AI-Driven Assessment of Conceptual Understanding in Mechanical Engineering Mechanics

DOI

10.2991/978-94-6239-750-7_39How to use a DOI?

Keywords

Artificial intelligence; mechanical engineering education; conceptual under-standing; large language models; prompt engineering; and problem-solving assessment

Abstract

The study focuses on the use of artificial intelligence technology in mechanical engineering education, effectiveness in addressing basic mechanics problems. State-of-the-art language models were evaluated on a structured set of 126 conceptual and computational questions related to important mechanical disciplines, for example, fluid mechanics, dynamics, and elasticity. A comparison against 46 human subjects at four levels of expertise assesses accuracy, reasoning power, and the effect of optimised prompting strategies. The findings provide insights into the effectiveness of AI-assisted learning towards enhancing comprehension and problem-solving in mechanical engineering education.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Yash Amin
PY  - 2026
DA  - 2026/08/31
TI  - AI-Driven Assessment of Conceptual Understanding in Mechanical Engineering Mechanics
BT  - Proceedings of the International Conference on Advanced Design, Manufacturing, and Sustainable Energy Systems (ICADMSES 2026)
PB  - Atlantis Press
SP  - 549
EP  - 557
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6239-750-7_39
DO  - 10.2991/978-94-6239-750-7_39
ID  - Amin2026
ER  -