Why Should Statisticians Who Primarily Work with Structured Data Learn to Use Large Language Models?
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| Title: | Why Should Statisticians Who Primarily Work with Structured Data Learn to Use Large Language Models? |
|---|---|
| Language: | English |
| Authors: | Kévin Allan Sales Rodrigues (ORCID |
| Source: | Teaching Statistics: An International Journal for Teachers. 2026 48(1):12-18. |
| Availability: | Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us |
| Peer Reviewed: | Y |
| Page Count: | 7 |
| Publication Date: | 2026 |
| Document Type: | Journal Articles Reports - Evaluative |
| Descriptors: | Statistics Education, Artificial Intelligence, Natural Language Processing, Data Analysis, Classification, Technology Integration, Data Science, Computation, Ethics, Innovation |
| DOI: | 10.1111/test.70005 |
| ISSN: | 0141-982X 1467-9639 |
| Abstract: | The advent of Large Language Models (LLMs) represents a paradigm shift in data analysis, bridging the gap between structured and unstructured data. This paper explores the transformative potential of LLMs in statistics, focusing on their ability to preprocess unstructured textual data and streamline tasks such as classification, summarization, and feature extraction. We argue for integrating LLMs into the statistics curriculum to prepare students for the complexities of modern data science. The discussion encompasses practical challenges, including computational demands, ethical considerations, and the nuances of incorporating LLM outputs into traditional workflows. By reimagining statistical education and practice in the era of generative AI, we advocate for a complementary approach that balances innovation with foundational methodologies, fostering a new generation of adaptable and skilled statisticians. |
| Abstractor: | As Provided |
| Entry Date: | 2026 |
| Accession Number: | EJ1494355 |
| Database: | ERIC |
| Abstract: | The advent of Large Language Models (LLMs) represents a paradigm shift in data analysis, bridging the gap between structured and unstructured data. This paper explores the transformative potential of LLMs in statistics, focusing on their ability to preprocess unstructured textual data and streamline tasks such as classification, summarization, and feature extraction. We argue for integrating LLMs into the statistics curriculum to prepare students for the complexities of modern data science. The discussion encompasses practical challenges, including computational demands, ethical considerations, and the nuances of incorporating LLM outputs into traditional workflows. By reimagining statistical education and practice in the era of generative AI, we advocate for a complementary approach that balances innovation with foundational methodologies, fostering a new generation of adaptable and skilled statisticians. |
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| ISSN: | 0141-982X 1467-9639 |
| DOI: | 10.1111/test.70005 |
