Teaching the Design of Data Visualization using Artificial Intelligence

About This Grant

This project aims to serve the national interest by improving STEM instruction via the design of data visualizations, which is foundational to scientific communication. Effective scientific communication is crucial for both advancing of scientific fields and engaging with the general public. Although students are provided detailed instruction regarding how to collect and analyze data, the current instruction on how to best communicate these data using visualizations is comparatively light. Thus, this project addresses a growth opportunity in scientific education. The project proposes to leverage emerging artificial intelligence (AI) tools to accomplish this instruction – thereby simultaneously improving communication skills while providing guided introduction to these AI tools. This Level 1 Engaged Student Learning project seeks to improve this instruction by producing a series of resources that demonstrate how to apply the rules of graphic design to create clear and effective data visualizations and assessing these resources using a rubric designed during the project. The resources and rubric will be made publicly available. The project will develop a suite of educational resources that will include a wiki and video tutorials, all freely accessible. These materials will provide the basis for the design of a series of workshops that will be given at universities and professional meetings. An existing university-level course will be restructured around these resources. The workshop and course materials will be hosted on the wiki, so that the materials are broadly accessible. Both experiences will leverage AI tools, such as ChatGPT and Gemini, to aid in the processing of data and creation of the data visualizations. Accompanying these instructional activities, a rubric will be designed to assess the quality of the data visualizations produced by participants in the workshops and courses. The design and testing of the rubric will be iterative, with the goal of producing and disseminating a validated instrument for assessing both the quality of data visualizations produced by participants and the efficacy of the instructional resources. The rubric will aid in the testing the guiding hypothesis for the project: that participants' ability to produce effective data visualizations will be improved via explicit instruction in the intersection between data visualizations and graphic design. Finally, the project plans to use a second rubric to test a secondary hypothesis: that instruction in the design of data visualization will also improve participants' ability to interpret data visualizations. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.