AGuided Comparison of Bioinstrumentation Laboratory Data Analysis using Mathematical Software and Generative AI

Generative AI tools are becoming more widely available and have increasing functionality. Students are beginning to integrate AI into their current practice and will need to be able to ethically use AI in their future careers. Instead of banning AI in an undergraduate biomedical instrumentation instructional laboratory course at a large public university, it was intentionally added with awareness of privacy, equity, and accountability. An assignment was adapted to walk students through comparing data analysis by hand, with mathematical software, and with generative AI. The goal of the updated assignment was for students to be able to think critically about the difference between doing analysis by hand, with purpose-built and validated software, and with a generic tool based on a large language model. The submitted post-lab assignments were analyzed by the research team to understand the students' approach to this assignment and what they learned about each method. All the students were able to complete the assignment, however there was mixed feedback on the usefulness of the assignment. Details about the assignment development and analysis of student work on the assignment are included in this paper.

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Published
2024-10-13
DOI
https://doi.org/10.1109/fie61694.2024.10893373
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1
Primary Topic
Machine Learning and Data Classification
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article
Field-Weighted Citation Impact
0.35
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AGuided Comparison of Bioinstrumentation Laboratory Data Analysis using Mathematical Software and Generative AI

Megha Agrawal, Rebecca Reck, Hannah Kimmel, Maya Miriyala et al.
1 citations
Machine Learning and Data Classification
0.35
article

AGuided Comparison of Bioinstrumentation Laboratory Data Analysis using Mathematical Software and Generative AI

Megha Agrawal, Rebecca Reck, Hannah Kimmel, Maya Miriyala, Kaitlyn Tuvilleja, Hanwen Liang
article en
1 citations

Abstract

Generative AI tools are becoming more widely available and have increasing functionality. Students are beginning to integrate AI into their current practice and will need to be able to ethically use AI in their future careers. Instead of banning AI in an undergraduate biomedical instrumentation instructional laboratory course at a large public university, it was intentionally added with awareness of privacy, equity, and accountability. An assignment was adapted to walk students through comparing data analysis by hand, with mathematical software, and with generative AI. The goal of the updated assignment was for students to be able to think critically about the difference between doing analysis by hand, with purpose-built and validated software, and with a generic tool based on a large language model. The submitted post-lab assignments were analyzed by the research team to understand the students' approach to this assignment and what they learned about each method. All the students were able to complete the assignment, however there was mixed feedback on the usefulness of the assignment. Details about the assignment development and analysis of student work on the assignment are included in this paper.

University of Illinois Urbana-Champaign (US)
Openalex Percentile: Top 29%
Machine Learning and Data Classification
0.35
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AGuided Comparison of Bioinstrumentation Laboratory Data Analysis using Mathematical Software and Generative AI — Megha Agrawal, Rebecca Reck, et al. · (2024) | TGRS Research Map | TGRS