Machine Learning Tools in the Technical Writing Classroom: A Modular Approach
- Conference
- Location
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Portland, Oregon
- Publication Date
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June 23, 2024
- Start Date
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June 23, 2024
- End Date
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June 26, 2024
- Conference Session
- Tagged Division
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Computers in Education Division (COED)
- Page Count
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10
- DOI
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10.18260/1-2--47756
- Permanent URL
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https://peer.asee.org/47756
- Download Count
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79
Paper Authors
Alyson G. Eggleston Pennsylvania State University
Alyson Eggleston is an Associate Professor in the Penn State Hershey College of Medicine and Director of Evaluation for the Penn State Clinical and Translational Science Institute. Her research and teaching background focuses on program assessment, STEM technical communication, industry-informed curricula, and educational outcomes veteran and active duty students.
Robert J. Rabb P.E. Pennsylvania State University
Robert Rabb is the associate dean for education in the College of Engineering at Penn State. He previously served as a professor and the Mechanical Engineering Department Chair at The Citadel. He previously taught mechanical engineering at the United States Military Academy at West Point. He received his B.S. in Mechanical Engineering from the United Military Academy and his M.S. and PhD in Mechanical Engineering from the University of Texas at Austin. His research and teaching interests are in mechatronics, regenerative power, and multidisciplinary engineering.
Abstract
As new Machine Learning (ML) tools come online, technical writing instruction is poised to create new applied projects, teaching students to use ML constructively, objectively evaluate ML output, and refine final products faster. STEM researchers are already publishing their use of Chat GPT-adjacent language tools in high impact scientific outlets like Nature. Engineering students need exposure and to develop competency in using these tools. ML can support technical writing by proofreading content; suggesting novel syntactic structures; producing usable content faster; and upskilling writers in the process. This paper presents the use of four ML tools, applied in service to a series of technical writing and communication projects appropriate for sophomore-junior level students. Projects can be used in embedded technical communication modules and are scaled up for independent courses.
Changing the technical writing and communication (TWC) curriculum will prepare students to employ technical writing tools already on the market. TWC courses are typically taught in sophomore and junior year, and their curricular placement supports more technical lab-based courses and senior design courses. By employing the modular approach that this paper advocates, ML-informed technical writing projects can be scaffolded throughout the curriculum, paired with a more technical course, or tailored to a graduate seminar.
Current technical writing courses for engineers support the curriculum by improving Engineering Students’ (ES) communication skills; teaching ES technical conventions; and building capacity for project management and project documentation. Engineering students become more accurate in their evaluations of Technical Writing (TW), and better able to distinguish effective and ineffective TW after working with these tools. Lastly, teaching students to use ML writing tools allow engineering educators to effectively promote these learning outcomes in novel ways, while supporting professional preparation.
Eggleston, A. G., & Rabb, R. J. (2024, June), Machine Learning Tools in the Technical Writing Classroom: A Modular Approach Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. 10.18260/1-2--47756
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