# What Should Data Science Education Do with Large Language Models? arXiv: https://arxiv.org/abs/2307.02792 ยท HTML: https://mlsanity.com/paper/2307.02792/ ## Key facts - Cited by 13 papers in the map (data through 14 Oct 2026). - Builds on 22 of its 36 references that are in the map. - First posted on arXiv 6 Jul 2023. ## Builds on (most cited first, 22 of 22) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020) - [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023) - [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023) - [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023) - [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023) - [WebGPT: Browser-assisted question-answering with human feedback](https://mlsanity.com/paper/2112.09332/) (2021) - [HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face](https://mlsanity.com/paper/2303.17580/) (2023) - [Extracting Training Data from Diffusion Models](https://mlsanity.com/paper/2301.13188/) (2023) - [Persistent Anti-Muslim Bias in Large Language Models](https://mlsanity.com/paper/2101.05783/) (2021) - [Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scale](https://mlsanity.com/paper/2211.03759/) (2022) - [Identifying and Reducing Gender Bias in Word-Level Language Models](https://mlsanity.com/paper/1904.03035/) (2019) - [Evaluating the Logical Reasoning Ability of ChatGPT and GPT-4](https://mlsanity.com/paper/2304.03439/) (2023) - [GPT detectors are biased against non-native English writers](https://mlsanity.com/paper/2304.02819/) (2023) - [GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models](https://mlsanity.com/paper/2303.10130/) (2023) - [Should ChatGPT be Biased? Challenges and Risks of Bias in Large Language Models](https://mlsanity.com/paper/2304.03738/) (2023) - [On Provable Copyright Protection for Generative Models](https://mlsanity.com/paper/2302.10870/) (2023) - [Is GPT-4 a Good Data Analyst?](https://mlsanity.com/paper/2305.15038/) (2023) - [Boosting Theory-of-Mind Performance in Large Language Models via Prompting](https://mlsanity.com/paper/2304.11490/) (2023) - [Mind meets machine: Unravelling GPT-4's cognitive psychology](https://mlsanity.com/paper/2303.11436/) (2023) - [Data Science: A Comprehensive Overview](https://mlsanity.com/paper/2007.03606/) (2020) - [A Guide to Teaching Data Science](https://mlsanity.com/paper/1612.07140/) (2016) ## Built on by (most cited first, 13 of 13) - [LAMBDA: A Large Model Based Data Agent](https://mlsanity.com/paper/2407.17535/) (2024) - [A Survey on Large Language Model-based Agents for Statistics and Data Science](https://mlsanity.com/paper/2412.14222/) (2024) - [User Modeling in the Era of Large Language Models: Current Research and Future Directions](https://mlsanity.com/paper/2312.11518/) (2023) - [A Law of Next-Token Prediction in Large Language Models](https://mlsanity.com/paper/2408.13442/) (2024) - [Large Language Models in Computer Science Education: A Systematic Literature Review](https://mlsanity.com/paper/2410.16349/) (2024) - [FOKE: A Personalized and Explainable Education Framework Integrating Foundation Models, Knowledge Graphs, and Prompt Engineering](https://mlsanity.com/paper/2405.03734/) (2024) - [Co-audit: tools to help humans double-check AI-generated content](https://mlsanity.com/paper/2310.01297/) (2023) - [Do Large Language Models (Really) Need Statistical Foundations?](https://mlsanity.com/paper/2505.19145/) (2025) - [An Eye for an AI: Evaluating GPT-4o's Visual Perception Skills and Geometric Reasoning Skills Using Computer Graphics Questions](https://mlsanity.com/paper/2410.16991/) (2024) - [Navigating Pitfalls: Evaluating LLMs in Machine Learning Programming Education](https://mlsanity.com/paper/2505.18220/) (2025) - [Artificial intelligence and the transformation of higher education institutions](https://mlsanity.com/paper/2402.08143/) (2024) - [Using ChatGPT for Data Science Analyses](https://mlsanity.com/paper/2404.08480/) (2024) - [VDSAgents: A PCS-Guided Multi-Agent System for Veridical Data Science Automation](https://mlsanity.com/paper/2510.24339/) (2025) ## Abstract (arXiv) The rapid advances of large language models (LLMs), such as ChatGPT, are revolutionizing data science and statistics. These state-of-the-art tools can streamline complex processes. As a result, it reshapes the role of data scientists. We argue that LLMs are transforming the responsibilities of data scientists, shifting their focus from hands-on coding, data-wrangling and conducting standard analyses to assessing and managing analyses performed by these automated AIs. This evolution of roles is reminiscent of the transition from a software engineer to a product manager. We illustrate this transition with concrete data science case studies using LLMs in this paper. These developments necessitate a meaningful evolution in data science education. Pedagogy must now place greater emphasis on cultivating diverse skillsets among students, such as LLM-informed creativity, critical thinking, AI-guided programming. LLMs can also play a significant role in the classroom as interactive teaching and learning tools, contributing to personalized education. This paper discusses the opportunities, resources and open challenges for each of these directions. As with any transformative technology, integrating LLMs into education calls for careful consideration. While LLMs can perform repetitive tasks efficiently, it's crucial to remember that their role is to supplement human intelligence and creativity, not to replace it. Therefore, the new era of data science education should balance the benefits of LLMs while fostering complementary human expertise and innovations. In conclusion, the rise of LLMs heralds a transformative period for data science and its education. This paper seeks to shed light on the emerging trends, potential opportunities, and challenges accompanying this paradigm shift, hoping to spark further discourse and investigation into this exciting, uncharted territory.