Inclusive Leadership in the Age of AI: A Dataset and Comparative Study of LLMs vs. Real-Life Leaders in Workplace Action Planning

Vindhya Singh, Sabine Schulte im Walde, Ksenia Keplinger


Abstract
Generative Large Language Models have emerged as useful tools, reshaping professional workflows. However, their efficacy in inherently complex and human-centric tasks such as leadership and strategic planning remains underexplored. In this interdisciplinary study, we present a novel dataset and compare LLMs and human leaders in the context of workplace action planning, specifically focusing on translating the abstract idea of inclusion into actionable SMART goals. We developed the Leader Success Bot, a script-based chatbot co-designed with domain experts, to guide more than 250 real-life leaders in generating inclusive workplace action plans. We systematically prompted seven state-of-the-art chat-based LLMs to perform the same task using the socio-demographic data of real-life leaders and instructions co-developed with domain experts. Our publicly released dataset enables direct comparison between human and LLM-generated workplace action plans, offering insights into their respective strengths, biases, and limitations. Our findings highlight critical gaps and opportunities for LLMs in leadership applications, fostering interdisciplinary collaboration and NLP applications.
Anthology ID:
2025.findings-emnlp.1075
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2025
Month:
November
Year:
2025
Address:
Suzhou, China
Editors:
Christos Christodoulopoulos, Tanmoy Chakraborty, Carolyn Rose, Violet Peng
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
19732–19753
Language:
URL:
https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.1075/
DOI:
10.18653/v1/2025.findings-emnlp.1075
Bibkey:
Cite (ACL):
Vindhya Singh, Sabine Schulte im Walde, and Ksenia Keplinger. 2025. Inclusive Leadership in the Age of AI: A Dataset and Comparative Study of LLMs vs. Real-Life Leaders in Workplace Action Planning. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 19732–19753, Suzhou, China. Association for Computational Linguistics.
Cite (Informal):
Inclusive Leadership in the Age of AI: A Dataset and Comparative Study of LLMs vs. Real-Life Leaders in Workplace Action Planning (Singh et al., Findings 2025)
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PDF:
https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.1075.pdf
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