By Anna Valeva (Western Illinois University)
Information
Drawing on two foundational texts in data visualization—The Wall Street Journal Guide to Information Graphics by Dona Wong and How Charts Lie by Alberto Cairo—this project reimagines how students engage with core “Dos and Don’ts” of visual communication. Using AI (Claude) as a coding assistant, we create apps that demonstrate and summarize key principles, forming a supplemental library of interactive exercises. The focus is on best practices rather than coding, while inviting students to create their own apps with AI support. The key topics include creating the right comparison, controlling the message through framing the data with reference information, choosing the chart based on the intended message, appropriate use of pre-attentive attributes (color, order, orientation, position, shape, size, texture, value), and the importance of editing to achieve clarity. For over 11 years, Wong’s text has been central in our Data Visualization courses with Cairo’s text recommended for the past five years. Students have traditionally engaged through reading, discussion, short-answer assignments, hands-on Tableau viz building, and exams. However, we have observed limited long-term retention and engagement with these approaches. This project shifts learning from passive consumption to active exploration by allowing students to interact with apps and then create their own versions. Students are introduced to the apps and the process of building them using AI. They are then assigned to develop their own apps based on selected visualization principles. Through this process, they implement fundamental ideas of visual communication while focusing on specific concepts each app is designed to illustrate. Student interviews compare experiences with traditional instruction and AI-assisted interactive learning. This work is implemented at a small public university in rural Illinois, with class sizes of 5–20 students, primarily upper-division undergraduate business majors and some graduate students. By lowering technical barriers, this approach supports deeper engagement and helps students more meaningfully apply data visualization principles in practice.
Link: https://drive.google.com/drive/folders/1sCwHZ-ko4_84hqWXQE0sPWOcQPku0Pij?usp=sharing