Jaurez, J., & Radhakrishnan, B., & Altamirano, N. (2022, August), Application of Artificial Intelligence and the Cynefin Framework to establish a Statistical System Prediction and Control (SSPC) in Engineering Education.  Paper presented at 2022 ASEE Annual Conference & Exposition, Minneapolis, MN. 10.18260/1-2--41718

\bibitem{asee_peer_41718}
  Jaurez, J., \& Radhakrishnan, B., \& Altamirano, N. (2022, August), \emph{Application of Artificial Intelligence and the Cynefin Framework to establish a Statistical System Prediction and Control (SSPC) in Engineering Education.}
  Paper presented at 2022 ASEE Annual Conference \& Exposition, Minneapolis, MN.
  10.18260/1-2--41718

James Jaurez, Ben Radhakrishnan, and Nelson Altamirano.  "Application of Artificial Intelligence and the Cynefin Framework to establish a Statistical System Prediction and Control (SSPC) in Engineering Education.".  2022 ASEE Annual Conference & Exposition, Minneapolis, MN, 2022, August.  ASEE Conferences, 2022.  https://peer.asee.org/41718 Internet.  19 Jul, 2025

\bibitem{asee_peer_41718}
  James Jaurez, Ben Radhakrishnan, and Nelson Altamirano.
  "Application of Artificial Intelligence and the Cynefin Framework to establish a Statistical System Prediction and Control (SSPC) in Engineering Education.".
  \emph{2022 ASEE Annual Conference \& Exposition, Minneapolis, MN, 2022, August}.
  ASEE Conferences, 2022.
  https://peer.asee.org/41718 Internet.  19 Jul, 2025

@INPROCEEDINGS{asee_peer_41718
author = "James Jaurez, Ben Radhakrishnan, and Nelson Altamirano"
title = "Application of Artificial Intelligence and the Cynefin Framework to establish a Statistical System Prediction and Control (SSPC) in Engineering Education."
booktitle = "2022 ASEE Annual Conference \& Exposition"
year = "2022"
month = "August"
address = "Minneapolis, MN"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/41718}
number = {10.18260/1-2--41718}
}

TY  - CPAPER
AB  - Using IBM OpenScale for Explainable AI (XAI) decisions in the engineering lifecycle can be made by mapping bias, quality, and drift in complex systems. Traditionally, the primary components of the systems engineering (SE) lifecycle include concept development, requirements engineering, systems architecture, systems design and development, systems integration, test and evaluation, transition operation, and maintenance.  Using the Cynefin framework to identify decision points, most of the SE lifecycle can be viewed as a simple to the complicated domain while functioning at the process and subsystem levels.  However, the decision points and Cynefin domains change, during the deployment, maintenance, and operations as the output of these complex systems changes to determine where and when projects or processes need revision, iterations, or fixes.  Each step of the SE involves data collection and analysis, during project execution through monitoring and control, to verify project objectives and course-correct at the process level for optimal output.  This complexity for large systems is difficult to manage based on the number of inputs and expertise required to determine anomalies.  AI can help solve these issues by mapping large datasets and inputs into predictive outcomes, which can be monitored for variation and quality, while XAI can be utilized to probe and identify the operational complex system for features that significantly affect output and thus be able to rectify before system failures.
AU  - James Jaurez
AU  - Ben Radhakrishnan
AU  - Nelson Altamirano
CY  - Minneapolis, MN
DA  - 2022/08/23
PB  - ASEE Conferences
TI  - Application of Artificial Intelligence and the Cynefin Framework to establish a Statistical System Prediction and Control (SSPC) in Engineering Education.
UR  - https://peer.asee.org/41718
DO  - 10.18260/1-2--41718
ER  -