1. Exploring natural language processing in mechanical engineering education: Implications for academic integrity.
- Author
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Lesage, Jonathan, Brennan, Robert, Eaton, Sarah Elaine, Moya, Beatriz, McDermott, Brenda, Wiens, Jason, and Herrero, Kai
- Subjects
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MECHANICAL engineering education , *EDUCATION ethics , *LANGUAGE models , *ENGINEERING laboratories , *ENGINEERING students , *ASSISTIVE technology , *NATURAL language processing - Abstract
In this paper, the authors review extant natural language processing models in the context of undergraduate mechanical engineering education. These models have advanced to a stage where it has become increasingly more difficult to discern computer vs. human-produced material, and as a result, have understandably raised questions about their impact on academic integrity. As part of our review, we perform two sets of tests with OpenAI's natural language processing model (1) using GPT-3 to generate text for a mechanical engineering laboratory report and (2) using Codex to generate code for an automation and control systems laboratory. Our results show that natural language processing is a potentially powerful assistive technology for engineering students. However, it is a technology that must be used with care, given its potential to enable cheating and plagiarism behaviours given how the technology challenges traditional assessment practices and traditional notions of authorship. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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