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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 0000-0003-4925-5883)
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
FullText Text:
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  Data: <searchLink fieldCode="SO" term="%22Teaching+Statistics%3A+An+International+Journal+for+Teachers%22"><i>Teaching Statistics: An International Journal for Teachers</i></searchLink>. 2026 48(1):12-18.
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  Data: 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
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  Data: 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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