Radhakrishnan, B. D., & Jaurez, J. J. (2021, July), Explainable Artificial Intelligence (XAI) in Project Management Curriculum: Exploration and Application to Time, Cost, and Risk  Paper presented at 2021 ASEE Virtual Annual Conference Content Access, Virtual Conference. 10.18260/1-2--37135

\bibitem{asee_peer_37135}
  Radhakrishnan, B. D., \& Jaurez, J. J. (2021, July), \emph{Explainable Artificial Intelligence (XAI) in Project Management Curriculum: Exploration and Application to Time, Cost, and Risk}
  Paper presented at 2021 ASEE Virtual Annual Conference Content Access, Virtual Conference.
  10.18260/1-2--37135

Ben D. Radhakrishnan and James Jay Jaurez.  "Explainable Artificial Intelligence (XAI) in Project Management Curriculum: Exploration and Application to Time, Cost, and Risk".  2021 ASEE Virtual Annual Conference Content Access, Virtual Conference, 2021, July.  ASEE Conferences, 2021.  https://peer.asee.org/37135 Internet.  13 Dec, 2024

\bibitem{asee_peer_37135}
  Ben D. Radhakrishnan and James Jay Jaurez.
  "Explainable Artificial Intelligence (XAI) in Project Management Curriculum: Exploration and Application to Time, Cost, and Risk".
  \emph{2021 ASEE Virtual Annual Conference Content Access, Virtual Conference, 2021, July}.
  ASEE Conferences, 2021.
  https://peer.asee.org/37135 Internet.  13 Dec, 2024

@INPROCEEDINGS{asee_peer_37135
author = "Ben D. Radhakrishnan and James Jay Jaurez"
title = "Explainable Artificial Intelligence (XAI) in Project Management Curriculum: Exploration and Application to Time, Cost, and Risk"
booktitle = "2021 ASEE Virtual Annual Conference Content Access"
year = "2021"
month = "July"
address = "Virtual Conference"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/37135}
number = {10.18260/1-2--37135}
}

TY  - CPAPER
AB  - Artificial Intelligence (AI) technology is rapidly being adopted in all facets of products and services. Many aspects of AI technology have been adopted at different levels in areas like robotics, autonomous vehicles, retail, and virtual agents. AI technology has yet to be exploited to adopt many Tools, Techniques, and Procedures (TTP) that can help Project Management’s (PM) planning, prediction, and performance. Current PM education uses traditional deterministic and simulation models and far away from using AI. Adoption of AI into PM will deliver processes that will be superior with new features such as machine learning and predictive data analysis for better decisions and higher productivity. Project Management Institute has recognized the significance of AI technology and recommends its teaching and adoption to real world PM. 
The notion of educational AI has been closely related to the burgeoning industry need for explainable AI, known as XAI.  With XAI, an organization can provide AI solutions with great transparency and trust.  For educators, this move to XAI for PM provides a bridge to teaching and learning for the same purpose of transparency and trust, as well as the unique opportunity to validate traditional engineering methods.
This research will explore the adoption and applicability of AI technology of automation, data analysis, machine learning, and prediction to PM education and training in two key knowledge areas of PMBOK’s (Project Management Book of Knowledge), namely, time and cost which are also associated with risk. Traditional PM processes will be discussed and compared to AI-enabled TTP demonstrating AI advantage. AI’s machine learning will be illustrated using a schedule time dataset to facilitate time prediction on a well-known IBM Watson platform confirming verification and trust in the process. This research culminates with recommendations for inclusions of AI principles and practices in PM education and an exploration of future AI applications for other PMBOK’s knowledge areas with optimal solutions.



AU  - Ben D. Radhakrishnan
AU  - James Jay Jaurez
CY  - Virtual Conference
DA  - 2021/07/26
PB  - ASEE Conferences
TI  - Explainable Artificial Intelligence (XAI) in Project Management Curriculum: Exploration and Application to Time, Cost, and Risk
UR  - https://peer.asee.org/37135
DO  - 10.18260/1-2--37135
ER  -