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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]

The complexity and dynamics of the automotive industry require highly trained graduates with competencies and skills in various scientific and industrial fields, as well as practical skills. Theory with can be applied to practice in several ways: increasing practical application of labs and workshops, practice activities, race car contests, participation in scientific groups, work on research and development projects, internships, etc. The present paper explores the attitudes and perceptions of students who participated in an international competition for prototype vehicles powered with compressed air. Based on an exploratory questionnaire, uni- and multivariate statistical methods were applied, and the results were used to create a model related to the students’ perception of the usefulness of participating compared to the additional engineering skills learned. The results show that students involved in race car competitions enjoy positive feedback and more (regardless of country of origin) and learn professional skills with high-quality and relevant technical, social, and behavioral competences, which are required in the automotive industry today. [ABSTRACT FROM AUTHOR]