Challenges and Opportunities in Data Visualization Education: A Call to Action
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Abstract (arXiv)
This paper is a call to action for research and discussion on data visualization education. As visualization evolves and spreads through our professional and personal lives, we need to understand how to support and empower a broad and diverse community of learners in visualization. Data Visualization is a diverse and dynamic discipline that combines knowledge from different fields, is tailored to suit diverse audiences and contexts, and frequently incorporates tacit knowledge. This complex nature leads to a series of interrelated challenges for data visualization education. Driven by a lack of consolidated knowledge, overview, and orientation for visualization education, the 21 authors of this paper-educators and researchers in data visualization-identify and describe 19 challenges informed by our collective practical experience. We organize these challenges around seven themes People, Goals & Assessment, Environment, Motivation, Methods, Materials, and Change. Across these themes, we formulate 43 research questions to address these challenges. As part of our call to action, we then conclude with 5 cross-cutting opportunities and respective action items: embrace DIVERSITY+INCLUSION, build COMMUNITIES, conduct RESEARCH, act AGILE, and relish RESPONSIBILITY. We aim to inspire researchers, educators and learners to drive visualization education forward and discuss why, how, who and where we educate, as we learn to use visualization to address challenges across many scales and many domains in a rapidly changing world: viseducationchallenges.github.io.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 15 Aug 2023.
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Citing papers per year: 2024: 3; 2025: 1; 2026: 2.
6 papers.
- 2024Charting the Future of AI in Project-Based Learning: A Co-Design Exploration with Studentscited by 8
- 2024StuGPTViz: A Visual Analytics Approach to Understand Student-ChatGPT Interactionscited by 7
- 2024C2: Scalable Auto-Feedback for LLM-based Chart Generationcited by 6
- 2025Automated Visualization Makeovers with LLMscited by 0
- 2026DataCanvas-EDU: An Agentic Framework for Instructor-Guided Synthetic Data Generation in Business Analytics Educationcited by 0
- 2026Beyond Static Charts: Can Language and Vision Language Models Generate Interactive Data Visualization Interfaces?cited by 0
- 2026DataCanvas-EDU: An Agentic Framework for Instructor-Guided Synthetic Data Generation in Business Analytics Education17 Sep 2026
- 2026Beyond Static Charts: Can Language and Vision Language Models Generate Interactive Data Visualization Interfaces?11 Aug 2026
- 2025Automated Visualization Makeovers with LLMs21 Jul 2025
- 2024C2: Scalable Auto-Feedback for LLM-based Chart Generation24 Oct 2024
- 2024StuGPTViz: A Visual Analytics Approach to Understand Student-ChatGPT Interactions17 Jul 2024
- 2024Charting the Future of AI in Project-Based Learning: A Co-Design Exploration with Students26 Jan 2024