Bard, ChatGPT and 3DGPT: a scientometric analysis of generative AI tools and assessment of implications for mechanical engineering education

• Published in: Interactive technology and smart education Vol. 21; no. 4; pp. 588 - 624
• Main Authors: Mustapha, Khameel B., Yap, Eng Hwa, Abakr, Yousif Abdalla
• Format: Journal Article
• Language: English
• Published: Bingley Emerald Group Publishing Limited 30.10.2024
• Subjects: Artificial Intelligence; Bibliometrics; Chatbots; Competence; Democratization; Design analysis; Educational Technology; Engineering education; Engineering profession; Experimentation; Generative artificial intelligence; Image processing; Language; Language Processing; Large language models; Materials selection; Mechanical engineering; MOOCs; Natural Language Processing; Product design; Professional Education; Recognition (Achievement); Scientific Research; Scientometrics; Technical information; Troubleshooting
• ISSN: 1741-5659; 1758-8510
• DOI: 10.1108/ITSE-10-2023-0198

PurposeFollowing the recent rise in generative artificial intelligence (GenAI) tools, fundamental questions about their wider impacts have started to reverberate around various disciplines. This study aims to track the unfolding landscape of general issues surrounding GenAI tools and to elucidate the specific opportunities and limitations of these tools as part of the technology-assisted enhancement of mechanical engineering education and professional practices.Design/methodology/approachAs part of the investigation, the authors conduct and present a brief scientometric analysis of recently published studies to unravel the emerging trend on the subject matter. Furthermore, experimentation was done with selected GenAI tools (Bard, ChatGPT, DALL.E and 3DGPT) for mechanical engineering-related tasks.FindingsThe study identified several pedagogical and professional opportunities and guidelines for deploying GenAI tools in mechanical engineering. Besides, the study highlights some pitfalls of GenAI tools for analytical reasoning tasks (e.g., subtle errors in computation involving unit conversions) and sketching/image generation tasks (e.g., poor demonstration of symmetry).Originality/valueTo the best of the authors’ knowledge, this study presents the first thorough assessment of the potential of GenAI from the lens of the mechanical engineering field. Combining scientometric analysis, experimentation and pedagogical insights, the study provides a unique focus on the implications of GenAI tools for material selection/discovery in product design, manufacturing troubleshooting, technical documentation and product positioning, among others.