Free, NGSS-aligned resources for teaching students to analyze data and understand artificial intelligence—developed by science educators for science classrooms.
Data in the Classroom provides free, standards-aligned curriculum resources for teaching students to analyze environmental data using artificial intelligence tools. We believe every student deserves to develop the skills they need to understand and critically evaluate data-driven claims.
Our curriculum modules use authentic NOAA datasets to teach both environmental science concepts and AI data literacy skills. Students learn to work with real satellite data while developing critical thinking about how AI analyzes information.
Everything we create is free, openly accessible, and designed by educators who understand the realities of classroom teaching. Our materials are NGSS-aligned and classroom-tested across diverse school contexts.
Our curriculum is developed in direct collaboration with NOAA scientists and educators, ensuring scientific accuracy and pedagogical excellence.
Real satellite observations and measurements from NOAA's extensive monitoring networks.
Curriculum mapped to NGSS, Common Core, and state science standards across grade levels.
Cutting-edge AI tools help students analyze patterns and understand complex systems.
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Data in the Classroom was originally developed as an environmental data education platform using NOAA satellite datasets. For years, we focused on helping students explore real-world environmental data—temperature trends, ocean conditions, atmospheric composition, and other measurements of our changing planet. Teachers appreciated the authentic context, and students engaged deeply with material that felt relevant to their own lives.
As artificial intelligence advanced and became more prevalent in data analysis, we recognized that our mission had to evolve. We couldn't responsibly teach environmental data analysis without also teaching students about machine learning, algorithmic bias, and how to work critically with AI-powered tools. The tools themselves were changing. Data analysis was changing. And what students needed to learn had to change with it.
Over the past three years, we've completely reimagined our curriculum. We kept the real environmental data that made our platform special—that authentic context is irreplaceable—but we wrapped it in a much more comprehensive approach to data literacy that centers on artificial intelligence.
Our new curriculum acknowledges the reality of modern data work: algorithms and machine learning models do much of the heavy computational work. The human role has shifted. Instead of manually calculating statistics, students now focus on asking the right questions, interpreting algorithmic results, identifying bias, and thinking ethically about how data analysis gets used.
We've added modules on machine learning fundamentals, AI bias, critical thinking about AI outputs, and responsible AI use. We've integrated contemporary AI tools into our lesson plans. And we've made sure that every resource we provide helps educators teach not just the "what" of data analysis, but the "why" and "how"—and most importantly, the critical thinking skills students need to work thoughtfully with AI.
Data in the Classroom serves educators and students across the K-12 spectrum:
Elementary educators (grades 3-5) who want to introduce data exploration and foundational thinking about what information data can reveal. Our elementary modules focus on observation, asking questions, and beginning to understand patterns.
Middle school teachers (grades 6-8) who teach science, math, and social studies and are looking for contemporary, relevant content that engages students in thinking about real-world data problems. Our middle school resources emphasize critical thinking and introduce foundational concepts in machine learning.
High school teachers (grades 9-12) in science, computer science, and math classes who need rigorous, college-preparatory content on data literacy, AI, and ethics. Our high school modules go deeper into statistical thinking, algorithmic bias, and the real-world applications of machine learning.
Schools and districts building comprehensive data literacy curricula or computer science programs and looking for peer-reviewed, standards-aligned resources.
Informal educators in museums, science centers, libraries, and youth programs who want to offer data literacy workshops and AI education.
Have questions about Data in the Classroom? Want to discuss a custom curriculum project? Looking for professional development support?
We'd love to hear from you. Reach out with questions about our resources, feedback on what's working in your classroom, or ideas for how we can serve you better. Our team is committed to continuously improving our content based on educator feedback.