Abstract

Higher education is one of many industries grappling with the implications of generative AI (genAI). The use of such tools for assisting educators has been studied for their effectiveness in creating unique assignments, syllabi, or even grading assistance (Chiang et al., 2024). The use of genAI tools has also been touted for offering key insights for personalized instruction uniquely tailored to each individual student. However, educators are still leery about students themselves being allowed to use generative AI tools to aid in their educational development, such as writing a research paper. Fear of students becoming dependent on this technology for producing creative works, rather than developing the skills to create their own, are well founded (Kompella, 2025; Reiter et al., 2025). However, many workplaces have already incorporated the use of generative AI tools into the workplace and expect their new hires to not only know how to use them to be a more efficient worker, but also how to leverage the technology to effectively create new product and service ideas in the perpetual race to gain and/or maintain a competitive advantage. However, the concerns of educators in higher-ed and K-12 institutions alike remain. In particular, evaluating student submissions that may have used generative AI has been an on-going concern. The certainty of determining beyond a reasonable doubt a student’s use of genAI is difficult and places undue pressure on the instructor, leading many to simply ban outright the use of these tools. Rather than discourage their use, this paper aims to highlight a path to embrace an appropriate use of genAI tools in the classroom for a student research project. Incorporating the technology into the assignment removes the fear of it being used surreptitiously and also gives students a stronger understanding of its limitations as well as its effectiveness. This helps the students assess the genAI tool critically to better understand its proper place in their own life, education, and professional career. Our aim in this TREO Talk is to discuss the experience of incorporating genAI into a student research project given first to seniors, and subsequently to graduate students. The students were required to research various genAI systems, then choose the one they believe best suited their needs. Working in teams, students went through the project in four separate stages, each one introducing them to more features of their selected platform. Detailed assignment requirements in each stage of the project, criteria for evaluating each stage, grading schemes, and best practices for implementing the genAI project will be presented.

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