Teaching / Critical Thinking in the Age of Data & AI
Scheduled · January 2027

Critical Thinking in the Age of Data & AI

Charts, dashboards, and AI-generated content are everywhere, but how do you know what to trust? This course explores how to think critically about the data you encounter at work and in everyday life. No technical experience required.

Schedule & Registration at Parkland ↗

About the Course

Who it's for

Working Professionals & Small Business Owners

Data and generative AI tools now touch nearly every job and business. The course is built for people with little to no prior data or AI experience, across roles and industries.

What you'll ask

Better Questions

Where did this data come from? What might be missing? Is this chart telling the full story? Can I trust this AI-generated summary?

Register at Parkland

Dates, times, location, and registration are on the Parkland College Community Education listing.

Schedule & Registration ↗ More about my teaching

Six Sessions

Each session builds on the last, moving from foundational questions to hands-on practice with examples you bring.

Session1

What Is Data and AI, Really? Foundations and Everyday Risks

Plain-language definitions of data and generative AI, a tour of common tools (ChatGPT, Copilot, Gemini, Claude), and practical data-governance habits for everyday use, including the real risk of leaking business secrets by pasting sensitive information into a public AI tool.

Learning objectives
  • Define basic terms: data, dataset, generative AI, dashboard.
  • Identify common generative AI tools and what each is typically used for.
  • Ask foundational questions about any dataset or chart: where did this come from, what might be missing, is it reliable.
  • Apply basic data-governance habits when using commercial AI tools.
Session2

Misleading Visualizations and Why Our Brains Fall For Them

The cognitive science behind misleading charts, such as scale distortion and misjudged growth or change over time, with practice spotting issues in real examples from news, social media, and work.

Learning objectives
  • Analyze a chart to identify common visual tricks that mislead viewers.
Session3

Is This Chart Telling the Full Story?

Audience and context when reading or sharing a visualization, plus a light introduction to accessible and inclusive design. A chart needs a written narrative: people process visual and verbal information through different channels, and it is how alt text for screen readers gets written.

Learning objectives
  • Judge whether a visualization needs a written narrative description.
  • Write a short narrative description suitable for both comprehension and accessibility.
Session4

Can AI Build This For Me? Understanding AI-Generated Visualizations

What generative AI can and cannot reliably do. Two ways AI can help make a chart: give it your data to produce a finished image, or ask it to write code that processes data you keep to yourself. We compare the tradeoffs for governance and transparency.

Learning objectives
  • Describe the two basic ways AI can help create a visualization.
  • Compare the tradeoffs of each in governance and transparency.
  • Evaluate whether a chart or AI-generated summary seems trustworthy and complete.
Session5

Putting It Into Practice

Bring a chart, dashboard, or AI-generated summary from your own work or daily life and apply what you have learned, with group discussion and takeaways.

Learning objectives
  • Apply what you've learned to a real example from your own work or daily life.
Session6

Showcase of Projects by Students

Share a dashboard you built, whether from the tutorial example provided in the course or your own, and get feedback from others in the course.