Transformative teaching in management statistics: Integrating real‐world experiences and artificial intelligence

Kate Z. Williams & Tatiana Rudchenko

Decision Sciences Journal of Innovative Education2026https://doi.org/10.1111/dsji.70019article
AJG 1ABDC C
Weight
0.50

What the paper says

Abstract Traditional lecture‐based teaching methods can fall short of preparing students for the complexities of modern workplaces. With the arrival of generative AI (GenAI) in both workplaces and academia, faculty must choose whether and how to introduce students to the powers of artificial intelligence. This teaching brief explores the implementation of a series of scaffolded lessons using GenAI in a quantitative, undergraduate operations management course. Leveraging Fink's theory of significant learning and Kuh's High Impact Practices (HIPs), the initiative integrates real‐world applications of artificial intelligence to enhance student learning and career readiness. This brief explains how the teaching innovation allowed students to compare the practical value of Excel versus ChatGPT in conducting statistical analysis. Industry experts and a site visit provided insights into the use of generative AI in business contexts. Results indicate that these course enhancements elevate student interest in operations management and business analytics and offer a model for future improvements in business education.

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https://doi.org/https://doi.org/10.1111/dsji.70019

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@article{kate2026,
  title        = {{Transformative teaching in management statistics: Integrating real‐world experiences and artificial intelligence}},
  author       = {Kate Z. Williams & Tatiana Rudchenko},
  journal      = {Decision Sciences Journal of Innovative Education},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1111/dsji.70019},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.