Collaboration with GenAI in engineering research design

Abstract

Over the past five years, the fast development and use of generative artificial intelligence (GenAI) and large language models (LLMs) has ushered in a new era of study, teaching, and learning in many domains. The role that GenAIs can play in engineering research is addressed. The related previous works report on the potential of GenAIs in the literature review process. However, such potential is not demonstrated by case studies and practical examples. The previous works also do not address how GenAIs can assist with all the steps traditionally taken to design research. This study examines the effectiveness of collaboration with GenAIs at various stages of research design. It explores whether collaboration with GenAIs can result in more focused and comprehensive outcomes. A generalised approach for collaboration with AI tools in research design is proposed. A case study to develop a research design on the concept of “shared machine-human driving” is deployed to show the validity of the articulated concepts. The case study demonstrates both the pros and cons of collaboration with GenAIs. The results generated at each stage are rigorously validated and thoroughly examined to ensure they remain free from inaccuracies or hallucinations and align with the original research objectives. When necessary, the results are manually adjusted and refined to uphold their integrity and accuracy. The findings produced by the various GenAI models utilized in this study highlight the key attributes of generative artificial intelligence, namely speed, efficiency, and scope. However, they also underscore the critical importance of researcher oversight, as unexamined inferences and interpretations can render the results irrelevant or meaningless.