Scholar Project: Building Intrinsic Motivation for Ethical AI Use in Engineering Education

CIRTL Scholars add to community knowledge about teaching and learning, most often by presenting or publishing their work to university, regional, national, or international audiences. It is the highest achievement level CIRTL participants can earn. 

In this spotlight, Aditya Bandimatt, TAR Sp’25, shares with us how he developed his project with Prof. Sherilyn Keaton and revised it to present at the national annual CIRTL TAR Symposium this month. See his full project here at the U of A TAR project archives.

What’s your TAR project about?

Students are increasingly over-relying on Generative AI tools to complete academic tasks, bypassing the cognitive processes essential for developing critical thinking, problem-solving and deep learning. This undermines students' essential learning outcomes. It also raises important questions about the responsibility of educators in ensuring safe AI adoption by their students.

My project is about enabling engineering students with knowledge and motivation to safely and ethically adopt AI in their learning journey.

What inspired you to do your TAR project on this topic?

AI is the most consequential technology disruption since the first industrial revolution! Some experts say, most consequential in human history. It is reshaping all aspects of our industry and society. While researching for a topic for my TAR project, the 'impact of generative AI tools on the next generation of students' was a theme that came up repeatedly in my conversations with professors and educators.

While a lot of work was underway in understanding and addressing this concern as an academic integrity issue, there wasn't much focus on empowering students themselves to prioritize AI safety. This was the primary motivation in selecting this topic for my TAR project.

Tell us a little bit about what you did and what you found.

The question I framed for this TAR project was: "How can we build intrinsic motivation for students of engineering courses (SFWE***, SIE*** and SIE***) to implement best practices while using generative AI tools in order to ensure that essential learning outcomes are not compromised?".

I worked with Prof. Sherilyn Keaton from Systems and Industrial Engineering with mentoring from Dr. Lisa Elfring and Dr. Kristin Winet. The methodology involved a baseline survey of students, an intervention lecture and workshop, followed by a post-intervention survey to measure the outcome. The workshop and homework had students work with ChatGPT without any restrictions to answer homework questions and then again in “tutor mode.” They were asked to compare and reflect on the responses.

Among 131 students, the project achieved a 23% increase in policy awareness and greater enthusiasm and self-motivation for responsible adoption of GenAI tools. As one student said, “I enjoyed this interaction. I did not know what GenAI was capable of such interaction.” (See the results here).

What did you learn about yourself as an educator by doing a TAR project?

There were a couple of key takeaways - first the importance of connection and empathy (not just knowledge of the topic) if you are serious about student outcomes. I went into this thinking of myself as a researcher and came out of it thinking of myself as a teacher. Second, the importance of defining metrics to measure those outcomes. Enthusiasm and engagement are abstract until you are able to demonstrate changes in your students' attitudes via measurable outcomes.

How did you turn your project into a conference presentation? Did anyone ask you any questions about your work that surprised you?

It needed some work as conference presentation needs more than a summary of your project documentation. Given the time constraint and a different audience, project objective, methodology, results and outcomes had to be reorganized to deliver key messages in a limited time. After the presentation, people had lots of questions. I mostly got asked if the project methodology can be applied outside of STEM classrooms - in other disciplines. Another question I remember was, given the rapid changes with technology and its impact, how long the project outcomes will still be relevant.

What advice would you have for other TARs who want to take their work “public,” i.e., share it with a wider audience?

Understand  the audience, time limits and delivery format. Redo, rephrase, and reorganize based on these parameters. And most importantly, ask for help! There are many who have done this before, and their suggestions and insights can be of great support and value.

About Aditya
Aditya Bandimatt is a graduate from the School of Information Science, University of Arizona, with specialization in Machine Learning (Spring 2025). He is interested in interdisciplinary applications of AI to Systems Engineering and Social Science domains. He is also a certified Product Manager and DevOps Architect with extensive consulting experience in digital transformation, AI adoption and AI product management.