Assessing Large Language Models in Mechanical Engineering Education: A
  Study on Mechanics-Focused Conceptual Understanding
Jie Tian Jixin Hou Zihao Wu Peng Shu Zhengliang Liu Yujie Xiang Beikang Gu Nicholas Filla Yiwei Li Ning Liu Xianyan Chen Keke Tang Tianming Liu Xianqiao Wang
This study is a pioneering endeavor to investigate the capabilities of Large
Language Models (LLMs) in addressing conceptual questions within the domain of
mechanical engineering with a focus on mechanics. Our examination involves a
manually crafted exam encompassing 126 multiple-choice questions, spanning
various aspects of mechanics courses, including Fluid Mechanics, Mechanical
Vibration, Engineering Statics and Dynamics, Mechanics of Materials, Theory of
Elasticity, and Continuum Mechanics. Three LLMs, including ChatGPT (GPT-3.5),
ChatGPT (GPT-4), and Claude (Claude-2.1), were subjected to evaluation against
engineering faculties and students with or without mechanical engineering
background. The findings reveal GPT-4's superior performance over the other two
LLMs and human cohorts in answering questions across various mechanics topics,
except for Continuum Mechanics. This signals the potential future improvements
for GPT models in handling symbolic calculations and tensor analyses. The
performances of LLMs were all significantly improved with explanations prompted
prior to direct responses, underscoring the crucial role of prompt engineering.
Interestingly, GPT-3.5 demonstrates improved performance with prompts covering
a broader domain, while GPT-4 excels with prompts focusing on specific
subjects. Finally, GPT-4 exhibits notable advancements in mitigating input
bias, as evidenced by guessing preferences for humans. This study unveils the
substantial potential of LLMs as highly knowledgeable assistants in both
mechanical pedagogy and scientific research.