大型语言模型对项目管理的意义

IF 5.2 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Stamatis Karnouskos
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引用次数: 0

摘要

人工智能的兴起,尤其是像 ChatGPT 这样的大型语言模型(LLM)的出现,不断显示出各个领域的众多优势。然而,项目管理领域尚未得到充分探索。本研究通过对三种著名的 LLM 进行实证评估,填补了这一研究空白:它们是 OpenAI 的 ChatGPT-3.5 和 ChatGPT-4,以及 Google 的 Bard。评估工作包括对这些 LLM 进行测试,测试目的是让专业人员为获得项目管理协会的项目管理认证做好准备。评估结果对所有三个 LLM 都给予了肯定,每个模型的得分都超过了 82%。获得的主要见解包括法律硕士展示了有效回答项目管理认证考试问题的能力;法律硕士和项目经理应被视为一种动态和互补的合作伙伴关系;项目管理认证应发展到包括对项目经理如何与法律硕士合作以加强项目管理的评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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 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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来源期刊
IEEE Open Journal of the Industrial Electronics Society
IEEE Open Journal of the Industrial Electronics Society ENGINEERING, ELECTRICAL & ELECTRONIC-
CiteScore
10.80
自引率
2.40%
发文量
33
审稿时长
12 weeks
期刊介绍: The IEEE Open Journal of the Industrial Electronics Society is dedicated to advancing information-intensive, knowledge-based automation, and digitalization, aiming to enhance various industrial and infrastructural ecosystems including energy, mobility, health, and home/building infrastructure. Encompassing a range of techniques leveraging data and information acquisition, analysis, manipulation, and distribution, the journal strives to achieve greater flexibility, efficiency, effectiveness, reliability, and security within digitalized and networked environments. Our scope provides a platform for discourse and dissemination of the latest developments in numerous research and innovation areas. These include electrical components and systems, smart grids, industrial cyber-physical systems, motion control, robotics and mechatronics, sensors and actuators, factory and building communication and automation, industrial digitalization, flexible and reconfigurable manufacturing, assistant systems, industrial applications of artificial intelligence and data science, as well as the implementation of machine learning, artificial neural networks, and fuzzy logic. Additionally, we explore human factors in digitalized and networked ecosystems. Join us in exploring and shaping the future of industrial electronics and digitalization.
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