Research in Action

In our previous post, we analyzed a cohort of approximately 87,000 U.S. educators, representing the top 5% of MagicSchool users. We found that the multi-purpose chatbot is the most used tool in the platform and that its usage represents a similar share of threads for each grade level. Averages conceal substantial variation. The analysis established a distinct grade-level gradient in specific tool use: elementary teachers favor tools supporting communication and student support, while middle and high school teachers trend toward greater use of tools for creating instructional materials. 

To dive deeper into use dynamics, this post focuses on the tools most used by teachers across subject areas. Since most elementary school teachers report teaching “All Core Subjects”, we restrict this analysis to a subsample of middle and high school educators who report teaching one or more specific subjects.¹

We focus on the sample of roughly 41,000 power users, educators who logged 52 or more distinct days on MagicSchool since creating their account. Our analysis tracks these teachers’ activity across specific tools, such as Lesson Plan or Writing Feedback, the individual interaction threads where content is generated, and the broader functional categories used to group these workflows. 

Early findings from the top 5% active users

Artificial intelligence is becoming increasingly embedded in teachers' daily work. Yet we know little about how educators are using these tools and whether usage differs across teaching contexts.

This post is the first in a series examining usage patterns among highly active users of MagicSchool, one of the largest K-12 AI platforms. This first post examines how teachers are using AI tools and how these patterns differ across elementary, middle, and high school grade levels.

Using platform activity data from approximately 87,000 U.S. educators in the top 5% of MagicSchool users between May 2023 and November 2025, we explore which grade levels and subject areas these highly engaged users teach and how they interact with AI tools. Narrowing the analysis to highly active users allows us to focus on educators who have integrated these tools into their regular practice.

High-impact tutoring is a highly effective, well-researched approach for accelerating student learning. Students who receive it show learning gains of three to more than 15 months across grade levels and content areas, along with improved well-being and engagement in school. Many practitioners identify strong relationships as a key driver of effective tutoring, and research suggests that relationship-building strategies, particularly consistent tutoring with the same tutor, improve student engagement, attendance, motivation, and learning outcomes. 

AI tools are arriving in schools faster than research can evaluate them. Teachers are experimenting with new tools and districts are writing policies, all while students are already using AI both inside and outside the classroom.

But for many education leaders, a basic question remains: What does rigorous research actually say about how AI affects teaching and learning?

To help answer that question, we released a new report: The Evidence Base on AI in K-12: A 2026 Review. The report reviews the current research, focusing specifically on studies that convincingly estimate causal impact, meaning studies that can tell us whether an AI tool changed outcomes for students or educators.

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. 

A new collaboration between Stanford’s SCALE and OpenAI, the creator of ChatGPT, strives to better understand how students and teachers use the popular AI platform and how it impacts learning

Education is one of the fastest-growing use cases of AI products. Students log on for writing assistance, brainstorming, image creation, and more. Teachers tap into tools like attendance trackers, get curriculum support to design learning materials, and much more.

Yet despite the rapid growth – and potential – a substantial gap remains in knowledge about the efficacy of these tools to support learning. 

A new research project from the Generative AI for Education Hub at SCALE, an initiative of the Stanford Accelerator for Learning, aims to help fill that gap by studying how ChatGPT is used in K-12 education. In particular, the research will examine how secondary level teachers and students use ChatGPT.