The Future of Learning: Harnessing Generative AI for Enhanced Engineering Technology Education

Jody Lee Alberd; Mahesh Kumar Pallikonda; Ravi C. Manimaran

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Conference: 2024 ASEE Annual Conference & Exposition
Location: Portland, Oregon
Publication Date: June 23, 2024
Start Date: June 23, 2024
End Date: July 12, 2024
Conference Session: Panel: AI and Engineering Technology Education: What, Why, How?
Tagged Division: Engineering Technology Division (ETD)
Permanent URL: https://peer.asee.org/48100

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Paper Authors

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Jody Lee Alberd Austin Peay State University

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Mr. Jody Lee Alberd is an Assistant Professor with the Department of Engineering Technology at Austin Peay State University (APSU) from where he earned his Master of Science in Engineering Technology. Mr. Alberd’s industrial career as a Manufacturing Engineer included working with several renowned companies such as Trane Technologies and Electrolux North America. Prior to that, he served in the United States Navy during a 20-year career that included service during the Persian Gulf War as well as the Global War on Terror having completed six deployments to the Persian Gulf region.
Mr. Alberd seeks to enhance educational opportunities and experiences for veteran, non-traditional and traditional students in Engineering Technology careers.

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Mahesh Kumar Pallikonda Austin Peay State University

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Dr. Mahesh Kumar Pallikonda is a faculty member in the Department of Engineering technology at Austin Peay State University (APSU). Prior to his academic career, he gained valuable industry experience in roles ranging from New Product Development to Process Control. He holds a Ph.D. and a Master's degree in Mechanical Engineering from Cleveland State University, as well as a Bachelor's degree in Manufacturing Engineering from the National Institute of Advanced Manufacturing Technology. Prior to joining APSU, he served as a faculty member at Ohio Northern University, where he taught courses on the fundamentals of electronics, including electrical circuits. At APSU, Dr. Pallikonda instructs courses specializing in Robotics and its applications, Engineering Economics, CAD and Manufacturing processes. Dr. Pallikonda is passionate about educating and inspiring the next generation of engineers, technologists, and innovators through his lectures. He is deeply committed to advancing the fields of robotics and manufacturing through interdisciplinary research in connected devices and Industrial Internet of Things (IIoT). His research interests span Manufacturing, Material Science, pedagogy, Lean Six Sigma, and Industry 4.0

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Ravi C. Manimaran Austin Peay State University

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Ravi C Manimaran is a Professor and Chair of the Department of Engineering Technology, Austin Peay State University, Clarksville, Tennessee. His education includes two Master of Science degrees in Electrical and Computer Engineering and Electronics and Control Engineering. He has been actively involved in higher education leadership in various capacities as a Dean, Department Chair, PI, Project Director, and a faculty member since 1997. He has served as the PI / Project Director for multiple agencies including NSF, DOL, DOD, and Perkin’s Grant. His research interests include Industrial Automation Systems, VLSI, ASIC, and FPGA. Other areas of interest are Active Learning, Innovative Pedagogy, Higher Education Leadership and Accreditation including ABET.

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Abstract

Engineering Technology education stands at the precipice of a profound transformation driven by the integration of Generative Artificial Intelligence (Generative AI). Incorporating generative AI into engineering technology education can enhance the learning experience, foster creativity, and prepare students for the increasingly AI-driven field of engineering. It allows students to focus on problem-solving, innovation, and the application of engineering principles, while AI handles routine tasks and provides valuable insights and guidance. However, it's crucial to strike a balance and ensure that students also develop a deep understanding of the fundamental concepts and skills that underlie the technology they are using. This abstract provides an overview of a study that explores the transformative potential and application of Generative AI in engineering technology education. Generative AI refers to a category of AI models and algorithms that have the ability to generate new content that is similar to, or in some cases indistinguishable from, content created by humans. These AI systems are designed to generate data, such as text, images, audio, and more, based on patterns and knowledge they've learned from large datasets during their training. Integrating Generative AI into engineering education can be a valuable way to prepare students for the future and equip them with skills relevant to emerging technologies. This study explores how Generative AI can revolutionize the traditional pedagogical approach by enabling the development of interactive lab experiences, simulations, and practical exercises to integrate and create a greater understanding of AI capabilities. These innovations create authentic learning environments, equipping students with hands-on experience and honing their problem-solving skills. This study also scrutinizes the ethical implications and challenges tied to the incorporation of Generative AI in education. It emphasizes the need for unbiased AI algorithms and responsible usage while calling for comprehensive training and support for instructors in harnessing this innovative technology. In conclusion, this study intends to demonstrate that harnessing Generative AI in engineering technology education has the potential to revolutionize the way students learn in addition to preparing students to leverage these technologies for innovative engineering solutions and equip them with valuable skills that are increasingly in demand in various engineering domains.

Citation
Format

Alberd, J. L., & Pallikonda, M. K., & Manimaran, R. C. (2024, June), The Future of Learning: Harnessing Generative AI for Enhanced Engineering Technology Education Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. https://peer.asee.org/48100

TY  - CPAPER
AB  -      Engineering Technology education stands at the precipice of a profound transformation driven by the integration of Generative Artificial Intelligence (Generative AI).  Incorporating generative AI into engineering technology education can enhance the learning experience, foster creativity, and prepare students for the increasingly AI-driven field of engineering. It allows students to focus on problem-solving, innovation, and the application of engineering principles, while AI handles routine tasks and provides valuable insights and guidance. However, it&#39;s crucial to strike a balance and ensure that students also develop a deep understanding of the fundamental concepts and skills that underlie the technology they are using. This abstract provides an overview of a study that explores the transformative potential and application of Generative AI in engineering technology education. 
     Generative AI refers to a category of AI models and algorithms that have the ability to generate new content that is similar to, or in some cases indistinguishable from, content created by humans. These AI systems are designed to generate data, such as text, images, audio, and more, based on patterns and knowledge they&#39;ve learned from large datasets during their training.
     Integrating Generative AI into engineering education can be a valuable way to prepare students for the future and equip them with skills relevant to emerging technologies. This study explores how Generative AI can revolutionize the traditional pedagogical approach by enabling the development of interactive lab experiences, simulations, and practical exercises to integrate and create a greater understanding of AI capabilities. These innovations create authentic learning environments, equipping students with hands-on experience and honing their problem-solving skills.
     This study also scrutinizes the ethical implications and challenges tied to the incorporation of Generative AI in education. It emphasizes the need for unbiased AI algorithms and responsible usage while calling for comprehensive training and support for instructors in harnessing this innovative technology.
     In conclusion, this study intends to demonstrate that harnessing Generative AI in engineering technology education has the potential to revolutionize the way students learn in addition to preparing students to leverage these technologies for innovative engineering solutions and equip them with valuable skills that are increasingly in demand in various engineering domains.

AU  - Jody Lee Alberd
AU  - Mahesh Kumar Pallikonda
AU  - Ravi C. Manimaran
CY  - Portland, Oregon
DA  - 2024/06/23
PB  - ASEE Conferences
TI  - The Future of Learning: Harnessing Generative AI for Enhanced Engineering Technology Education
UR  - https://peer.asee.org/48100
ER  -