Critical Data Literacy: What Is It and Why Should It Be Part of Our Classrooms in 2026?

Critical data literacy is more than just basic understanding of how to work with or make sense of data. It is knowing how to think critically with and about data. And it is crucial for our students to learn as they are building their foundational data skills.

Therefore, it is not just a skill for any one subject area to tackle—it’s instead an essential part of how all students need to learn how to make sense of the world. Whether they’re interpreting a historical graph, evaluating a claim on social media, or examining data linked to a current event, students need the ability to question, analyze, and reason with that information critically. They cannot and should not blindly accept as fact or the whole story what is presented.

As we look toward 2026, when data continues to shape nearly every aspect of students’ academic, civic, and digital lives, building these skills intentionally has never been more important.

Why Critical Data Literacy Matters in Every Classroom

While “data literacy” provides students with a broad foundation in working with and making sense of data, critical data literacy expands in a key different area: students not only work with and make sense of data but they also question its source, purpose, limitations, and the perspective behind it.

Here’s why all students need to build their critical data literacy and thus why it is important for us all across our subject areas—truly all of our core and non-core subject areas—to help them:

1. Students Encounter Data Everywhere

From election polls to infographics on climate change to statistics embedded in advertising, students currently, and will continue to, see data-based claims constantly. But without guidance on how to engage with such material critically, they often accept these data-based claims and data visualizations at face value.

Through building out critical data literacy students learn to ask:

• Who created this?
• What story are they trying to tell?
• What’s missing?
• How else could this data be interpreted?

 👉 Want a grade-leveled resource to ask your students deeper questions to help them build these skills as they are working with data? Explore our What to Ask? Digital Resources.

2. It Strengthens Academic Reasoning Within and Across Subjects

Whether students are writing CER responses in science, interpreting demographic shifts over time in social studies, or evaluating survey results presented in a non-fiction text in English class, working with data is woven into our disciplinary work in the 21st century. 

And in each of these examples, students need more than just the skills of how to make a graph or calculate a slope. They need to know how to actively engage with the material. In fact they need to be able to:

• Write evidence-based claims,
• Engage in academic discourse about the material, 
• Offer nuanced interpretations of the texts, visuals, and arguments, etc.

These are all aspects of critical thinking that our students learn within each of our subject areas and they are as essential for working with data as working with prose. Thus there is a great opportunity–in the time of us all being asked to do more and more–to leverage each other as resources in helping students build these necessary skills. By aligning our approach as a full faculty we can each be individually responsible for less, but set our students up for more success overall.

Curious of how to do this within your school or district? Reach out, we would love to share some ideas and examples of how it is working elsewhere: [email protected]. 

3. It Helps Students Navigate Misinformation

We live in a century in which making and sharing data visualizations is becoming increasingly easier each year via the internet and other mediums. This has many advantages for us as a society, and for situating our students’ data work in a larger context to “do better in life”, rather than just in school like Elliot Eisner (2003) positioned as the function of schooling. Yet multiple studies over the years continue to demonstrate that students struggle to evaluate the credibility of online content—including when it includes visuals or data.

This is exactly why we need to strive to set our students up to “instead of just looking at charts, as if they were mere illustrations, we must learn to read them and interpret them correctly.” (Cairo, 2019, xiv). This may feel easier said than done, especially when it comes to building students' skills in working with data in a post factual world (as described by Boaler & Williams, 2025). We have all seen and heard examples in which data visualizations have been used to manipulate a reader’s interpretation of the evidence, situation, and/or facts. Therefore, it is extremely important that we help our students “stop, look, and listen” (as suggested by Fontichiaro and colleagues, 2017) to all data visualizations, even those not designed to manipulate, as all visualizations can mislead or be misunderstood, even those not designed ill intent. Critical data literacy empowers students to do just that by checking data-based claims, not just consuming them.

4. AI, Algorithms, and Digital Tools Are Accelerating the Need

As of this year (2026), students will encounter data not only through their own creation in our classrooms, but also “out in the wild” through:

• AI-generated charts and summaries,
• Algorithm-curated media and reel feeds,
• Predictive models tied to their everyday tech practices, etc.

Without critical data literacy, the outputs from these tools can mislead our students into thinking what they are served is factual, accurate, and truthful. 

What Critical Data Literacy Looks Like in the Classroom

Critical data literacy includes several interconnected skills, for example pieces like:

1. Interpreting the Structure of Visualizations

Noticing:

• scale
• representation choices
• how visuals influence understanding

Looking for some additional strategies for how to help students engage with others’ data visualizations? Check out our upcoming Jan/Feb 2026 Science Scope article “Critique to Create: Leverage Real-World Visualizations to Deepen Data Skills (Data Literacy 101).”  

2. Questioning Data Sources

• Who collected it?
• Why was it collected?
• Is the sample representative?
• What’s missing?

3. Spotting Bias and Limitations

Students learn that all data tells a story—and sometimes that story is incomplete or skewed.

4. Comparing Conflicting Claims

Students examine multiple datasets or charts about the same topic and decide which is more reliable or why the conclusions differ.

👉 Support students in evaluating data-based claims with Dataspire’s “What to Ask?” resources (grade-banded).

Why 2026 Is a Turning Point for Critical Data Literacy

Looking ahead critical data literacy is a thread that ties many current educational realities together:

1. Standards Are Evolving

More states are incorporating data reasoning, modeling, and analysis across multiple subjects—not only math.

2. The Classroom is More Data-Rich

Students interact with:

• Assessment dashboards
• Interactive simulations
• Digital mapping tools

Each offers opportunities for deepened sensemaking if students can question and interpret the information presented.

3. Employers Want Data-Savvy Thinkers

Future jobs—from business to the arts—rely on analyzing, interpreting, and critiquing data. Embedding these skills early positions students for long-term success.

Practical Ways Teachers Can Embed Critical Data Literacy (Any Subject)

Our upcoming Jan/Feb 2026 Science Scope article “Critique to Create: Leverage Real-World Visualizations to Deepen Data Skills (Data Literacy 101)” dives into a hands-on structure for helping students build their critical thinking around data visualizations (accessible here: https://www.tandfonline.com/journals/ujss20). And here are a few other suggestions:

🔎 Strategy 1: Compare Two Conflicting Graphs

Students analyze:

• which is more reliable
• why they differ
• how someone might misinterpret one or the other

🧠 Strategy 2: Analyze the “Hidden Story”

Have students identify:

• the creator’s perspective
• missing groups
• assumptions made in the data