Integrating Artificial Intelligence into Electrical Engineering Education: A Paradigm Shift in Teaching and Learning

Kenan Baltaci; Monika Herrmann; Ahmet Turkmen

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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: Student Success in ET
Tagged Division: Engineering Technology Division (ETD)
Tagged Topic: Diversity
Permanent URL: https://peer.asee.org/47644

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Paper Authors

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Kenan Baltaci University of Wisconsin, Stout

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Kenan Baltaci earned a Bachelor of Science in Electrical Engineering in 2006 from Istanbul Technical University, Turkey, and a Master of Science in Energy Management in 2008 from the University of Northern Iowa, Cedar Falls, IA. He also holds a Doctor of Technology in Industrial Technology obtained in 2012 from the University of Northern Iowa. His research interests include renewable energy, power electronics, IoT, and embedded systems.

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Monika Herrmann University of Wisconsin, Stout

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About the Author
Monika Herrmann is an assistant professor in the Engineering and Technology department at the University of Wisconsin Stout. She holds professional licenses in Architecture and Interior Architecture in Germany and the USA and is practicin

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Ahmet Turkmen

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Ahmet Turkmen, PhD is an Associate Professor in the Engineering and Technology Department at the University of Wisconsin-Stout. Dr. Turkmenâ€™s fields of expertise include medical instrumentation, processing of physiological signals, and modeling of physi

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Abstract

The modern landscape of Electrical Engineering (EE) education is in flux, with technological advancements heralding new pedagogical tools and methodologies. Among these, the rise of Artificial Intelligence (AI) offers transformative potential. This study aims to comprehensively explore the integration of AI within EE courses, emphasizing its advantages, challenges, and the critical approach needed for its effective use. Additionally, it seeks to analyze how computer and electrical engineering students use and embrace AI tools in their education, supported by a survey to gather insights on the impact of these tools on learning and teaching strategies. For educators, AI-driven tools offer dynamic avenues for course content creation and delivery, adapting in real time based on student feedback and performance metrics. Laboratory preparations, too, are enriched with AI algorithms that predict equipment requirements, optimize setups, and simulate potential outcomes, ensuring impactful hands-on experiences for students. On the other hand, students benefit from AI-powered platforms that facilitate tailored explanations, visualizations, and simulations across various topics and difficulty levels, allowing self-paced acquisition of skills. However, the imperative need for critical engagement comes with the proliferation of AI tools. This paper emphasizes the importance of educating students on the judicious use of AI. While AI platforms are invaluable resources, students must be taught to approach them with a discerning mindset, questioning and verifying the information they receive. Over-reliance on AI can lead to unvetted assimilation of knowledge, making it crucial for students to cross-check AI-generated insights with trusted textbooks, online resources, scholarly articles, and faculty and- teaching assistants in the institution. Moreover, the paper underscores the significance of fostering a balanced pedagogical approach where AI tools are viewed as complementary resources rather than definitive sources of information. Educators are encouraged to instill in students a sense of responsibility in discerning the accuracy and relevance of AI-driven content, promoting a blend of traditional learning with the advantages of AI. AI-driven tools present dynamic opportunities for course content creation, laboratory preparations, and tailored student learning experiences. However, with these advantages come challenges. Over-reliance on AI tools can lead to the assimilation of potentially unverified knowledge. Hence, a balanced pedagogical approach is essential, where AI tools complement, rather than replace, traditional learning resources. For the effective integration of AI in EE education, educators must prioritize a two-pronged approach: leveraging the benefits of AI-driven tools while instilling in students a critical mindset toward the information they receive. Such a balanced approach promises a more dynamic, responsive, and critically engaged learning environment in Electrical Engineering.

Citation
Format

Baltaci, K., & Herrmann, M., & Turkmen, A. (2024, June), Integrating Artificial Intelligence into Electrical Engineering Education: A Paradigm Shift in Teaching and Learning Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. https://peer.asee.org/47644

TY - CPAPER
AB - The modern landscape of Electrical Engineering (EE) education is in flux, with technological advancements heralding new pedagogical tools and methodologies. Among these, the rise of Artificial Intelligence (AI) offers transformative potential.
This study aims to comprehensively explore the integration of AI within EE courses, emphasizing its advantages, challenges, and the critical approach needed for its effective use. Additionally, it seeks to analyze how computer and electrical engineering students use and embrace AI tools in their education, supported by a survey to gather insights on the impact of these tools on learning and teaching strategies.
For educators, AI-driven tools offer dynamic avenues for course content creation and delivery, adapting in real time based on student feedback and performance metrics. Laboratory preparations, too, are enriched with AI algorithms that predict equipment requirements, optimize setups, and simulate potential outcomes, ensuring impactful hands-on experiences for students.
On the other hand, students benefit from AI-powered platforms that facilitate tailored explanations, visualizations, and simulations across various topics and difficulty levels, allowing self-paced acquisition of skills. However, the imperative need for critical engagement comes with the proliferation of AI tools. This paper emphasizes the importance of educating students on the judicious use of AI. While AI platforms are invaluable resources, students must be taught to approach them with a discerning mindset, questioning and verifying the information they receive. Over-reliance on AI can lead to unvetted assimilation of knowledge, making it crucial for students to cross-check AI-generated insights with trusted textbooks, online resources, scholarly articles, and faculty and- teaching assistants in the institution.
Moreover, the paper underscores the significance of fostering a balanced pedagogical approach where AI tools are viewed as complementary resources rather than definitive sources of information. Educators are encouraged to instill in students a sense of responsibility in discerning the accuracy and relevance of AI-driven content, promoting a blend of traditional learning with the advantages of AI.
AI-driven tools present dynamic opportunities for course content creation, laboratory preparations, and tailored student learning experiences. However, with these advantages come challenges. Over-reliance on AI tools can lead to the assimilation of potentially unverified knowledge. Hence, a balanced pedagogical approach is essential, where AI tools complement, rather than replace, traditional learning resources.
For the effective integration of AI in EE education, educators must prioritize a two-pronged approach: leveraging the benefits of AI-driven tools while instilling in students a critical mindset toward the information they receive. Such a balanced approach promises a more dynamic, responsive, and critically engaged learning environment in Electrical Engineering.

AU - Kenan Baltaci
AU - Monika Herrmann
AU - Ahmet Turkmen
CY - Portland, Oregon
DA - 2024/06/23
PB - ASEE Conferences
TI - Integrating Artificial Intelligence into Electrical Engineering Education: A Paradigm Shift in Teaching and Learning
UR - https://peer.asee.org/47644
ER -