Measuring the impact of learning with AI in Sierra Leone and beyond
Measuring the impact of learning with AI in Sierra Leone and beyond
衡量人工智能学习在塞拉利昂及其他地区的影响
June 9, 2026 | Responsibility & Safety | Zoubin Ghahramani 2026年6月9日 | 责任与安全 | Zoubin Ghahramani
Today, we are sharing the results and technical report from a randomized controlled trial (RCT), conducted in partnership with Fab AI and with the support of the Sierra Leone Ministry of Education. Over eight weeks, we evaluated how Guided Learning in Gemini affected the math progress of 1,763 junior secondary students across 12 schools in Port Loko District of Sierra Leone. 今天，我们分享了一项随机对照试验（RCT）的结果和技术报告。该试验是与 Fab AI 合作，并在塞拉利昂教育部支持下进行的。在八周的时间里，我们评估了 Gemini 中的“引导式学习”（Guided Learning）如何影响塞拉利昂洛科港区（Port Loko District）12 所学校 1,763 名初中生的数学进步。
“We look to be innovative and improve service delivery, but we must also rigorously study the results of our innovations… I am therefore delighted that we now have strong evidence that carefully designed AI can help improve learning outcomes in support of our many hard-working teachers.” — Conrad Sackey, Minister of Basic and Senior Secondary Education of Sierra Leone “我们寻求创新并改善服务交付，但我们也必须严谨地研究创新成果……因此，我很高兴我们现在有了强有力的证据，证明精心设计的人工智能可以帮助改善学习成果，支持我们许多辛勤工作的教师。”——塞拉利昂基础与高级中学教育部部长 Conrad Sackey
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. 这项预注册试验的结果表明，人工智能可以成为强大的教学合作伙伴——它不是取代教师，而是增强教师的影响力。这项研究是我们持续努力的一部分，旨在为人工智能对教学的影响建立全球证据库。
Beyond the answer engine: protecting critical thinking
超越答案引擎：保护批判性思维
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. 一个普遍的担忧是，生成式 AI 可能成为学生的“捷径”，从而绕过深度学习所需的、充满挑战但至关重要的认知努力。引导式学习旨在解决这一问题：它基于我们 LearnLM 项目多年的研究和工作，以教学法为基础，并经过专门调整，优先考虑建立理解，而非直接提供答案。
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. 来自塞拉利昂的数据表明这种方法行之有效。对试验期间超过 113,000 次交互的分析显示，学生在 91.4% 的对话中利用该工具构建概念理解，而不是简单地寻求解决方案。Gemini 在 76% 的回复中通过提出引导性问题（scaffolding questions）进行回应，仅在 2% 的情况下直接提供答案。这种“苏格拉底式”的互动确保了认知上的“重活”依然由学生自己完成。
A teacher-led intervention
以教师为主导的干预
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. 此次试验的成功建立在人工智能与教育工作者的合作之上，教师始终处于教学体验的核心。教育工作者负责设计课程、设定目标，并引导推动学习的课堂讨论。
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. 在焦点小组中，教师们表示 Gemini 也支持了他们的专业成长。通过使用该工具进行备课，他们发现了讲解分数等熟悉主题的新方法。许多教师描述了从“讲师”到“引导者”的转变，他们在教室中穿梭，支持学生结对探索各自的学习旅程。
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. 为了帮助其他人实施类似项目，我们发布了一份教师培训指南，其中包含与 Fab AI 合作制作的材料，包括本研究所使用的具体方案。
Measuring the impact
衡量影响
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. 定量结果非常显著。与对照组相比，使用引导式学习的学生数学成绩提高了 0.258 个标准差。从实际效果来看，这意味着在八周的试验中，学生取得了相当于正常学习进度约 1.2 到 1.7 年的进步。
Students in classrooms where their teachers incorporated Gemini into roughly half their lessons to meet a target of 12 hours during the trial saw even higher gains—roughly 1.8 to 2.5 years of progress. Engagement was also remarkably high: 69% of students met or exceeded usage targets, far surpassing the five percent typical for voluntary educational technology (famously known as “The Five Percent Problem”). That means students were not only engaged but they enjoyed coming to class more. 在教师将 Gemini 融入约一半课程以达到试验期间 12 小时使用目标的班级中，学生取得了更高的进步——约 1.8 到 2.5 年的学业进展。参与度也非常高：69% 的学生达到或超过了使用目标，远超自愿教育技术通常 5% 的参与率（即著名的“百分之五问题”）。这意味着学生不仅参与其中，而且更喜欢上课了。
Beyond the numbers, we also saw a profound shift in behavior. Students reported enjoying math more and actively engaged with learning beyond regular instruction. Crucially, over time, their conversations and questions became more learning-oriented, shifting toward skill building instead of seeking direct solutions. Specifically, skill-building queries rose to 90% by the final week — up from 68% in the first week — while solution-seeking questions dropped from 25% to 10%, proving students didn’t just want answers, they wanted to understand how they got there. 除了数据之外，我们还看到了行为上的深刻转变。学生们表示更喜欢数学，并积极参与常规教学之外的学习。至关重要的是，随着时间的推移，他们的对话和提问变得更具学习导向，转向技能培养而非寻求直接答案。具体而言，技能培养类提问的比例从第一周的 68% 上升到最后一周的 90%，而寻求答案的提问则从 25% 下降到 10%，这证明学生不仅想要答案，更想理解得出答案的过程。
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. 为了进一步了解引导式学习对学生的影响，我们正在全球范围内进行一系列额外的预注册随机对照试验。为了推进开放科学并传播及时的见解，我们还发布了一份关于我们与 Fab AI 进行随机对照试验方法的指南，以帮助其他人开展更快速、可扩展且符合其需求和背景的研究，从而挖掘出与技术进步同步的稳健的本地化证据。随着后续试验的结束，我们将继续发布结果和经验，以构建更全面、跨国家的证据库，我们希望这将为整个学习生态系统中人工智能的负责任发展提供参考。此外，我们对全球人工智能学习联盟（GAILA）的支持将通过集体行动加速这些承诺及其他目标的实现。
The path forward
前进之路
Though these results are promising, they also highlighted the challenge of the “achievement gap.” While the majority of students benefited, those who entered the trial with stronger math skills benefited most. This underscores an important need: to offer tools that deliver the strongest gains for the students who need it most. 尽管这些结果令人鼓舞，但也凸显了“成就差距”的挑战。虽然大多数学生受益，但那些在试验开始时数学基础较好的学生受益最大。这强调了一个重要的需求：提供能够为最需要的学生带来最大收益的工具。
Looking ahead, we plan to expand these trials to other countries and probe more deeply into areas like metacognition and relational intelligence to capture a more holistic view that explores the nuanced complexity of learning. By combining the relational foundation of a teacher-led classroom of students with the personalized, scaffolding capabilities of AI, we can help ensure that technology serves as a bridge to meaningful learning opportunities for all. 展望未来，我们计划将这些试验扩展到其他国家，并更深入地探讨元认知和关系智能等领域，以获取更全面的视角，探索学习中微妙的复杂性。通过将教师主导的课堂关系基础与人工智能的个性化引导能力相结合，我们可以确保技术成为通向所有人有意义学习机会的桥梁。
1 We also received support from Google.org and the Gates Foundation. 1 我们还获得了 Google.org 和盖茨基金会的支持。