# ChatGPT in Data Visualization Education: A Student Perspective

arXiv: https://arxiv.org/abs/2405.00748 · HTML: https://mlsanity.com/paper/2405.00748/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 57 references that are in the map.
- First posted on arXiv 1 May 2024.

## Builds on (most cited first, 19 of 19)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Grounded Copilot: How Programmers Interact with Code-Generating Models](https://mlsanity.com/paper/2206.15000/) (2022)
- [Programming Is Hard -- Or at Least It Used to Be: Educational Opportunities And Challenges of AI Code Generation](https://mlsanity.com/paper/2212.01020/) (2022)
- [CodeAid: Evaluating a Classroom Deployment of an LLM-based Programming Assistant that Balances Student and Educator Needs](https://mlsanity.com/paper/2401.11314/) (2024)
- [A Large-Scale Survey on the Usability of AI Programming Assistants: Successes and Challenges](https://mlsanity.com/paper/2303.17125/) (2023)
- [How Novices Use LLM-Based Code Generators to Solve CS1 Coding Tasks in a Self-Paced Learning Environment](https://mlsanity.com/paper/2309.14049/) (2023)
- [Students' Voices on Generative AI: Perceptions, Benefits, and Challenges in Higher Education](https://mlsanity.com/paper/2305.00290/) (2023)
- [An Empirical Study on Usage and Perceptions of LLMs in a Software Engineering Project](https://mlsanity.com/paper/2401.16186/) (2024)
- [The Effects of Generative AI on Computing Students' Help-Seeking Preferences](https://mlsanity.com/paper/2401.02262/) (2024)
- [Generating Automatic Feedback on UI Mockups with Large Language Models](https://mlsanity.com/paper/2403.13139/) (2024)
- [How Good is ChatGPT in Giving Advice on Your Visualization Design?](https://mlsanity.com/paper/2310.09617/) (2023)
- [Beyond Generating Code: Evaluating GPT on a Data Visualization Course](https://mlsanity.com/paper/2306.02914/) (2023)
- [The Rise of the AI Co-Pilot: Lessons for Design from Aviation and Beyond](https://mlsanity.com/paper/2311.14713/) (2023)
- [ChatGPT in the Classroom: An Analysis of Its Strengths and Weaknesses for Solving Undergraduate Computer Science Questions](https://mlsanity.com/paper/2304.14993/) (2023)
- [ChatGPT, Can You Generate Solutions for my Coding Exercises? An Evaluation on its Effectiveness in an undergraduate Java Programming Course](https://mlsanity.com/paper/2305.13680/) (2023)
- [Exploring the Role of AI Assistants in Computer Science Education: Methods, Implications, and Instructor Perspectives](https://mlsanity.com/paper/2306.03289/) (2023)
- ["It's not like Jarvis, but it's pretty close!" -- Examining ChatGPT's Usage among Undergraduate Students in Computer Science](https://mlsanity.com/paper/2311.09651/) (2023)
- [Generative AI in Education: A Study of Educators' Awareness, Sentiments, and Influencing Factors](https://mlsanity.com/paper/2403.15586/) (2024)

## Built on by (most cited first, 3 of 3)

- [Large Language Models in Computer Science Education: A Systematic Literature Review](https://mlsanity.com/paper/2410.16349/) (2024)
- [ParaView-MCP: An Autonomous Visualization Agent with Direct Tool Use](https://mlsanity.com/paper/2505.07064/) (2025)
- [Transformers Utilization in Chart Understanding: A Review of Recent Advances & Future Trends](https://mlsanity.com/paper/2410.13883/) (2024)

## Abstract (arXiv)

Unlike traditional educational chatbots that rely on pre-programmed responses, large-language model-driven chatbots, such as ChatGPT, demonstrate remarkable versatility to serve as a dynamic resource for addressing student needs from understanding advanced concepts to solving complex problems. This work explores the impact of such technology on student learning in an interdisciplinary, project-oriented data visualization course. Throughout the semester, students engaged with ChatGPT across four distinct projects, designing and implementing data visualizations using a variety of tools such as Tableau, D3, and Vega-lite. We collected conversation logs and reflection surveys after each assignment and conducted interviews with selected students to gain deeper insights into their experiences with ChatGPT. Our analysis examined the advantages and barriers of using ChatGPT, students' querying behavior, the types of assistance sought, and its impact on assignment outcomes and engagement. We discuss design considerations for an educational solution tailored for data visualization education, extending beyond ChatGPT's basic interface.