Ethical Use of Generative AI in Engineering: Assessing Students and Preventing Them from Cheating Themselves

Ronald P. Uhlig; Shatha Jawad; Phillip Zamora; Elizabeth Niven

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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: Using technology in engineering ethics education
Tagged Division: Engineering Ethics Division (ETHICS)
Permanent URL: https://peer.asee.org/47339

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Paper Authors

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Ronald P. Uhlig National University

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Dr. Ron Uhlig is currently Chair, Department of Engineering, Data and Computer Sciences, National University School of Technology and Engineering, College of Business, Engineering and Technology. From 2010-2014, he served as Dean, NU School of Business and Management. He returned to the engineering faculty in 2014. From 2000-2005, he was President/CEO, SegWave, Inc., an educational technology systems company he founded. Previous positions include Vice President for Russia and CIS Countries, Qualcomm Inc., 1995-99, and multiple positions with Northern Telecom and Bell-Northern Research in Ottawa, Canada and Richardson, TX during 1978-1995. He had nationwide responsibility for US Army Materiel Command scientific & engineering computing, 1969-78, pioneering many applications in what has become today’s Internet, and he has served as a US Army Officer in the Office of the Chief of Staff, in the Pentagon, He holds a BS in Physics from MIT and a PhD in Physics from the University of Maryland.

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Shatha Jawad National University

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Dr. Shatha Jawad has more than 25 years of experience in teaching and more than three years as a software engineer. She received the Full-Time Faculty Excellence in Teaching Award for the School of Technology and Engineering at National University in 2023. She had UNESCO Fellowship in the field of Information and Communication Technologies, in 2002. Her Ph.D. is in computer engineering. She is a member of the Institute for Learning-enabled Optimization at Scale (TILOS) which has an NSF grant that began on November 1, 2021, for five years. TILOS is a National Science Foundation-funded Artificial Intelligence (AI) Research Institute led by the University of California-San Diego and includes faculty from the Massachusetts Institute of Technology, the University of Pennsylvania, the University of Texas at Austin, Yale University, and the National University.

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Phillip Zamora National University

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Phillip Zamora is a First Lieutenant in the United States Air Force and a recipient of the AFRL Scholar award. He obtained a Bachelor's degree in Computer Science from South Dakota State University in 2020. Currently, he is pursuing a Master's degree in Computer Science from National University in California.

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Elizabeth Niven National University

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Abstract

An ASEE 2023 paper considered whether student use of Generative Artificial Intelligence (GenAI) to write engineering papers constitutes cheating. It was concluded that it depended on specific circumstances, but it was noted that students might potentially undermine their own learning by relying on tools like ChatGPT to answer questions and compose papers. This paper addresses how to enable student use of these tools in a way that students are not cheating themselves. Educators faced a related issue 20 years ago when calculators were introduced into the classroom. As time passed, instructors realized that allowing students to use calculators allowed them to concentrate on teaching concepts, strategies for problem solving and critical thinking. Calculators allowed more individualized instruction and more use of real-world applications. The introduction of calculators into the classroom stimulated discussion on ethical use of technology in teaching. A similar revolution is occurring with the introduction of Generative Artificial Intelligence tools such as ChatGPT, BARD and many others, and a similar set of opportunities is emerging. A key issue is how to use GenAI tools constructively to encourage critical thinking in the solving of engineering problems. The tools can make it easier to differentiate instruction to meet the diverse needs of students and tailor teaching to students with different levels of skills. Using Generative AI tools can help build students’ confidence in their own abilities to function as engineers. For example, students can analyze a problem from a variety of perspectives by varying the prompts they give to their GenAI tool. In this paper, we discuss some actions taken in our classes, including changing the style of assessments, teaching students “prompt engineering” as part of the critical thinking process, teaching students how to use Generative AI effectively in coding, and helping students to acquire the habit of validating outputs from Gen AI tools to eliminate hallucinations and fake references. Students can work at their own pace and explore concepts on their own, as Gen AI tools provide instant feedback. Students can explore a problem from multiple perspectives, including the impact of various ethical considerations by varying their prompts. Using Gen AI to generate simple answers to simple questions could, indeed, cheat students out of the ability to learn important engineering concepts. Using Gen AI to explore alternatives and assess different approaches to an engineering problem, for example asking for code using different Python libraries, can make students better and more effective engineers.

Citation
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Uhlig, R. P., & Jawad, S., & Zamora, P., & Niven, E. (2024, June), Ethical Use of Generative AI in Engineering: Assessing Students and Preventing Them from Cheating Themselves Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. https://peer.asee.org/47339

TY - CPAPER
AB - An ASEE 2023 paper considered whether student use of Generative Artificial Intelligence (GenAI) to write engineering papers constitutes cheating. It was concluded that it depended on specific circumstances, but it was noted that students might potentially undermine their own learning by relying on tools like ChatGPT to answer questions and compose papers. This paper addresses how to enable student use of these tools in a way that students are not cheating themselves. Educators faced a related issue 20 years ago when calculators were introduced into the classroom. As time passed, instructors realized that allowing students to use calculators allowed them to concentrate on teaching concepts, strategies for problem solving and critical thinking. Calculators allowed more individualized instruction and more use of real-world applications. The introduction of calculators into the classroom stimulated discussion on ethical use of technology in teaching. A similar revolution is occurring with the introduction of Generative Artificial Intelligence tools such as ChatGPT, BARD and many others, and a similar set of opportunities is emerging. A key issue is how to use GenAI tools constructively to encourage critical thinking in the solving of engineering problems. The tools can make it easier to differentiate instruction to meet the diverse needs of students and tailor teaching to students with different levels of skills. Using Generative AI tools can help build students’ confidence in their own abilities to function as engineers. For example, students can analyze a problem from a variety of perspectives by varying the prompts they give to their GenAI tool. In this paper, we discuss some actions taken in our classes, including changing the style of assessments, teaching students “prompt engineering” as part of the critical thinking process, teaching students how to use Generative AI effectively in coding, and helping students to acquire the habit of validating outputs from Gen AI tools to eliminate hallucinations and fake references. Students can work at their own pace and explore concepts on their own, as Gen AI tools provide instant feedback. Students can explore a problem from multiple perspectives, including the impact of various ethical considerations by varying their prompts. Using Gen AI to generate simple answers to simple questions could, indeed, cheat students out of the ability to learn important engineering concepts. Using Gen AI to explore alternatives and assess different approaches to an engineering problem, for example asking for code using different Python libraries, can make students better and more effective engineers.
AU - Ronald P. Uhlig
AU - Shatha Jawad
AU - Phillip Zamora
AU - Elizabeth Niven
CY - Portland, Oregon
DA - 2024/06/23
PB - ASEE Conferences
TI - Ethical Use of Generative AI in Engineering: Assessing Students and Preventing Them from Cheating Themselves
UR - https://peer.asee.org/47339
ER -