ChatGPT versus engineering education assessment: a multidisciplinary and multi-institutional benchmarking and analysis of this generative artificial intelligence tool to investigate assessment integrity

ChatGPT, a sophisticated online chatbot, sent shockwaves through many sectors once reports filtered through that it could pass exams. In higher education, it has raised many questions about the authenticity of assessment and challenges in detecting plagiarism. Amongst the resulting frenetic hubbub,...

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Bibliographic Details
Published in:European journal of engineering education 2023-07, Vol.48 (4), p.559-614
Main Authors: Nikolic, Sasha, Daniel, Scott, Haque, Rezwanul, Belkina, Marina, Hassan, Ghulam M., Grundy, Sarah, Lyden, Sarah, Neal, Peter, Sandison, Caz
Format: Article
Language:English
Subjects:
Artificial Intelligence
Artificial intelligence (AI)
assessment
Benchmarking
Chatbots
ChatGPT
Colleges & universities
Computer Assisted Testing
Engineering Education
Evaluation Methods
Foreign Countries
Generative artificial intelligence
GPT-3
Integrity
Interdisciplinary Approach
Performance Based Assessment
Prompting
Shock waves
Thinking Skills
Undergraduate Students
Universities
Writing Evaluation
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Summary:ChatGPT, a sophisticated online chatbot, sent shockwaves through many sectors once reports filtered through that it could pass exams. In higher education, it has raised many questions about the authenticity of assessment and challenges in detecting plagiarism. Amongst the resulting frenetic hubbub, hints of potential opportunities in how ChatGPT could support learning and the development of critical thinking have also emerged. In this paper, we examine how ChatGPT may affect assessment in engineering education by exploring ChatGPT responses to existing assessment prompts from ten subjects across seven Australian universities. We explore the strengths and weaknesses of current assessment practice and discuss opportunities on how ChatGPT can be used to facilitate learning. As artificial intelligence is rapidly improving, this analysis sets a benchmark for ChatGPT's performance as of early 2023 in responding to engineering education assessment prompts. ChatGPT did pass some subjects and excelled with some assessment types. Findings suggest that changes in current practice are needed, as typically with little modification to the input prompts, ChatGPT could generate passable responses to many of the assessments, and it is only going to get better as future versions are trained on larger data sets.
ISSN:0304-3797
1469-5898
DOI:10.1080/03043797.2023.2213169