Conference Session

AI, Technology, and Data-Driven Learning in Biomedical Engineering

Collection

2025 ASEE Annual Conference & Exposition

Authors

Viswajith Siruvallur Vasudevan, Cornell University; Shivaun D Archer, Cornell University; Jonathan T. Butcher, Cornell University

Tagged Divisions

Biomedical Engineering Division (BED)

Paper ID #46505Exploring the Efficacy of Generative AI and ChatGPT in BME InstructionalLabs: A Case Study on GABA Receptors and Synaptic PotentialsDr. Viswajith Siruvallur Vasudevan, Cornell University Viswajith Siruvallur Vasudevan is currently pursuing a postdoctoral fellowship in Active Learning at the Meinig School of Biomedical Engineering, Cornell University. He obtained his Ph.D. in Electrical Engineering from University of Central Florida (May 2020) working on the control and therapeutic optimization of Ventricular Assist Devices. Following his Ph.D., he worked in Cornell University as a postdoctoral associate in

Conference Session

AI, Technology, and Data-Driven Learning in Biomedical Engineering

Collection

2025 ASEE Annual Conference & Exposition

Authors

Angela Lai, Tufts University; Kavon Karrobi, Boston University

Tagged Topics

Diversity

Tagged Divisions

Biomedical Engineering Division (BED)

their writing in sustained or long-term writing projects[13, 14]. Due to thismodule, the majority of students were optimistic towards using AI in future assignments forwriting. However, students who use ChatGPT to write tend to run into common pitfalls such asambiguous writing, bias reinforcement, and “hallucinations”[15]. This shift reflects the need toprovide clear guidance on appropriate AI usage in educational settings. This work highlights thegrowing recognition that fostering AI literacy is a crucial educational practice in modernclassrooms.To investigate the ways students respond to AI literacy efforts and how they may change theiruse of genAI in these situations, we introduce structured usage of AI in one lecture to increase AIliteracy

Conference Session

Biomedical Engineering Division (BED) Postcard Session (Best of WIPs)

Collection

2025 ASEE Annual Conference & Exposition

Authors

Nathan Hyungsok Choe, The George Washington University; Chanyee Hong; Hyeyeon Lim

Tagged Topics

Diversity

Tagged Divisions

Biomedical Engineering Division (BED)

al.emphasized that utility value and self-efficacy are essential in shaping the learning outcomes ofengineering students, highlighting the importance of integrating practical applications into thecurriculum to enhance students' perceived utility value [3]. In the context of using large languagemodel (LLM) like ChatGPT in engineering education, the utility value can significantlyinfluence how students perceive and utilize these tools. Recent studies have explored the impactof LLM on students' perception of utility value in using AI tools. For instance, Rosenzweig et alexamined the effectiveness of utility value interventions in online math courses and found thatsuch interventions significantly increased students' perceived utility value and

Conference Session

Curriculum Development and Pedagogical Innovations

Collection

2025 ASEE Annual Conference & Exposition

Authors

William H Guilford, University of Virginia

Tagged Divisions

Biomedical Engineering Division (BED)

according topredefined rubric criteria, which aligned with levels of DPK. Each student's pre and postresponses were evaluated for the presence or absence of rubric categories. ChatGPT providedconsistency in applying the rubric across the dataset and sped the analysis. Further, for eachrubric criterion, ChatGPT highlighted specific examples and patterns of alignment ormisalignment with rubric, allowing for manual validation of the results.Statistical comparisons were performed using SPSS. Scores were ordinal and not normallydistributed. Therefore, a Wilcoxon Signed Ranks test was used for comparison of pre-post paireddata. A Mann-Whitney test was used to compare beginning of semester scores between the twosemesters.ResultsBefore the curriculum

Conference Session

Biomedical Engineering Division (BED) Poster Session

Collection

2025 ASEE Annual Conference & Exposition

Authors

Xianglong Wang, University of California, Davis; Tiffany Marie Chan, University of California, Davis; Angelika Aldea Tamura, University of California, Davis

Tagged Topics

Diversity

Tagged Divisions

Biomedical Engineering Division (BED)

], rapidimprovement in the performance of these products, as reflected by one faculty’s experience ofPerplexity AI scoring 80% on their multiple choice-based engineering quiz, accentuated the needfor BME educators and students to improve AI literacy and cultivate responsible use of AI.ML algorithms are computer programs that improve their performance with more experience(data) [8]. Therefore, problems in the data used to train ML algorithms, such as demographicbiases, can be reflected in the performance of ML algorithms. In a BME context, GPT-4, whichpowers ChatGPT and Perplexity AI, showed strong ethnic biases when assigning medicalconditions such as HIV/AIDS [9], while GPT-4 and Gemini (also powers AI-enabled notebook,NotebookLM) showed negative perception

Conference Session

Biomedical Engineering Division (BED) Poster Session

Collection

2025 ASEE Annual Conference & Exposition

Authors

Alex Nelson Frickenstein, University of Oklahoma

Tagged Divisions

Biomedical Engineering Division (BED)

Instructor Responses Showing how AI can be used to conduct initial research into a I think that students could improve at their use of sequential prompts. subject and find appropriate sources. I think students would benefit from [the] use of more diverse AI tools Asking AI for ideas of how to start problems or explain the current (i.e., in addition to ChatGPT). I think that students should use AI flow in circuitry to better understand how they interact. more to generate practice problems and summaries from lectures Have [AI] write more of the [project] document. Most of the work is (i.e., to help them study at their own pace and be more self-directed busy work, wanting a specific

Conference Session

Biomedical Engineering Division (BED) Technical Session 2

Collection

2024 ASEE Annual Conference & Exposition

Authors

Eliot Bethke, University of Illinois at Urbana - Champaign; Ali Ansari, University of Illinois Urbana-Champaign; Jennifer R Amos, University of Illinois Urbana-Champaign; Joe Bradley, University of Illinois Urbana-Champaign; Ruth Ochia P.E., Temple University; Holly M Golecki, University of Illinois Urbana-Champaign

Tagged Divisions

Biomedical Engineering Division (BED)

we determined were the most relevant to the topicscovered in our workshops [8,29]. Text definitions for IEEE terminology [29] in Table 3 weregenerated and summarized using ChatGPT 3.5 [6].Table 3. IEEE taxonomy terms selected as coding scheme for analysis. Definitions provided by ChatGPT. Term Definition Workshop CAD/CAM The use of computer technology in the design and CAD manufacturing processes of products. Design for An approach in product design that focuses on CAD manufacture optimizing the design of a product to make it more easily and cost-effectively manufacturable. Breadboarding

Conference Session

Biomedical Engineering Division (BED) Poster Session

Collection

2024 ASEE Annual Conference & Exposition

Authors

Kavon Karrobi, Boston University; Angela Lai, Tufts University

Tagged Divisions

Biomedical Engineering Division (BED)

understanding of the scientific writing process. On an integer scale of 1 to 5, where 1 is “Weak” and 5 is “Strong”. 8. Rate your understanding of ethics in scientific publication. On an integer scale of 1 to 5, where 1 is “Weak” and 5 is “Strong”. 9. How comfortable are you with preparing and presenting technical presentations? On an integer scale of 1 to 5, where 1 is “Never Tried” and 5 is “Very Comfortable”.10. How often do you use ChatGPT, BingChat or other AI Large Language Model (LLM) tools for writing tasks? On an integer scale of 1 to 5, where 1 is “Never Tried” and 5 is “Very Often”.11. If you use these AI tools, what specifically have you used them for? [open response]12. What is one area of technical communication

Conference Session

Biomedical Engineering Division (BED) Poster Session

Collection

2024 ASEE Annual Conference & Exposition

Authors

Amy N Adkins, North Carolina State University; Naji S Husseini, North Carolina State University; Lianne Cartee, North Carolina State University

Tagged Topics

Diversity

Tagged Divisions

Biomedical Engineering Division (BED)

into technical writing instruction.References[1] “Best Practices for Using AI When Writing Scientific Manuscripts: Caution, Care, andConsideration: Creative Science Depends on It” ACS Nano 2023, 17, 5, 4091–4093. 2023.https://doi.org/10.1021/acsnano.3c01544[2] Leung TI, de Azevedo Cardoso T, Mavragani A, Eysenbach G. Best Practices for Using AITools as an Author, Peer Reviewer, or Editor. J Med Internet Res. 2023 Aug 31;25:e51584. doi:10.2196/51584. PMID: 37651164; PMCID: PMC10502596.[3] J. Qadir, "Engineering Education in the Era of ChatGPT: Promise and Pitfalls of GenerativeAI for Education," 2023 IEEE Global Engineering Education Conference (EDUCON), Kuwait,Kuwait, 2023, pp. 1-9, doi: 10.1109/EDUCON54358.2023.10125121.[4] A. Adkins, N. S

Conference Session

Biomedical Engineering Division (BED) Technical Session 3

Collection

2024 ASEE Annual Conference & Exposition

Authors

Mercedes Terry, University of North Dakota; Enrique Alvarez Vazquez, University of North Dakota; Dan Ewert, University of North Dakota; Ryan Striker, University of North Dakota

Tagged Divisions

Biomedical Engineering Division (BED)

graduate students interactwith them more frequently. Together, these results suggest that how graduate students interactwith UG students leads to skill growth and that merely having one is inadequate for drivinggrowth.Perceived Challenges and Advantages of Graduate Students on their Instructional Setting Generally, online students did not indicate they perceived many challenges whenmentoring UG students in an online instructional setting. Generative AI, specifically ChatGPT,found that online students perceive that being online poses many advantages. The three mostcommon trends were that an online instructional setting allows students to be more flexible andhandle their professional and personal lives more efficiently, it is easier to