report provides new evidence.
Releases
This research note examines how artificial intelligence is changing the relationship between early-career work, learning and capability development. Drawing on five structured conversations with early-career professionals and one strategic peer conversation, it identifies three emerging organizational signals: developmental tasks may be automated while low-learning work remains; stronger AI-assisted outputs may conceal weaker understanding and ownership; and broad responsibility does not necessarily translate into meaningful development. The note argues that sustainable AI-enabled productivity requires organizations to redesign work, learning and career progression together. It proposes practical questions for deciding which tasks should be automated, augmented, protected or redesigned, and how employees should be expected to verify, explain, judge and independently modify AI-assisted work. The findings are exploratory and are intended to establish a research agenda rather than make representative claims about Gen Z as a whole.
Artificial intelligence is transforming education, but its benefits remain out of reach for many of the communities that could benefit most. Drawing primarily from an hour-long public interview with Dr. Seiji Isotani and secondarily from the OECD Digital Education Outlook 2026 interview chapter, this memo argues that policymakers should stop treating infrastructure build-out as a precondition for AI in education. Instead, they should design around the infrastructure that already exists—especially mobile phones, intermittent connectivity, and teacher-led delivery models. The Brazil case discussed by Dr. Isotani shows that this approach can work at scale: 500,000 students across 7,000 schools and 20,000 teachers received materially faster feedback on writing, with statistically significant improvement and no meaningful urban-rural or resource-based gap in gains.
Playbooks
Purpose: Policy and guardrails for school use of AI.
For: School leaders, IT, policy officers
Purpose: Staged training and capability building for educators.
For: Teachers, professional development leads
Purpose: How to embed AI literacy and computational thinking into existing curricula.
For: Curriculum designers, educators
Purpose: Outcome and impact measurement for AI literacy programs.
For: School systems, researchers
Related coverage
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Related podcast episodes: Practical use of AI·The Future of Assessment
