2025 ASEE Annual Conference & Exposition

The inevitable diffusion of generative artificial intelligence (genAI) into the academic sphere has rapidly progressed in the past two years. Courses that prioritize critical thinking and technical writing have seen students relying on genAI to brainstorm, clarify questions, and improve their report writing. Of particular interest are engineering students utilizing genAI to potentially support their avoidance of writing. Engineering students tend to have more reluctant attitudes toward writing-based assessments compared to problem-solving-based assessments and hands-on project work. These attitudes may be related to student perceived barriers to writing, which can transpire for a multitude of reasons, least of which being the potential lack of emphasis on writing in engineering curricula. Irrespective of the reasons for the perceived barriers, students are turning to genAI to support their technical writing. This study, which originated as a work in progress, aimed to investigate the following research question: How and why are students adopting genAI tools to surmount perceived barriers to technical writing? To investigate this question and the ways students use genAI for technical communication, we introduced structured usage of genAI in one lecture and provided forms to track genAI usage by type and the exchange in biomedical engineering courses that emphasize technical communication skills. Specifically, data was collected in three ways: (i) a pre-course survey on technical communication and genAI use; (ii) responses to a Generative Artificial Intelligence Assistance (GAIA) disclosure form submitted with assignments; (iii) a post-course survey mirroring the pre-course survey to see how student responses evolve. We aim to characterize biomedical engineering students’ adoption of the new technology and what student-identified barriers exist to potentially motivate student adoption of genAI for technical writing. Our study results showed that BME students adjusted their usage of GAI for technical writing after receiving a lecture on genAI prompting techniques for writing, editing, and assessing its efficacy. The students changed their usage of genAI in different ways and fell into two categories: 1) those who adopted it willingly and used it more frequently, and 2) those who decided to abstain from using it at all. The latter group of students reported strong feelings for self-efficacy and to be independently proficient at technical writing. By examining the ways in which students adopt genAI for technical writing and the underlying intentions, we hope to identify areas in curricula that may require greater emphasis. This insight could enable us to better support our students’ development of their technical writing skills.