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Bringing Historical Management Theories to Life: An Experiential Exercise Using Generative AI

Mustafa Akben et al.

Management Teaching Review2025https://doi.org/10.1177/23792981241304632article
AJG 2ABDC C
Weight
0.41

What the paper says

Foundational management theories, such as the Principles of Scientific Management, Bureaucratic Management, or Systems Theory, provide frameworks for understanding and organizing the extensive body of management knowledge. Despite their significance, students often find it difficult to grasp and apply historical concepts in today’s workplace. In this experiential exercise, we use generative artificial intelligence (AI) to create an interactive chatbot through which students “interview” historical figures. Through this exercise, students achieve four key learning objectives: (a) demonstrating a critical understanding of historical management frameworks; (b) analyzing how historical, social, and economic contexts influenced management thinking; (c) evaluating the applicability of historical management frameworks to contemporary business settings; and (d) critically analyzing and interpreting information generated by AI tools to enhance AI literacy. The activity includes tailored open-access chatbots and resources to assist these learning outcomes across various class formats.

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https://doi.org/https://doi.org/10.1177/23792981241304632

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@article{mustafa2025,
  title        = {{Bringing Historical Management Theories to Life: An Experiential Exercise Using Generative AI}},
  author       = {Mustafa Akben et al.},
  journal      = {Management Teaching Review},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1177/23792981241304632},
}

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Bringing Historical Management Theories to Life: An Experiential Exercise Using Generative AI

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Evidence weight

0.41

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

F · citation impact0.25 × 0.4 = 0.10
M · momentum0.55 × 0.15 = 0.08
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.