AI Literacy and Data Literacy: Skills Every Teacher Needs
https://www.educatorstechnology.com/2025/08/ai-literacy-and-data-literacy-skills-every-teacher-needs.html
In the age of artificial intelligence, data is often described as “the new oil” (Crawford, 2021). Just as oil fuelled the industrial revolution, data now powers the algorithms and systems shaping our daily lives. But while oil can be refined and stored, data carries its own risks: bias, misuse, and ethical dilemmas that affect classrooms as much as companies.
For teachers, this makes data literacy and AI literacy more urgent than ever. Understanding how data is collected, analyzed, and applied is no longer just about spreadsheets or standardized test scores. It’s about preparing both educators and students to navigate a world where algorithms influence everything from lesson platforms to social media feeds.
This post explores what data literacy and AI literacy mean for education, why they matter, and the skills teachers need to model responsible, evidence-based practice in an AI-driven era.
What is Data Literacy?
When we talk about data literacy in education, we’re really talking about the ability to move from raw information to meaningful action in the classroom. Gummer and Mandinach (2015) describe it as “the ability to transform information into actionable instructional knowledge and practices by collecting, analyzing, and interpreting all types of data (assessment, school climate, behavioral, snapshot, longitudinal, moment-to-moment, etc.) to help determine instructional steps. It combines an understanding of data with standards, disciplinary knowledge and practices, curricular knowledge, pedagogical content knowledge, and an understanding of how children learn” (p. 2).
From the learner’s side, Vahey et al. (2012) stress that “data literacy requires that students investigate authentic problems; use data as part of evidence-based thinking; use appropriate data, tools, and representations to support this thinking; develop and evaluate data-based inferences and explanations; and communicate solutions” (p. 182).
More recently, the National Center for Education Statistics (2024) offered a broader definition that applies across classrooms and school systems: “Data literacy is the practice of examining and understanding data to draw and communicate conclusions and make decisions. Data-literate educators continually, effectively, and appropriately access, interpret, act on, and communicate multiple types of data from classroom, local, state, and other sources to improve outcomes and experiences for students” (p. 4).
Together, these perspectives highlight that data literacy is both a professional competency for teachers and a critical skill for students. It is about interpretation, ethical use, communication, and most importantly, transforming numbers into insights that improve learning.
Related: AI Literacy and Computational Thinking: Building 21st Century Skills
Why Data Literacy Matters for Teachers
Data literacy is a vital skill for today’s teachers. It enables them to move beyond intuition and base their decisions on evidence that reflects the realities of their classrooms. By understanding and using data effectively, teachers can better support student learning and wellbeing. Here are some of the ways it makes a difference:
Visibility: Knowing which data streams you have helps you prioritize what to monitor and where to look next.
Actionable Insight: Comparing attendance, behavior, assessment, and engagement opens a window into how factors outside—or inside—the classroom connect to learning.
Responsive Teaching: If a student’s homework isn’t completed, data can reveal whether the issue stems from access, motivation, or another factor, helping teachers respond more effectively.
Designing Interventions: Patterns in behavior, attendance, or assessments can signal when students may need additional support.
In these ways, data literacy equips educators to see the bigger picture and act with confidence.
Skills of a Data-Literate Teacher
Data literacy is not a single skill, it’s a collection of habits and practices that help teachers make better instructional choices. To use data effectively, educators need to build a toolkit that blends technical know-how with professional judgment. Some of the most important skills include:
Asking the right questions: Framing instructional challenges as problems that data can help address.
Collecting and selecting relevant data: Knowing what information is useful and what is just noise.
Interpreting data responsibly: Analyzing patterns carefully without jumping to premature or simplistic conclusions.
Connecting data to practice: Turning numbers and charts into concrete instructional decisions.
Evaluating outcomes: Checking whether data-informed changes actually improve learning.
Recognizing limitations: Understanding that data never tells the whole story and must be combined with professional judgment.
Safeguarding privacy and ethics: Respecting student confidentiality and using data responsibly.
Collaborating with colleagues: Sharing insights across grade levels and subjects to strengthen collective decision-making.
These skills highlight that data literacy is both individual and collaborative. Teachers need to think critically about data themselves while also working with peers to ensure students benefit from informed, ethical decisions.
Data Literacy and AI
In today’s classrooms, data literacy and AI literacy go hand in hand. AI tools are becoming more common in education, but they are only as trustworthy as the data that shapes them. For teachers and students, this means that developing strong data skills is essential to use AI responsibly and effectively. Here are some key connections:
AI tools depend on data: Understanding how data is collected and used helps teachers and students question AI outputs.
Spotting bias: Data literacy equips educators to identify biases in AI systems by asking who collected the data and whose voices may be missing.
Interpreting predictions: Being data-literate helps teachers and students critically evaluate AI outputs, such as adaptive learning scores, instead of accepting them blindly.
Protecting privacy: Teachers can highlight the importance of privacy and ethical use when AI systems handle student information.
Avoiding overreliance: While AI offers new ways to analyze classroom data, without strong data literacy teachers risk misinterpretation or overdependence.
Using everyday examples: Discussing platforms like TikTok recommendations or ChatGPT outputs helps students see how algorithms rely on data in daily life.
Hands-on projects: Small activities where students collect and analyze their own datasets show the link between data quality and AI reliability.
Preparing for the future: Ultimately, AI literacy grows from data literacy—teachers who model critical and ethical use of data prepare students to navigate an AI-driven world.
I’ve also captured these insights in a visual summary that you can check out below. To make it more practical, I’ve prepared an AI Literacy and Data Literacy PDF version that you can download and use with your students, in professional development workshops, or as a quick reference tool in your own practice.
References
Garner, I. (2022, June 29). Data in education. Learning A-Z Breakroom Blog. https://www.learninga-z.com/site/resources/breakroom-blog/data-in-education
Gummer, E.S. & Mandinach, E.B. (2015). Building a conceptual framework for data literacy. Teachers College Record, 117(A), 1-12.
Crawford, K. (2021). Atlas of AI: Power, politics, and the planetary costs of artificial intelligence. Yale University Press.
Mandinach, E. B., & Gummer, E. S. (2016). Every teacher should succeed with data literacy. Phi Delta Kappan, 97(8), 43–46. https://www.jstor.org/stable/24893334
Pennsylvania Department of Education. (n.d.). Data and assessment literacy: K-12 data informed culture in PA. Commonwealth of Pennsylvania. Retrieved August 24, 2025, from https://www.pa.gov/agencies/education/programs-and-services/instruction/elementary-and-secondary-education/assessment-and-accountability/pvaas/k-12-data-informed-culture/data-assessment-literacy.html
U.S. Department of Education, National Center for Education Statistics. (2024). Forum guide to data literacy (Publication No. NFES 2024‑079). U.S. Department of Education. https://nces.ed.gov/Pubs2024/NFES2024079.pdf?
Vahey, P., Rafanan, K., Patton, C., Swan, K., van ’t Hooft, M., Kratcoski, A., & Stanford, T. (2012). A cross-disciplinary approach to teaching data literacy and proportionality. Educational Studies in Mathematics, 81(2), 179–205. https://doi.org/10.1007/s10649-012-9392-z
Wikipedia contributors. (2025, August 11). Learning analytics. In Wikipedia. Retrieved August 24, 2025, from https://en.wikipedia.org/wiki/Learning_analytics
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August 24, 2025 at 06:25PM