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伙伴——不是取代教师，而是拓展其影响力。本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基础。
超越答案引擎：保护批判性思维
一个普遍的担忧是，生成式人工智能可能成为学生的捷径，潜在地绕过了深度学习所需的具有挑战性但至关重要的认知努力。引导式学习正是为解决这一担忧而设计：它基于我们在LearnLM项目中多年的研究和实践，以教学法为基础，并专门调整以优先建立理解而非提供直接答案。
来自塞拉利昂的数据表明这一方法行之有效。对试验期间超过11.3万次互动的分析显示，学生在91.4%的对话中使用该工具构建概念理解，而非简单寻求解决方案。Gemini在76%的回复中提出支架式问题，仅在2%的情况下提供直接答案。这种"苏格拉底式"互动确保了认知重担仍由学生承担。
教师主导的干预
本次试验的成功建立在人工智能与教育者的合作之上，教师始终处于体验的核心。教育者设计课程、设定目标，并引导推动学习的课堂讨论。
在焦点小组中，教师报告称Gemini也支持了他们的专业成长。通过使用该工具进行备课，他们发现了解释分数等熟悉主题的新方法。许多人描述了自己从"讲授者"向"引导者"的转变，在教室中走动支持结对学生的学习旅程。
为帮助他人实施类似项目，我们发布了教师培训指南，其中包含与Fab AI合作开发的材料，包括本研究使用的具体协议。
衡量影响
定量结果显著。使用引导式学习的学生在数学成绩上比对照组高出+0.258个标准差。实际而言，这相当于在八周试验期间取得了约1.2至1.7年的典型学习进步。
在教师将Gemini融入约半数课程以达到试验期间12小时目标的班级中，学生取得了更高的进步——约1.8至2.5年的学习进展。参与度也异常高：69%的学生达到或超过了使用目标，远超自愿教育技术通常的5%（即著名的"百分之五问题"）。这意味着学生不仅积极参与，而且更享受上课。
除了数字之外，我们还观察到行为的深刻转变。学生报告称更喜欢数学，并积极参与常规教学之外的学习。关键的是，随着时间的推移，他们的对话和问题变得更加以学习为导向，转向技能培养而非寻求直接答案。具体而言，技能培养类查询从第一周的68%上升到最后一周的90%，而寻求解决方案的问题从25%下降到10%，证明学生不仅想要答案，更想理解如何得出答案。
为进一步了解引导式学习对学生学习的影响，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。为推进开放科学并传播及时见解，我们还发布了关于我们与Fab AI进行随机对照试验方法的操作手册，以帮助他人根据自身需求和背景开展更快、可扩展的研究——发现与技术进步同步的可靠本地化证据。我们将在后续随机对照试验完成后继续发布结果和心得，构建更全面的跨国证据基础，希望为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举措。
前进之路
尽管这些结果令人鼓舞，但它们也凸显了"成就差距"的挑战。虽然大多数学生受益，但进入试验时数学基础较好的学生受益最大。这强调了一个重要需求：为最需要帮助的学生提供能带来最大收益的工具。
展望未来，我们计划将这些试验扩展到其他国家，并更深入地探索元认知和关系智能等领域，以捕捉更全面的视角，探索学习的细微复杂性。通过将教师主导的课堂中学生的关系基础与人工智能的个性化支架式能力相结合，我们可以帮助确保技术成为通向有意义学习机会的桥梁，惠及所有人。
1 我们还获得了Google.org和盖茨基金会的支持以进行试验。EducAid、Laterite和Oxford MeasurEd也与我们合作。
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.
Beyond the answer engine: protecting critical thinking
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.
A teacher-led intervention
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.
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 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.
Measuring the impact
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.
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.
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.
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.
The path forward
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.
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.
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.
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