3130. A New Perspective on the Human-Machine Interface Design: Transformative Personalized Learning Efficiency in AI-Powered Active Learning Settings
Invited abstract in session WA-18: OR Education 1, stream OR Education.
Wednesday, 8:30-10:00Room: Esther Simpson 2.09
Authors (first author is the speaker)
| 1. | Nina Kajiji
|
| Computer Science and Statistics, University of Rhode Island, and The NKD Group, Inc. | |
| 2. | Gordon Dash
|
| Finance and Decision Sciences, University of Rhode Island |
Abstract
This study explores the human-machine interface (HMI) as a transformative mechanism that impacts the use of AI and OR in education. HMI methods crafted in the age of generative AI can leverage personalized and adaptive learning systems, add to efficient classroom management, and support ethical principles. The HMI framework described in this study utilizes WinORS (Winidows Operational Research Software), a computational tool integrating AI and Operations Research, to enhance pedagogy across diverse disciplines. Two case studies are featured, one in computational finance and another in neuroethics. The finance case study demonstrates WinORS' capabilities, such as integrating a multi-target neural network and multiple-objective goal programming to optimize complex investment portfolios. The neuroscience case study details how a neural network mapping of the rodent brain is used to formulate a multiple-goal OR assignment model to seek efficient prosocial outcomes in an urban housing application. The WinORS support for experiential lab solutions and branded digital credentials demonstrates its scalable framework for integrating a unique human-machine interface to effectively incorporate AI and Operations Research principles into modern classroom pedagogy.
Keywords
- OR in Education
- Artificial Intelligence
- Mathematical Programming
Status: accepted
Back to the list of papers