Optimizing Virtual Learning: Advanced Recommendations for an AI Teaching Assistant
- Conference
- Location
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Portland, Oregon
- Publication Date
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June 23, 2024
- Start Date
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June 23, 2024
- End Date
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July 12, 2024
- Conference Session
- Tagged Division
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Industrial Engineering Division (IND)
- Permanent URL
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https://peer.asee.org/47822
Paper Authors
Md Abdullah The University of Texas at Arlington
Md Abdullah is a PhD student of Industrial, Manufacturing and Systems Engineering department at the University of Texas at Arlington. His research interest are Human Factors, Safety, Augmented Reality, Virtual Reality.
Gowtham Nageshwara rao The University of Texas at Arlington
Gowtham Nageshwara Rao is an industrial engineer at Spirit Aerosystems in Wichita, having graduated with a master's in engineering management from the University of Texas at Arlington. Specializing in capacity analysis, simulation, and Lean methodologies, he optimizes production workflows and drives cost reduction initiatives with a focus on operational excellence. With a keen interest in AI applications, particularly in aiding production, operations, and manufacturing engineering.
Faith Lauren Sowell The University of Texas at Arlington
Faith Lauren Sowell is an Undergraduate Student of Computer Engineering at the University of Texas at Arlington. She is the Lead Virtual Reality Developer for the Human Factors Laboratory. Her research interests include virtual reality as a training and teaching aide, and transportation research. She is expected to graduate in the fall of 2024.
Vibhav Nirmal The University of Texas at Arlington
Vibhav Nirmal is a graduate student of Computer Science department at the University of Texas at Arlington. His research focuses on the applications of Robotics and Virtual Reality in conjunction with Machine Vision, particularly exploring their impact on automation and data analysis.
Shuchisnigdha Deb The University of Texas at Arlington
SHUCHISNIGDHA DEB is an Assistant Professor in the Department of Industrial, Manufacturing, and Systems Engineering at The University of Texas at Arlington. She received her PhD in Industrial and Systems Engineering from Mississippi State University and MS in Industrial and Management Engineering from Montana State University.
Abstract
Virtual reality (VR) has emerged as a promising tool for educating students on complex skills, providing a safe and immersive environment for practice, and learning from mistakes. However, VR training can be challenging, requiring students to navigate around the virtual environment and interact with objects in an effective way. This study presents the development of an AI teaching assistant to help students learn 3D printing operations using VR. Students’ gaze behavior and performance measures, such as task completion time and accuracy, have been tracked to develop the AI teaching assistant. The assistant provides personalized recommendations and support to help students improve their 3D printing skills. The findings of this study will have significant implications for the advancement of engineering education, providing a safer and more innovative learning experience for students. The research impact extends beyond 3D printing, with the methodology for the development of AI teaching assistants across different educational domains.
Abdullah, M., & Nageshwara rao, G., & Sowell, F. L., & Nirmal, V., & Deb, S. (2024, June), Optimizing Virtual Learning: Advanced Recommendations for an AI Teaching Assistant Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. https://peer.asee.org/47822
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