Exploring the Utilisation of Generative AI Tools by Undergraduate First-Year Mechanical Engineering Students in Programming Assessments.

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Lama Hamadeh
https://orcid.org/0000-0002-1766-532X

Abstract

Integrating the fundamentals of computer science and programming skills into the undergraduate engineering curriculum has been a primary focus for many educational institutions worldwide. Learning the basics of programming from the beginning of undergraduate engineering education allows students to incorporate such skills into their future work easily. Therefore, an introductory programming course for first-year undergraduate students has been running since 2021 in the Mechanical Engineering Department at University College London intending to teach the fundamentals of Python programming language. However, it is well-known that generative artificial intelligence (Gen AI) tools in higher education are moving so fast that a wait-and-see approach cannot be taken. These applications have received much global attention from academics on their impact and proper use within the teaching-learning process. This paper investigates first-year undergraduate mechanical engineering students' use of Gen AI tools in their programming assessment. The results show that 60% of the cohort used tools that helped mainly to check their code, improve their English language, and understand error messages. However, 40% abstained from using any. Based on these findings, recommendations on how Gen AI tools can be utilised by undergraduate students in ways that support their learning and enhance their ability to achieve learning outcomes are made.

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How to Cite
Hamadeh, L. (2024). Exploring the Utilisation of Generative AI Tools by Undergraduate First-Year Mechanical Engineering Students in Programming Assessments. SEFI Journal of Engineering Education Advancement, 1(1), 39–52. https://doi.org/10.62492/sefijeea.v1i1.20
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Author Biography

Lama Hamadeh, University College London

After completing her PhD in theoretical and mathematical physics at the University of Nottingham in 2015, Dr Hamadeh joined the Product Design Department at the School of Architecture, Design & the Built Environment at Nottingham Trent University as a research assistant where she worked on an EPSRC funded project on analysing mathematically the thermal behaviour of electronic devices embedded in smart textiles samples. Then in 2017, Dr Hamadeh joined the Department of Physics and Mathematics, School of Science and Technology at Nottingham Trent University as a postdoctoral research fellow where she developed and applied sophisticated image analysis routines to objectively and statistically quantify the patterns seen in an extensive database of dried blood droplets. In 2018, and after the fellowship ended, she was appointed as a lecturer in physics and mathematics at the same department. She joined UCL as a lecturer in Engineering Mathematics and Computing in 2021.