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The current ethical considerations of using GenAI in engineering education and practice: A systematic literature review
Conference proceeding

The current ethical considerations of using GenAI in engineering education and practice: A systematic literature review

Zachary Quince, Kathy Petkoff, Ruby N Michael, Scott Daniel and Sasha Nikolic
Proceedings of the 35th Annual Conference of the Australasian Association for Engineering Education (AAEE 2024), pp.509-517
35th Annual Conference of the Australasian Association for Engineering Education (AAEE 2024), 35th (Christchurch, New Zealand, 08/12/2024–11/12/2024)
12/2024
Appears in  Recent Faculty of Science and Engineering Publications

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Abstract

ethics GENAI systemic review Engineering education Higher education
CONTEXT: The rapid advancement and integration of Generative Artificial Intelligence (GenAI) into engineering education and practice have brought to light many ethical considerations. The potential of GenAI to change engineering processes and higher educational approaches requires an understanding of its ethical implications to ensure its responsible use. PURPOSE: This systematic literature review aims to identify and analyse the current ethical considerations associated with the use of GenAI in engineering education and practice. The review seeks to address the following research question: What are the current ethical considerations of utilising GenAI in engineering higher education and professional practice? APPROACH: This review employs the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework to systematically identify, select, and analyse relevant literature. The search strategy encompasses Scopus and AAEE conference proceedings, focusing on findings related to the ethical use of GenAI in engineering education and practice. The included studies area ssessed for their methodological rigour and relevance to the research questions. OUTCOMES: The review identifies ten key ethical considerations associated with GenAI in engineering, including bias, error, learning, sustainable practice, equity, intellectual ownership, competitive advantage and automation, misuse (academic misconduct), GenAI awareness, and transparency. The analysis reveals that these considerations span across individual, institutional, and societal levels, highlighting the multifaceted nature of ethical challenges posed by GenAI. The review also discusses the limitations of the current EA Code of Ethics in addressing GenAI-specific concerns and suggests potential revisions to ensure responsible GenAI integration. CONCLUSIONS: The findings underscore the critical need for ongoing adaptation of ethical frameworks to navigate the ethical considerations of GenAI in engineering. The review emphasises the importance of further research to explore the implications of GenAI. Further work includes reviewing the current practice of implementation by the engineering industry, continuing empirical research and understanding if GenAI and current policies align regarding equity and diversity in engineering.

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