Understanding AI and learning outcomes

Education is one of AI’s most promising frontiers. With tools like ChatGPT, personalized learning support can be available to any student, anywhere, at any time. But the education sector is still early in its understanding of the impact of AI on learning outcomes. Last year, our team set out to study the use of tools like study mode and found promising gains in student performance. But our research also raised an important question: how can we assess how AI influences a learner's progress over time, not just on a final exam? This is a broader ecosystem challenge. To-date, most research methods focus on narrow performance signals—such as test scores—and lack the ability to assess how students actually learn with AI in real-world settings, and how that use shapes outcomes over time. To address this gap, we developed the Learning Outcomes Measurement Suite, a framework created with Estonia’s University of Tartu and the SCALE Initiative at the Stanford Accelerator for Learning to support longitudinal measurement of learning outcomes across different educational contexts. Extensive validation is underway through a randomized controlled trial, and further research is planned with founding organizations in the Learning Lab, OpenAI’s learning research ecosystem, including researchers from Arizona State University, UCL Knowledge Lab, and MIT Media Lab. Today, we’re sharing an overview of how the measurement suite works and why it matters. Over time, we intend to publish more research and release the measurement suite as a public resource for schools, universities, and education systems worldwide. “This research allows us to learn quickly while also laying the groundwork for a deeper understanding of how AI can be thoughtfully integrated into schools in ways that truly matter. We want to understand how these tools can support rigorous academic learning while also cultivating higher-order thinking, creativity, curiosity, and students’ confidence in themselves as learners.” Today’s research methods on the impact of AI on learning show promising signals about performance, but don’t capture the full picture of how AI affects learning outcomes over time. The Learning Outcomes Measurement Suite will, for the first time, provide a standard framework for longitudinal studies that help educators, researchers, and institutions understand how AI shapes learning and outcomes across different contexts. OpenAI’s Learning Lab is a new research ecosystem focused on advancing this work. OpenAI will publish findings alongside a range of partners as the field continues to develop. When students use AI tools to study and learn, it can mean many different things—from going to AI for quick answers to using it to work through problems step by step with tutor-like guidance. To encourage users to engage with ChatGPT in ways that support deeper understanding and skill-building, OpenAI introduced study mode last year. Under the hood, study mode is powered by custom system instructions we’ve written in collaboration with teachers, scientists, and pedagogy experts to reflect a core set of behaviors that support true learning, not just answers—using scaffolding, checks for understanding, and guided practice. To test whether this kind of pedagogically aligned AI interaction style translates into better learning outcomes, we ran a randomized study with over 300 college students preparing for neuroscience and microeconomics exams. While analysis is still underway, early results give us confidence that a pedagogically aligned AI interaction style, encouraged through features like study mode, can improve learning outcomes. But this research also surfaced an important reality: what really matters is whether the gains and associated productive behaviors remain durable over time. Most existing evaluation approaches rely on fixed interventions assessed over short time windows, using outcomes such as test scores or final essays as primary signals. These methods are not designed to capture the core mechanism through which AI affects learning in practice: ongoing, personalized interactions that evolve alongside a learner’s own strategies, preferences, and study habits. Based on the learnings from OpenAI’s study mode research, we have been building a structured measurement system to measure AI’s impact on learners at scale, and create a mechanism to improve models based on those outcomes. It is grounded in three signals—how the model behaves, how learners respond, and what measurable cognitive outcomes result over time.