Abstract:

Many undergraduate, graduate, and professional degree programs include a foundation course in project management (PM). These courses typically cover topics such as project planning, risk management, stakeholder engagement, budgeting, and quality control. Students are often taught methodologies like Waterfall and Agile, and learn to use PM software. Soft skills, such as team collaboration and effective communication, are also emphasized.

Nowadays, Artificial Intelligence (AI), and especially Generative AI (GenAI), is being used more and more in different industries in a wide range of use cases. The project manager's role and responsibilities are affected by AI, too. For example, AI-driven tools can automate tasks such as scheduling meetings, sending reminders, and tracking project timelines. GenAI can assist in creating detailed project proposals, support stakeholder management or risk assessments, and even comprehensive project documentation. These changes need to be reflected in project management courses.

This paper shows how a “classic” PM course can be extended so that students understand how AI can enhance PM processes and are trained to effectively use AI tools in PM tasks. The module description of a publicly available introductory course in PM will be used as the basis. We extend the learning objectives and suggest additional learning experiences. To provide students with a comprehensive understanding of AI in PM, we will explore interrelated perspectives - technological, socio-cultural, and user-oriented - using the Dagstuhl Triangle, a framework for structuring discussions on education in a digital world.

First, we consider the technological perspective. Students should get a basic knowledge of how AI works in this particular context (in our contribution, we assume a basic knowledge of AI that could be taught in an introductory course on digital skills, for example). For instance, students learn which tasks can be supported by AI, which do not require AI but "normal" digitalization, and which are not suitable.

The second is the socio-cultural perspective. This means that students learn to assess the impact of using AI in PM tasks. For example, they could discuss how it affects the project team and team dynamics when meetings are recorded, and an AI provides a transcript and even writes the meeting minutes and assigns tasks to team members. Ethical considerations regarding the use of AI in PM should be discussed with concrete examples from the field.

Third, the user-oriented perspective emphasizes the selection and the use of appropriate AI-based tools for PM tasks. We will outline what functionality of current AI-based tools could be used by students in case studies or practical exercises. Students should also learn how to interpret AI-generated insights.

The approach presented in this paper provides a concrete framework that educators and curriculum developers could use as a starting point to integrate knowledge about the use of AI in PM into a standard project management curriculum. It equips students not only with the technical skills needed to efficiently use AI tools, but also with the critical thinking skills needed to understand the impact of AI on PM.

Keywords:

Artificial intelligence, project management, teaching methods, generative artificial intelligence.