Josephine Louie, Principal Research Scientist, Education Development Center
According to one source, the world will have gathered 181 zettabytes of data by the end of 2025 (Bartley, 2025). Unknown to me previously, I learned that a zettabyte equals 1 sextillion bytes of data, or roughly what can be stored on 250 billion DVDs. Clearly, the world has been collecting vast amounts of data that can help to address almost any empirical question we may pose. How much have we spent on eggs over the past year? How likely will that tropical storm hit our area? What are the chances a pandemic will strike again in our lifetime?
Amidst accumulating repositories of data, emerging AI tools offer the promise of helping to process and synthesize information faster and better than humans can. But unless we decide to forfeit human judgment and to delegate our own data sensemaking to what AI algorithms feed us, we must ensure that current and future generations develop solid capacities to make sense of data. Calls to build data literacy among K-12 students have been mounting for over a decade. Such literacy may be even more important as the growth of AI makes it easier to offload our thinking to machines—with uncertain cognitive, social, and economic consequences (e.g., Kosmyna et al., 2025; Manning, 2024).
A national convening of researchers and educators gathered at the National Academy of Sciences in 2022 to discuss what K-12 schools have been doing, and what more is needed, to promote data literacy and foundational skills for data science (NASEM, 2023). Discussions revealed that educators have lacked a clear roadmap of essential data skills and practices that schools should support students to develop throughout the K-12 years. These conversations helped to motivate a group of STEM education leaders, organized by the Data Science for Everyone coalition, to produce such a roadmap.
After assembling working groups of data science experts from across the country, conducting focus groups with educators and policymakers, and soliciting input from the public, the coalition released a set of Data Science Learning Progressions in summer 2025. Designed to inform education policy, curriculum development, research, and instruction, these progressions propose a sequence of data literacy learning outcomes through the K-12 years and across five content strands. The strands are:
- Dispositions and responsibility: Understanding data’s forms, sources, and uses, and comprehending how people shape data, its analysis, and its meanings
- Creation and curation: Developing mindful practices in collecting, assembling, and organizing data for analysis
- Analysis and modeling techniques: Learning appropriate tools and processes for uncovering patterns and insights from data
- Interpreting problems & results: Building capacities for generating data-based claims, inferences, and reasoning
- Visualization & communication: Developing skills in reading, interpreting, evaluating, and creating data visualizations as form of communicating about data
I was one of the many people who helped to hash out these progressions. It was both fascinating and challenging to share ideas with educators and researchers in statistics, mathematics, computer science, and the learning sciences to articulate priority learning objectives across grade bands and to hypothesize appropriate developmental milestones along the way. The current product is meant to be a living document, to be adapted as researchers and educators test its recommendations and suggest refinements.
Even with a roadmap, however, my colleagues and I have asked how K-12 educators can integrate data literacy learning into packed school curricula. We’ve been wondering whether teachers would be able and willing to integrate more robust data practices into their instruction if we identify data practices that are aligned with the content standards and curricula that they are already teaching, and if we provide support for their integration.
Following this idea, we developed a Data Literacy Standards Crosswalk – a resource that maps recommended data practices to K-12 standards in the Common Core State Standards for Mathematics (CCSSM), the Next Generation Science Standards (NGSS), and the Computer Science Teachers Association (CSTA) K-12 standards, among other standards. Our source for recommended data practices comes from the American Statistical Association’s Pre-K-12 Guidelines for Assessment and Instruction in Statistics Education II (GAISE II) report (Bargagliotti et al., 2020). The idea is to provide teachers with guidance on how they may make small moves to integrate recommended data practices into their existing instruction, to support both stronger data literacy and disciplinary content learning among students.
Both resources described above offer new strategies to help educators promote data literacy learning in K-12 schools. These resources will require additional development and ongoing input from educators and researchers if they are going to influence classroom instruction. In the meantime, AI-enabled machines lurk nearby, ready to process and interpret data for us, unless we continue to work toward strengthening everyone’s capacities to navigate and operate with agency in a world with ever expanding troves of data.
References
Bargagliotti, A., Franklin, C., Arnold, P., Gould, R., Johnson, S., Perez, L., & Spangler, D. A. (2020). Pre-K-12 Guidelines for Assessment and Instruction in Statistics Education II (GAISE II). American Statistical Association. https://www.amstat.org/asa/files/pdfs/GAISE/GAISEIIPreK-12_Full.pdf
Bartley, K. (2025, May 28). Big data statistics: How much data is there in the world? Rivery. https://rivery.io/blog/big-data-statistics-how-much-data-is-there-in-th…
Kosmyna, N., Hauptmann, E., Yan, Y. T., Situ, J., Liao, X. H., Beresnitzky, A., V., Braunstein, I., & Maes, P. (2025). Your brain on ChatGPT: Accumulation of cognitive debt when using an AI assistant for essay writing task. Preprint. MIT Media Lab. 10.48550/arXiv.2506.08872
Manning, S. (2024, July 3). AI’s impact on income inequality in the U.S.: Interpreting recent evidence and looking to the future. Commentary. Brookings. https://www.brookings.edu/articles/ais-impact-on-income-inequality-in-t…
National Academies of Sciences, Engineering, and Medicine (NASEM). (2023). Foundations of data science for students in grades K-12: Proceedings of a workshop. Washington, DC: The National Academies Press. https://doi.org/10.17226/26852