Inclusive Leadership in the Age of AI: A Dataset and Comparative Study of LLMs vs. Real-Life Leaders in Workplace Action Planning
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 under-explored. In this interdisciplinary study, we present a novel dataset and compare LLMs and human leaders in the context of work-place action planning, specifically focusing on translating the abstract idea of inclusion into actionable SMART goals. We developed the Leader Success Bot, a script-based chat-bot 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 in-sights 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.
| Author(s): | Singh, Vindhya and Schulte im Walde, Sabine and Keplinger, Ksenia |
| Book Title: | Findings of the Association for Computational Linguistics: EMNLP 2025 |
| Pages: | 19732-19753 |
| Year: | 2025 |
| Month: | November |
| Day: | 09 |
| Publisher: | Association for Computational Linguistics |
| BibTeX Type: | Conference Paper (conference) |
| Address: | Suzhou, China |
| DOI: | 10.18653/v1/2025.findings-emnlp.1075 |
| Event Name: | Empirical Methods in Natural Language Processing |
| Event Place: | Suzhou, China |
| State: | Published |
| URL: | https://aclanthology.org/anthology-files/anthology-files/pdf/findings/2025.findings-emnlp.1075.pdf |
| Organization: | Association for Computational Linguistics |
BibTeX
@conference{InclusiveLeadershipintheAgeofAISingh2025,
title = {Inclusive Leadership in the Age of AI: A Dataset and Comparative Study of LLMs vs. Real-Life Leaders in Workplace Action Planning},
booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2025},
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 under-explored. In this interdisciplinary study, we present a novel dataset and compare LLMs and human leaders in the context of work-place action planning, specifically focusing on translating the abstract idea of inclusion into actionable SMART goals. We developed the Leader Success Bot, a script-based chat-bot 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 in-sights 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.},
pages = {19732-19753},
publisher = {Association for Computational Linguistics},
organization = {Association for Computational Linguistics},
address = {Suzhou, China},
month = nov,
year = {2025},
author = {Singh, Vindhya and Schulte im Walde, Sabine and Keplinger, Ksenia},
doi = {10.18653/v1/2025.findings-emnlp.1075},
url = {https://aclanthology.org/anthology-files/anthology-files/pdf/findings/2025.findings-emnlp.1075.pdf},
month_numeric = {11}
}