Measuring the impact of learning with AI in Sierra Leone and beyond

The results from this pre-registered trial suggest that AI can be a powerful pedagogical partner — not by replacing teachers, but by augmenting their reach. This study is part of our ongoing effort to build a global evidence base for the impact of AI on teaching and learning.

A common concern is that generative AI could become a shortcut for students, potentially bypassing the challenging yet essential cognitive effort required for deeper learning. Guided Learning is designed to address this concern: it’s built from years of research and work in our LearnLM efforts to be pedagogically-grounded and specifically tuned to prioritize building understanding over providing direct answers.

The data from Sierra Leone suggests this approach is working. An analysis of over 113,000 interactions exchanged during our trial revealed that students used the tool to build conceptual understanding in 91.4% of conversations, rather than simply seeking solutions. Gemini responded by posing scaffolding questions in 76% of its messages, providing direct solutions in only 2% of cases. This "Socratic" interaction ensures that the cognitive heavy lifting remains with the student.

In focus groups, teachers reported that Gemini also supported their own professional growth. By using the tool for lesson preparation, they discovered new ways to explain familiar topics like fractions. Many described a shift from "lecturers" to "facilitators," moving through the classroom to support pairs of students as they navigated their own learning journeys.

To further understand the impact of Guided Learning on student learning, we are conducting a series of additional pre-registered RCTs globally. In the interest of advancing open science and disseminating timely insights, we are also releasing a playbook on our approach to RCTs with Fab AI to help others run faster, scalable studies aligned to their needs and contexts — to uncover robust localised evidence that keeps pace with technological advances. We will continue to publish our results and learnings as we conclude subsequent RCTs to construct a more comprehensive, cross-country evidence base, which we hope will inform responsible development of AI across the learning ecosystem. Additionally, our support of the Global AI for Learning Alliance (GAILA) will accelerate these commitments and others through collective action.

1 We also received support from Google.org and the Gates Foundation to conduct the trial. EducAid , Laterite and Oxford MeasurEd also collaborated with us.

The success of this trial was built on a partnership between AI and educators, where teachers remained firmly at the center of the experience. Educators designed the lessons, set the objectives, and facilitated classroom discussions that drove learning.

To help others implement similar programs, we are releasing a teacher training guide with materials created in collaboration with Fab AI, including the specific protocols used for this study.

The quantitative results were significant. Students using Guided Learning saw a gain of +0.258 standard deviations in their math scores compared to the control group. In practical terms, this represents roughly 1.2 to 1.7 years of typical learning progress achieved within the eight-week trial.