Board 448: Exploring the Use of Artificial Intelligence in Racing Games in Engineering Education: A Systematic Literature Review
- 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
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Educational Research and Methods Division (ERM) Poster Session
- Tagged Division
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Educational Research and Methods Division (ERM)
- Permanent URL
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https://strategy.asee.org/47040
Paper Authors
An Nguyen University of Oklahoma
An Nguyen is a student in the Gallogly College of Engineering at The University of Oklahoma. He is pursuing a double degree in Computer Science and Math and is hoping to graduate with both Fall '25. Afterwards, An is hoping to pursue a one-year Master's program in Computer Science to further expand his knowledge in the technological field. Passionate about Artificial Intelligence (AI) and Machine Learning (ML), An's academic and research interests are focused on leveraging these advanced technologies to drive innovation and solve complex challenges. He is particularly intrigued by the transformative potential of AI in various industries and improving the quality of life. As he advances in his studies, An remains committed to engaging deeply with his subjects, aiming to make significant contributions to the field and explore new technological frontiers.
Javeed Kittur
University of Oklahoma
orcid.org/0000-0001-6132-7304
Dr. Kittur is an Assistant Professor in the Gallogly College of Engineering at The University of Oklahoma. He completed his Ph.D. in Engineering Education Systems and Design program from Arizona State University, 2022. He received a bachelor’s degree in Electrical and Electronics Engineering and a Master’s in Power Systems from India in 2011 and 2014, respectively. He has worked with Tata Consultancy Services as an Assistant Systems Engineer from 2011–2012 in India. He has worked as an Assistant Professor (2014–2018) in the department of Electrical and Electronics Engineering, KLE Technological University, India. He is a certified IUCEE International Engineering Educator. He was awarded the ’Ing.Paed.IGIP’ title at ICTIEE, 2018. He is serving as an Associate Editor of the Journal of Engineering Education Transformations (JEET).
He is interested in conducting engineering education research, and his interests include student retention in online and in-person engineering courses/programs, data mining and learning analytics in engineering education, broadening student participation in engineering, faculty preparedness in cognitive, affective, and psychomotor domains of learning, and faculty experiences in teaching online courses. He has published papers at several engineering education research conferences and journals. Particularly, his work is published in the International Conference on Transformations in Engineering Education (ICTIEE), American Society for Engineering Education (ASEE), Computer Applications in Engineering Education (CAEE), International Journal of Engineering Education (IJEE), Journal of Engineering Education Transformations (JEET), and IEEE Transactions on Education. He is also serving as a reviewer for a number of conferences and journals focused on engineering education research.
Abstract
Over the past couple of years, artificial intelligence (AI) has undergone numerous breakthroughs and advancements, developing and refining itself into a remarkable and versatile technological asset in various different fields and domains in automated machinery. As AI continues to evolve, it emerges as a pivotal tool in research development and in different fields of learning, including engineering education in racing games. This paper presents a systematic literature review (SLR) that delves into the subject of artificial intelligence and machine learning and how it can be used to optimize the performance of AI agents in online racing games and simulators such as Track Mania, Gran Turismo, and The Open Racing Simulator (TORCS). The usage of these racing simulators is crucial as they not only provide a platform for entertainment but also a safe and reliable simulator for researchers and developers to test different AI/ML algorithms such as reinforcement learning (RL), providing a cost-effective and risk-free learning environment for users.
The SLR focuses on the development and optimization of AI agents and finds from research in online engineering education. Information for this SLR was gathered from six different online scholarly sources including Google Scholar, Web of Science, IEEE Explorer, Engineering Village, EBSCOhost, ScienceDirect, and Wiley Online Library. The search process to ensure the inclusion of only relevant articles included screening by title, screening by abstract, screening by full-text, and a full synthesis of each targeted article. This methodological approach involving the combination of multiple scholarly sources and the utilization of a systematic screening process ensures a set of robust and reliable articles in providing a comprehensive literature review of the current state of AI in online racing games and its implications in engineering education. A total of twenty articles published between 2013 to 2023 met inclusion criteria, and the synthesis of these articles highlighted four themes: agent performance optimization, AI technologies applications, machine learning paradigms, and the racing simulation environment. Using these identified themes, the SLR explores the integration of AI in online racing games and simulators, shedding light on the intricate interplay and dynamics between AI technologies and the virtual racing environment.
Nguyen, A., & Kittur, J. (2024, June), Board 448: Exploring the Use of Artificial Intelligence in Racing Games in Engineering Education: A Systematic Literature Review Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. https://strategy.asee.org/47040
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