Artificial Intelligence in an Online Engineering Economy Class

Dani Fadda; Oziel Rios; P.l.stephan Thamban

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Conference: 2025 ASEE Annual Conference & Exposition
Location: Montreal, Quebec, Canada
Publication Date: June 22, 2025
Start Date: June 22, 2025
End Date: August 15, 2025
Conference Session: AI Integration in Engineering Economy Course
Tagged Division: Engineering Economy Division (EED)
Page Count: 8
Permanent URL: https://peer.asee.org/55459

Paper Authors

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Dani Fadda University of Texas at Dallas

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Dr. Dani Fadda is a mechanical engineering Professor of Practice at the University of Texas at Dallas. His background includes two decades of professional engineering practice in the energy industry where he published numerous papers and developed patented products for chemical, petrochemical, and nuclear applications. He enjoys teaching in-person and online classes and is the recipient of prestigious teaching awards. Dr. Fadda is a registered Professional Engineer in the state of Texas and an ASME fellow.

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Oziel Rios University of Texas at Dallas

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Dr. Oziel Rios earned his Ph.D. in mechanical engineering from the University of Texas at Austin in 2008 where his research focused on design of robotic systems with an emphasis on kinematic and dynamic modeling for analysis and control. Dr. Rios teaches the first-year and CAD courses in the Mechanical Engineering Department at the University of Texas at Dallas. Dr. Rios has also taught courses on Geometric Dimensioning and Tolerancing (GD&T), kinematics and dynamics, and graduate-level CAD courses. Dr. Rios’ research and teaching interests include: robotics, design, GD&T, and engineering education. Dr. Rios has received UTD President's Teaching Excellence Award, the Outstanding Undergraduate Teaching Award from UTD's Jonsson School, and the UT System Regent's Outstanding Teaching Award.

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P.l.stephan Thamban

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Dr. Thamban is an associate professor of instruction in the Mechanical Engineering department at the University of Texas at Dallas who contributes to the teaching mission of the department. He brings with him more than a decade long teaching experience and teaches foundational, introductory ME undergraduate courses and advanced mathematics courses for undergraduate and graduate students. He values and incorporates project-based learning components in undergraduate courses.

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Abstract

The use of artificial intelligence (AI) is investigated in an engineering economy class. The class is offered as an elective at our university in the asynchronous online modality. This online course is developed based on the backward design method [1] similarly other courses described in our previous publication [2].

The objective of this paper is to present evidence that the student’s curiosity to investigate GenAI tools can be leveraged, with some investment from the faculty, to enhance learning while the integrity of the major course assessments can be maintained by proctoring major exams. In the presence of AI, people can interact and receive real-time feedback by creating and using effective prompts [3]. Students are increasingly aware of AI tools and some use it guiltlessly on homework assignments [4]. However, a professor is still responsible for offering authentic education so students can identify, formulate, and solve complex engineering problems [5].

Evidence from incorporating AI in the engineering economy class indicates that some reward (e.g., extra credit) from the faculty is needed before students engage with AI in a desired positive way. An assignment, using GenAI, is deployed based on a problem in the textbook [6]. If copied and pasted into ChatGPT [7], Copilot [8], or Gemini [9], the AI gives an acceptable solution methodology but does not consistently give the correct final answers (as of the date of writing this paper). In this assignment, the students are tasked with solving the problem manually and show their work according to the method they learned in the class. They are also asked to use GenAI to analyze the problem and provide a conclusion about their experience in using AI.

The online course is described in this paper along with its assessments: proctored exams, homework assignments, and quizzes. The AI assignment and its corresponding student work are highlighted, and conclusions are presented.

In summary, making students aware of the benefits and drawbacks of AI is considered a better alternative than simply locking AI out of the class. Specifically, engineering the AI prompts resembles teaching and leading the AI tool to achieve the desired solution method and answer. Since teaching is one great way to learn, this process is considered a valuable learning experience. The use of AI tools in the engineering economy class can help the students better understand the course concepts. Conclusions provided by the students indicate that this assignment helped them with the engineering economy concepts and with using AI responsibly. However, offering proctored midterm and final exams with no access to AI is needed for major assessments.

References are excluded since this is a blind submission.

Citation
Format

Fadda, D., & Rios, O., & Thamban, P. (2025, June), Artificial Intelligence in an Online Engineering Economy Class Paper presented at 2025 ASEE Annual Conference & Exposition , Montreal, Quebec, Canada . https://peer.asee.org/55459

TY - CPAPER
AB - The use of artificial intelligence (AI) is investigated in an engineering economy class. The class is offered as an elective at our university in the asynchronous online modality. This online course is developed based on the backward design method [1] similarly other courses described in our previous publication [2].

The objective of this paper is to present evidence that the student’s curiosity to investigate GenAI tools can be leveraged, with some investment from the faculty, to enhance learning while the integrity of the major course assessments can be maintained by proctoring major exams. In the presence of AI, people can interact and receive real-time feedback by creating and using effective prompts [3]. Students are increasingly aware of AI tools and some use it guiltlessly on homework assignments [4]. However, a professor is still responsible for offering authentic education so students can identify, formulate, and solve complex engineering problems [5].

Evidence from incorporating AI in the engineering economy class indicates that some reward (e.g., extra credit) from the faculty is needed before students engage with AI in a desired positive way. An assignment, using GenAI, is deployed based on a problem in the textbook [6]. If copied and pasted into ChatGPT [7], Copilot [8], or Gemini [9], the AI gives an acceptable solution methodology but does not consistently give the correct final answers (as of the date of writing this paper). In this assignment, the students are tasked with solving the problem manually and show their work according to the method they learned in the class. They are also asked to use GenAI to analyze the problem and provide a conclusion about their experience in using AI.

The online course is described in this paper along with its assessments: proctored exams, homework assignments, and quizzes. The AI assignment and its corresponding student work are highlighted, and conclusions are presented.

In summary, making students aware of the benefits and drawbacks of AI is considered a better alternative than simply locking AI out of the class. Specifically, engineering the AI prompts resembles teaching and leading the AI tool to achieve the desired solution method and answer. Since teaching is one great way to learn, this process is considered a valuable learning experience. The use of AI tools in the engineering economy class can help the students better understand the course concepts. Conclusions provided by the students indicate that this assignment helped them with the engineering economy concepts and with using AI responsibly. However, offering proctored midterm and final exams with no access to AI is needed for major assessments.

References are excluded since this is a blind submission.
AU - Dani Fadda
AU - Oziel Rios
AU - P.l.stephan Thamban
CY - Montreal, Quebec, Canada
DA - 2025/06/22
PB - ASEE Conferences
TI - Artificial Intelligence in an Online Engineering Economy Class
UR - https://peer.asee.org/55459
ER -