IDEAS home Printed from https://ideas.repec.org/a/inm/orinte/v55y2025i4p319-343.html

A Tutorial on Teaching Data Analytics with Generative AI

Author

Listed:
  • Robert L. Bray

    (Department of Operations, Kellogg School of Management, Northwestern University, Evanston, Illinois 60208)

Abstract

This tutorial addresses the challenge of incorporating large language models, such as ChatGPT, in a data analytics class. It details several new in-class and out-of-class teaching techniques enabled by artificial intelligence (AI). Here are three examples. Instructors can parallelize instruction by having students interact with different custom-made GPTs to learn different parts of an analysis and then teach each other what they learned from their GPTs. Instructors can turn problem sets into AI tutoring sessions: a custom-made GPT guides a student through the problems and the student uploads the chatlog for their homework submission. Instructors can assign different labs to each section of a class and have each section create AI assistants to help the other sections work through their labs. This tutorial advocates the natural language programming (NLP) paradigm, in which students articulate desired data transformations with a spoken language, such as English, and then use AI to generate the corresponding computer code. Students can wrangle data more effectively with NLP than with Excel.

Suggested Citation

  • Robert L. Bray, 2025. "A Tutorial on Teaching Data Analytics with Generative AI," Interfaces, INFORMS, vol. 55(4), pages 319-343, July.
    • Handle: RePEc:inm:orinte:v:55:y:2025:i:4:p:319-343
    DOI: 10.1287/inte.2023.0053
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/inte.2023.0053
    Download Restriction: no
    File URL: https://libkey.io/10.1287/inte.2023.0053?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Robert L. Bray & Yuliang Yao & Yongrui Duan & Jiazhen Huo, 2019. "Ration Gaming and the Bullwhip Effect," Operations Research, INFORMS, vol. 67(2), pages 453-467, March.
    2. Robert L. Bray & Ioannis Stamatopoulos, 2022. "Menu Costs and the Bullwhip Effect: Supply Chain Implications of Dynamic Pricing," Operations Research, INFORMS, vol. 70(2), pages 748-765, March.
    3. Michael F. Gorman, 2021. "Contextual Complications in Analytical Modeling: When the Problem is Not the Problem," Interfaces, INFORMS, vol. 51(4), pages 245-261, July.
    4. Christian C. Blanco & Felipe Caro & Charles J. Corbett, 2019. "Managing Safety‐Related Disruptions: Evidence from the U.S. Nuclear Power Industry," Risk Analysis, John Wiley & Sons, vol. 39(10), pages 2197-2213, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. QU, Zhan & RAFF, Horst, 2023. "Two-part tariffs, inventory stockpiling, and the bullwhip effect," European Journal of Operational Research, Elsevier, vol. 308(1), pages 201-214.
    2. Minje Park & Anita L. Carson & Erin R. Fox & Rena M. Conti, 2024. "Stockpiling at the Onset of the COVID-19 Pandemic: An Empirical Analysis of National Prescription Drug Sales and Prices," Management Science, INFORMS, vol. 70(10), pages 6483-6501, October.
    3. Karsten Hansen & Kanishka Misra & Robert Evan Sanders, 2024. "Uninformed Choices in Perishables," Marketing Science, INFORMS, vol. 43(4), pages 751-777, July.
    4. Michael F. Gorman, 2025. "An Empirical Investigation into the Prevalence and Impacts of Complicating Environmental Factors in Published Interfaces / IJAA Projects," Interfaces, INFORMS, vol. 55(4), pages 279-295, July.
    5. Schiffels, Sebastian & Jost, Christian, 2026. "The role of scarcity behavior in inventory management," European Journal of Operational Research, Elsevier, vol. 328(1), pages 78-90.
    6. Aguirregabiria, Victor & Guiton, Francis, 2022. "Decentralized Decision-Making in Retail Chains: Evidence from Inventory Management," CEPR Discussion Papers 17149, Centre for Economic Policy Research.
    7. Mehran Navabi-Shirazi & Mohamed El Tonbari & Natashia Boland & Dima Nazzal & Lauren N. Steimle, 2022. "Multicriteria Course Mode Selection and Classroom Assignment Under Sudden Space Scarcity," Manufacturing & Service Operations Management, INFORMS, vol. 24(6), pages 3252-3268, November.
    8. Nikolay Osadchiy & William Schmidt & Jing Wu, 2021. "The Bullwhip Effect in Supply Networks," Management Science, INFORMS, vol. 67(10), pages 6153-6173, October.
    9. Dayton Steele & Seyed Morteza Emadi & Saravanan Kesavan, 2025. "Intertemporal Pricing with Resellers: An Empirical Study of Product Drops," Management Science, INFORMS, vol. 71(9), pages 7263-7285, September.
    10. Erkan Bayraktar & Kazim Sari & Ekrem Tatoglu & Selim Zaim & Dursun Delen, 2020. "Assessing the supply chain performance: a causal analysis," Annals of Operations Research, Springer, vol. 287(1), pages 37-60, April.
    11. Nitin Bakshi & Heikki Peura, 2022. "Prevent or Report? Managing Near Misses for Safer Operations," Manufacturing & Service Operations Management, INFORMS, vol. 24(4), pages 2064-2080, July.
    12. Marshall Fisher & Marcelo Olivares & Bradley R. Staats, 2020. "Why Empirical Research Is Good for Operations Management, and What Is Good Empirical Operations Management?," Manufacturing & Service Operations Management, INFORMS, vol. 22(1), pages 170-178, January.
    13. Seungrae Lee & Seung Jae Park & Sridhar Seshadri, 2023. "Variations of the Bullwhip Effect Across Foreign Subsidiaries," Manufacturing & Service Operations Management, INFORMS, vol. 25(1), pages 1-18, January.
    14. Dipankar Das, 2025. "Assortment planning with trustworthy reviews," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 24(5), pages 471-487, October.
    15. David Simchi-Levi & Yunzong Xu & Jinglong Zhao, 2025. "Blind Network Revenue Management and Bandits with Knapsacks Under Limited Switches," Operations Research, INFORMS, vol. 73(5), pages 2496-2514, September.
    16. Ecenur Oguz & Robert L. Bray, 2026. "Training Neural Networks Embedded in Dynamic Discrete Choice Models," Papers 2604.09736, arXiv.org.
    17. Michael Greenberg & Anthony Cox & Vicki Bier & Jim Lambert & Karen Lowrie & Warner North & Michael Siegrist & Felicia Wu, 2020. "Risk Analysis: Celebrating the Accomplishments and Embracing Ongoing Challenges," Risk Analysis, John Wiley & Sons, vol. 40(S1), pages 2113-2127, November.
    18. Villa, Sebastián, 2022. "Competing for supply and demand: Understanding retailers' ordering decisions," International Journal of Production Economics, Elsevier, vol. 244(C).
    19. Alper Saricioglu & Mujde Erol Genevois & Michele Cedolin, 2025. "Impact of COVID-19 on The Bullwhip Effect Across U.S. Industries," Papers 2506.06368, arXiv.org.
    20. Zhaohui (Zoey) Jiang & Jun Li & Dennis Zhang, 2025. "A High-Dimensional Choice Model for Online Retailing," Management Science, INFORMS, vol. 71(4), pages 3320-3339, April.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:orinte:v:55:y:2025:i:4:p:319-343. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.