Fast Facts
The Challenge
Teaching is often not targeted at the level of the student. Classes have wide-ranging learning levels. Many students do not learn effectively, becoming disengaged.
Our Role
Our role is to strengthen the evidence on how technology can improve personalised teaching and learning for individual students.
Our Goal
Our goal is to strengthen the evidence on how technology can improve personalised teaching and learning for individual students.is to influence how money is spent on personalised learning by strengthening the evidence on how technology can be used effectively and cost-effectively. Significant financial resources are being invested in a wide range of digital personalised learning programmes, yet there is limited evidence on whether these approaches are most effective as a complement to, or substitute for, other learning content, or on how they can be used in low- and middle-income countries. Clear, context-relevant evidence on pedagogically appropriate and cost-effective digital personalised learning can guide investment decisions and increase impact on learning outcomes.
EdTech Hub’s Work
Our focus is on strengthening the evidence base on how digital personalised learning (DPL) can improve learning outcomes in low- and middle-income countries (LMICs), and on the conditions under which these tools are most effective, scalable, and cost-effective. As governments and donors invest significant resources in DPL programmes, our work seeks to inform decisions about when and how these technologies should be used, including whether they should complement classroom instruction or, in more limited cases, substitute for it.
Digital personalised learning tools show promising evidence of impact, particularly in maths, literacy, and science, and in supporting learners to progress at their own level. However, important evidence gaps remain around implementation at scale, curriculum alignment, affordability, and the infrastructure and teacher support required for success. Our research examines how different models of DPL are designed and deployed, and how features such as adaptivity, feedback, and learner progression contribute to learning gains.
Through country-based research in contexts such as Kenya, we have studied a range of DPL tools with varying hardware and connectivity requirements, including mobile, desktop, and SMS-based solutions. This work highlights the trade-offs between technical sophistication and feasibility in LMIC settings, where access to devices and reliable connectivity remains uneven. We also examine how DPL tools can support marginalised learners and where they risk reinforcing existing inequities.
Across this work, our aim is to provide neutral, practical, context-specific evidence to guide policymakers, implementers, and funders in making informed investments in digital personalised learning, ensuring that these tools are pedagogically sound, affordable, and aligned with the realities of classrooms and education systems.
How Can Implementers Apply Digital Personalised Learning in Schools?
A practical guide offering examples, key insights to improve practice, areas for further exploration and more.
Related Studies
Behind the Screen: Teachers Co-Designing WhatsApp AI Tools
As AI becomes an increasingly prominent part of classrooms around the world, questions about its role in teaching and learning are intensifying. Yet too often, teachers are missing from the conversation. At the heart of our three Teachers-in-the-Lead sandboxes lies a simple hypothesis: If teachers are meaningfully engaged in shaping how AI is used, we will unlock more learning.
All Resources on Digital Personalised Learning
Frequently Asked Questions on EdTech for Digital Personalised Learning
What is digital personalised learning?
Technology to support personalised learning and teaching at the level of the student.
Are digital personalised learning tools effective for all learners, including those who are marginalised?
Digital personalised learning, also known as DPL, can help close learning gaps for some marginalised groups, such as lower-attaining learners or those in rural areas. However, many learners, including children with special educational needs and disabilities or minority groups, may still be underserved if tools are not designed inclusively or implemented with appropriate support.
Can digital personalised learning tools work in low-resource classrooms?
Yes, but their effectiveness depends on factors like access to devices, reliable electricity and internet, teacher training, and curriculum alignment. Tools designed for offline use or low-tech devices, such as feature phones, can make DPL more accessible in low-resource settings.
AI Insights for Digital Personalised Learning
Explore EdTech Hub’s AI Observatory, featuring a curated collection of weekly AI trends, research, and tools in DPL, teacher learning, and the wider education sector.
