Reliance on Artificial Intelligence Tools May Displace Research Skills Acquisition Within Engineering Doctoral Programmes: Examples and Implications

The escalation in capabilities of Large Language Models has triggered urgent discussions about their implications for tertiary education, particularly regarding how they might facilitate academic misconduct in graded engineering coursework. However, graduate research education — where a student works closely with a supervisor over years to develop both implicit and explicit research skills — has received comparatively less attention in this discussion. This paper seeks to develop this discourse by presenting targeted case studies that explore the opportunities and threats posed by artificial intelligence to engineering doctoral education. For instance, using a specimen exercise from a PhD-level research skills module, we demonstrate how artificial intelligence tools can now deeply penetrate research workflows in technical computing and scripting. We likewise investigate the capabilities of chatbot tools to assist engineering PhD candidates with the broader research skills central to their training and development. These include writing and proofreading theses and research papers, producing data visualizations, simulating peer review processes, and preparing scientific diagrams. By evaluating the capabilities and limitations of extant artificial intelligence in these areas, we can discuss both the potential benefits and ethical concerns of doctoral students engaging with such assistance.

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Publication Details

Published
2024-11-06
DOI
https://doi.org/10.1109/ithet61869.2024.10837618
Primary Topic
Biomedical and Engineering Education
Type
article
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article

Reliance on Artificial Intelligence Tools May Displace Research Skills Acquisition Within Engineering Doctoral Programmes: Examples and Implications

Paul Cuffe, Jan Przybyszewski, Yevhenii Mormul, Andrew Nakoud
Biomedical and Engineering Education
article

Reliance on Artificial Intelligence Tools May Displace Research Skills Acquisition Within Engineering Doctoral Programmes: Examples and Implications

Paul Cuffe, Jan Przybyszewski, Yevhenii Mormul, Andrew Nakoud
article en

Abstract

The escalation in capabilities of Large Language Models has triggered urgent discussions about their implications for tertiary education, particularly regarding how they might facilitate academic misconduct in graded engineering coursework. However, graduate research education — where a student works closely with a supervisor over years to develop both implicit and explicit research skills — has received comparatively less attention in this discussion. This paper seeks to develop this discourse by presenting targeted case studies that explore the opportunities and threats posed by artificial intelligence to engineering doctoral education. For instance, using a specimen exercise from a PhD-level research skills module, we demonstrate how artificial intelligence tools can now deeply penetrate research workflows in technical computing and scripting. We likewise investigate the capabilities of chatbot tools to assist engineering PhD candidates with the broader research skills central to their training and development. These include writing and proofreading theses and research papers, producing data visualizations, simulating peer review processes, and preparing scientific diagrams. By evaluating the capabilities and limitations of extant artificial intelligence in these areas, we can discuss both the potential benefits and ethical concerns of doctoral students engaging with such assistance.

University College Dublin (IE)
Quality Education
Openalex Percentile: Top 76%
Biomedical and Engineering Education
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