How might low- and middle-income countries leapfrog the global status quo and create education models that are fit for the context?
How might low- and middle-income countries leapfrog the global status quo and create education models that are fit for the context?
EdTech Hub’s AI Observatory is exploring how education systems can create context-driven solutions – which means ensuring AI tools, models, or approaches genuinely meet the diverse needs of a place and the people within it.
This week, in Issue No. 30 of the #WaypointWednesday, we spotlight early visions of AI-enabled education beyond the classroom, cross-cultural learning supercharged by AI, and an alternative to language models.
Early signals
Beyond brick-and-mortar classroom education
We’re seeing early visions of AI-enabled education that take place outside of formal classrooms and are embedded in everyday life and communities.
- Global – Microschools: Interest is growing in microschools: small schools (around 15 students) designed to offer a more personalised and flexible learning experience compared to traditional schools. Although not explicitly related to AI, this model could signal a future where AI-personalised learning supports smaller school models. (RAND, 2025)
- Global – Matter and Space’s Butterflies: In a podcast episode, Paul LeBlanc, co-founder of education company Matter and Space presents his vision for learning in the age of AI if it were “unconstrained by the way in which it happens today”, followed by a critique from education expert Maha Bali from a Global South perspective. (Learning Curve, 2025)
Learning about other contexts in immersive ways
We’re seeing AI and immersive technologies used to support learning about other cultures in more situated and authentic ways, in order to help learners develop the skills they need to understand people from different cultural backgrounds.
- Asia – AI-based X-Cultural App: This study developed and implemented an AI-based X-Cultural App with 25 graduate students from Chinese, Thai, Indonesian, and Vietnamese ethnicities to support cross-cultural learning in authentic environments. Results showed improvements in participants’ cross-cultural competencies. (Shadiev, Nguyen, and Hwang, 2026)
Beyond language models
Large language models have captured much of the attention, but they are not the only path, as small language models have shown. One signal that caught our attention recently is Yann LeCun’s ambition to leapfrog language models altogether.
- France – World Models: Yann LeCun, a top AI researcher, is arguing that the current fixation on large language models is fundamentally limited. His new Paris-based venture, AMI Labs, is an attempt to pursue an alternative direction – “world models” – systems that try to learn how the world works, not just how text is structured. (MIT Technology Review, 2026)
Reflections:
- In some ways, LMICs have a greater potential to leap frog ahead with envisioning new models for education. Other examples where Africa has jumped ahead include mobile money (circumventing traditional banking models), mobile-first (bypassing fixed line telephones), off-grid pay-as-you-go solar polar (shifting away from centralised power infrastructure) and mobile internet (instead of fixed broadband internet.
- Looking back to more traditional and indigenous models and purposes of education can help us look ahead to re-envisioning them, for example, recentring relational models of learning with family, community and role models; cultivating holistic development of moral character, emotional intelligent, and spiritual awareness; and learning through connecting with nature and land.
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We’d love to hear from you! What’s been shaping your thinking on AI? Drop your thoughts (and reading recommendations) in the comments. Explore more from EdTech Hub’s AI Observatory.
EdTech Hub’s AI Observatory is made possible with the support of the UK’s Foreign, Commonwealth and Development Office.
