Machine learning to boost digital education uptake and impact
Challenge
With shifts to digital learning, displaced communities are excluded from the benefits of these types of learning opportunities due to a lack of infrastructure, devices and know-how. UNHCR has worked to track and centralise digital learning resources but needs to more rapidly align content to relevant curricula, in partnership with Learning Equality.
Solution
UNHCR’s Global Education team will use machine learning and natural language processing to partially automate the curriculum alignment process, which will improve the relevance, adoption, and impact of digital learning resources. A competition is bringing together machine-learning practitioners to design new algorithms to support this effort, and the winning solutions will be made publicly available.
Impact
Creative solutions governments and educators can leverage to more efficiently align digital resources, particularly during crises. Uninterrupted access to education is supported for displaced communities and others, with benefits for students, teachers, parents and communities.
Project impact
Other information
Aligning open educational resources to national curricula is one of the biggest barriers to quality offline learning in refugee and low-connectivity settings, traditionally requiring weeks of manual work by curriculum specialists. Through a partnership between UNHCR, Learning Equality, Vodafone Foundation, and Google.org, this project leveraged a global Kaggle AI challenge to crowdsource state-of-the-art models that automatically match K-12 content to local curricular standards across 29 languages. The winning solutions now power AI-driven curriculum recommendations in Kolibri Studio, dramatically reducing the time educators need to organize relevant materials for refugee and host-community learners in offline contexts. The approach is already supporting refugee education programmes across Kenya, Uganda, Tanzania, Jordan, Mozambique, and South Sudan, and is scalable to any country where connectivity is limited. Learn more about this initiative in Learning Equality's webstory.