{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T16:18:29Z","timestamp":1764692309916,"version":"3.46.0"},"reference-count":38,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,11,30]],"date-time":"2025-11-30T00:00:00Z","timestamp":1764460800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Following PRISMA-ScR guidelines, this scoping review systematically maps the landscape of Large Language Models (LLMs) in mechanical engineering. A search of four major databases (Scopus, IEEE Xplore, ACM Digital Library, Web of Science) and a rigorous screening process yielded 66 studies for final analysis. The findings reveal a nascent, rapidly accelerating field, with over 68% of publications from 2024 (representing a year-on-year growth of 150% from 2023 to 2024), and applications concentrated on front-end design processes like conceptual design and Computer-Aided Design (CAD) generation. The technological landscape is dominated by OpenAI\u2019s GPT-4 variants. A persistent challenge identified is weak spatial and geometric reasoning, shifting the primary research bottleneck from traditional data scarcity to inherent model limitations. This, alongside reliability concerns, forms the main barrier to deeper integration into engineering workflows. A consensus on future directions points to the need for specialized datasets, multimodal inputs to ground models in engineering realities, and robust, engineering-specific benchmarks. This review concludes that LLMs are currently best positioned as powerful \u2018co-pilots\u2019 for engineers rather than autonomous designers, providing an evidence-based roadmap for researchers, practitioners, and educators.<\/jats:p>","DOI":"10.3390\/bdcc9120305","type":"journal-article","created":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T15:31:46Z","timestamp":1764689506000},"page":"305","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Large Language Models in Mechanical Engineering: A Scoping Review of Applications, Challenges, and Future Directions"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0231-5087","authenticated-orcid":false,"given":"Christopher","family":"Baker","sequence":"first","affiliation":[{"name":"School of Electronics, Electrical Engineering and Computer Science, School of Mechanical and Aerospace Engineering, Queen\u2019s University Belfast, Belfast BT7 1NN, UK"}]},{"given":"Karen","family":"Rafferty","sequence":"additional","affiliation":[{"name":"School of Electronics, Electrical Engineering and Computer Science, School of Mechanical and Aerospace Engineering, Queen\u2019s University Belfast, Belfast BT7 1NN, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4551-4457","authenticated-orcid":false,"given":"Mark","family":"Price","sequence":"additional","affiliation":[{"name":"School of Electronics, Electrical Engineering and Computer Science, School of Mechanical and Aerospace Engineering, Queen\u2019s University Belfast, Belfast BT7 1NN, UK"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,30]]},"reference":[{"key":"ref_1","unstructured":"Wang, A., Pruksachatkun, Y., Nangia, N., Singh, A., Michael, J., Hill, F., Levy, O., and Bowman, S. 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