The Relevance of Large Language Models for Project Management

The rise of artificial intelligence, particularly the emergence of large language models (LLMs) like ChatGPT, continuously reveals numerous advantages across various domains. However, the area of project management has not yet been sufficiently explored. This study fills the research gap by conducti...

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Main Author: Stamatis Karnouskos
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Open Journal of the Industrial Electronics Society
Subjects:
Online Access:https://ieeexplore.ieee.org/document/10553643/
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author Stamatis Karnouskos
author_facet Stamatis Karnouskos
author_sort Stamatis Karnouskos
collection DOAJ
description The rise of artificial intelligence, particularly the emergence of large language models (LLMs) like ChatGPT, continuously reveals numerous advantages across various domains. However, the area of project management has not yet been sufficiently explored. This study fills the research gap by conducting an empirical evaluation of three well-known LLMs: OpenAI's ChatGPT-3.5 and ChatGPT-4, as well as Google's Bard. The evaluation involves subjecting these LLMs to tests designed to prepare professionals for project management certification by the Project Management Institute. The findings cast a positive light on all three LLMs, with each model achieving scores exceeding 82%. Key insights acquired include: LLMs demonstrate the ability to effectively answer project management certification exam questions; LLMs and project managers should be viewed as a dynamic and complementary partnership; and project management certification should evolve to include an assessment of how project managers collaborate with LLMs to enhance project management.
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spelling doaj-art-110b088f165d48159c2ac2eaf3547ff02025-01-17T00:01:02ZengIEEEIEEE Open Journal of the Industrial Electronics Society2644-12842024-01-01575876810.1109/OJIES.2024.341222210553643The Relevance of Large Language Models for Project ManagementStamatis Karnouskos0https://orcid.org/0000-0003-3519-8232SAP, Walldorf, GermanyThe rise of artificial intelligence, particularly the emergence of large language models (LLMs) like ChatGPT, continuously reveals numerous advantages across various domains. However, the area of project management has not yet been sufficiently explored. This study fills the research gap by conducting an empirical evaluation of three well-known LLMs: OpenAI's ChatGPT-3.5 and ChatGPT-4, as well as Google's Bard. The evaluation involves subjecting these LLMs to tests designed to prepare professionals for project management certification by the Project Management Institute. The findings cast a positive light on all three LLMs, with each model achieving scores exceeding 82%. Key insights acquired include: LLMs demonstrate the ability to effectively answer project management certification exam questions; LLMs and project managers should be viewed as a dynamic and complementary partnership; and project management certification should evolve to include an assessment of how project managers collaborate with LLMs to enhance project management.https://ieeexplore.ieee.org/document/10553643/BardChatGPTGenerative artificial intelligence (AI)large language models (LLMs)project management
spellingShingle Stamatis Karnouskos
The Relevance of Large Language Models for Project Management
IEEE Open Journal of the Industrial Electronics Society
Bard
ChatGPT
Generative artificial intelligence (AI)
large language models (LLMs)
project management
title The Relevance of Large Language Models for Project Management
title_full The Relevance of Large Language Models for Project Management
title_fullStr The Relevance of Large Language Models for Project Management
title_full_unstemmed The Relevance of Large Language Models for Project Management
title_short The Relevance of Large Language Models for Project Management
title_sort relevance of large language models for project management
topic Bard
ChatGPT
Generative artificial intelligence (AI)
large language models (LLMs)
project management
url https://ieeexplore.ieee.org/document/10553643/
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