AI-Driven Knowledge Transfer in Architectural Education

Yara Hirsekorn; Nik Ansre; Gregor Grunwald

doi:10.38027/smart.v2n1-8

Authors

Yara Hirsekorn Jade University of Applied Sciences Oldenburg, Germany Author https://orcid.org/0009-0001-7275-2450
Nik Ansre Jade University of Applied Sciences Oldenburg, Germany Author https://orcid.org/0009-0006-4080-4398
Prof. Dr.-Ing. Gregor Grunwald Jade University of Applied Sciences Oldenburg, Germany Author https://orcid.org/0000-0003-2437-398X

DOI:

https://doi.org/10.38027/smart.v2n1-8

Keywords:

AI, Architecture, Knowledge Transfer, Retrieval-Augmented-Generation, Digital Education, Model Optimization

Abstract

The use of large language models (LLMs) in architecture is still at an early stage, but is becoming increasingly important due to the need for local, domain-specific assistance systems. This study investigates how such models can be developed and extended under local hardware conditions in order to provide architecture-specific knowledge in a context-appropriate manner. For this purpose, a methodological approach was chosen that combines lightweight fine-tuning with a two-stage retrieval augmented generation system (RAG). Two model series were tested: Mini-Spyra for dialogue-oriented knowledge retrieval and IwI-Spyra for semantic analysis of structured planning data (e.g. IFC). The results show that domain-specific training in combination with dynamic knowledge integration leads to significantly more precise, comprehensible answers. The article provides a transferable model for the use of AI in teaching and planning practice - data protection-compliant, locally executable and didactically comprehensible. The study thus contributes to the development of explainable AI tools in an architectural context. This research was conducted within a "Young Researchers" project supported by the Jade University of Applied Sciences.

References

Ansre, N., Hirsekorn, Y., & Grunwald, G. (2025). Wissensmanagement 2.0 – die AUFLADEN KI für eine neue Dimension der Wissensvermittlung. In T. Luhmann & T. Sieberth (Eds.), Photogrammetrie – Laserscanning – Optische 3D-Messtechnik: Beiträge der Oldenburger 3D-Tage und des BIMtags 2025 (pp. 282–291). Wichmann Verlag.

Baugesetzbuch: Mit Immobilienwertermittlungsverordnung, Baunutzungsverordnung, Planzeichenverordnung, Raumordnungsgesetz, Raumordnungsverordnung: Textausgabe mit ausführlichem Sachverzeichnis und einer Einführung. (2024). C. H. Beck.

Calcagno, G., Alves, S., & Grunwald, G. (2024). Assessing the quality of an innovative learning path for BIM education: The DIGITAL DECATHLON. Journal of Civil Construction and Environmental Engineering, 9(5), 143–150. https://doi.org/10.11648/j.jccee.20240905.11

Calcagno, G., Bertelli, M., & Grunwald, G. (2024). The Digital Decathlon: A journey in building information modelling education. Journal of Mediterranean Cities, 4(1). https://doi.org/10.38027/mediterranean-cities_vol4no1_3

Chen, J., Xiao, S., Zhang, P., Luo, K., Lian, D., & Liu, Z. (2024). BGE M3-Embedding: Multi-lingual, multi-functionality, multi-granularity text embeddings through self-knowledge distillation (ArXiv Preprint No. arXiv:2402.03216). arXiv. https://doi.org/10.48550/arXiv.2402.03216

Damen, T., Sebastian, R., MacDonald, M., Soetanto, D., Hartmann, T., Di Giulio, R., Bonsma, P., & Luig, K. (2015). The application of BIM as collaborative design technology for collective self-organised housing. International Journal of 3-D Information Modeling, 4(1), 1–18. https://doi.org/10.4018/ij3dim.2015010101

Enevoldsen, K., Chung, I., Kerboua, I., Kardos, M., Mathur, A., Stap, D., … Muennighoff, N. (2025). MMTEB: Massive multilingual text embedding benchmark (ArXiv Preprint No. arXiv:2502.13595). arXiv. https://doi.org/10.48550/arXiv.2502.13595

Grattafiori, A., Dubey, A., Jauhri, A., Pandey, A., Kadian, A., Al-Dahle, A., … Ma, Z. (2024, 31 July). The Llama 3 herd of models (ArXiv Preprint No. arXiv:2407.21783). arXiv. https://arxiv.org/abs/2407.21783

Groenmeyer, L., & Grunwald, G. (2024). Integration of AI and LLMs for enhanced BIM analysis: A study on IFC model interpretation. In AI-Driven Architecture: Pioneering the Digital Frontier (1st ed., pp. 117–144). Alanya University Publication. https://doi.org/10.38027/ai-driven-6

Grunwald, G., & Heins, C. (2023). BIM Game: A testing ground for specifying, modelling, evaluating and visualising information in IFC formats. In Lecture Notes in Civil Engineering (pp. 677–688). https://doi.org/10.1007/978-981-99-4049-3_52

Gregor, G., Sharina, A., Matteo, B., Gisella, C., Ireneusz, C., Emilia, D., … Zuber, L. (2025). Building BIM competence: Learning in the DIGITAL DECATHLON. In Innovative Renewable Energy (pp. 981–997). https://doi.org/10.1007/978-3-031-82323-7_77

Hajirasouli, A., & Banihashemi, S. (2022). Augmented reality in architecture and construction education: State of the field and opportunities. International Journal of Educational Technology in Higher Education, 19(1), Article 61. https://doi.org/10.1186/s41239-022-00343-9

Hanke, T. (2024). AUFLADEN – The web portal for self-study in the field of digital planning and construction. In T. Luhmann & T. Sieberth (Eds.), Photogrammetry, Laser Scanning, Optical 3D Metrology – Contributions to the Oldenburg 3D Days and the BIM Day 2024. VDE Verlag.

Hanke, T., & Grunwald, G. (2024). Artificial intelligence as a design tool in the architectural design process. In AI-Driven Architecture: Pioneering the Digital Frontier (1st ed., pp. 53–71). Alanya University Publication. https://doi.org/10.38027/ai-driven-3

Hanke, T., Kawasaki, J. Y., & Grunwald, G. (2023). Manufacturing processes of complex shapes and structures using 3D printing and augmented reality. Proceedings of the International Conference of Contemporary Affairs in Architecture and Urbanism (ICCAUA), 6(1), 62–66. https://doi.org/10.38027/iccaua2023en0134

Heins, C., & Grunwald, G. (2024). BIM and IPA – Excerpt of an automated assessment system for an autodidactic teaching concept. In Proceedings of the 41st International Symposium on Automation and Robotics in Construction (ISARC 2024) (pp. 830–837). https://doi.org/10.22260/ISARC2024/0001

Heins, C., Grunwald, G., & Helmus, M. (2021). Gamification and BIM – The didactic guidance of decentralised interactions of a real-life BIM business game for higher education. In Proceedings of the 38th International Symposium on Automation and Robotics in Construction (ISARC 2021) (pp. 932–939). https://doi.org/10.22260/ISARC2021/0126

Kawasaki, J., & Grunwald, G. (2023). Enhanced learning and teaching through AR: Case studies on design-build projects. JSEE Annual Conference International Session Proceedings, 2023, 18–19. https://doi.org/10.20549/jseeen.2023.0_18

Kawasaki, J. Y., Hirsekorn, Y., Ansre, N., & Grunwald, G. (2024). AUFLADEN LAB – Parametric AI-supported design. In T. Luhmann & T. Sieberth (Eds.), Photogrammetry, Laser Scanning, Optical 3D Metrology – Contributions to the Oldenburg 3D Days and the BIM Day 2024. VDE Verlag.

Leite, R. M. C., Winkler, I., & Alves, L. R. G. (2022). Visual management and gamification: An innovation for disseminating information about production to construction professionals. Applied Sciences, 12(11), 5682. https://doi.org/10.3390/app12115682

Michail, A., Clematide, S., & Sennrich, R. (2025, 12 February). Examining multilingual embedding models cross-lingually through LLM-generated adversarial examples (ArXiv Preprint No. arXiv:2502.08638). arXiv. https://doi.org/10.48550/arXiv.2502.08638

Özener, O. Ö. (2023). Context-based learning for BIM: Simulative role-playing games for strategic business implementations. Smart and Sustainable Built Environment, 13(4), 908–933. https://doi.org/10.1108/SASBE-08-2022-0184

Patil, A., & Jadon, A. (2025). Advancing reasoning in large language models: Promising methods and approaches (ArXiv Preprint No. arXiv:2502.03671). arXiv. https://doi.org/10.48550/arXiv.2502.03671

Yang, A., Yang, B., Zhang, B., Hui, B., Zheng, B., Yu, B., … Qiu, Z. (2024, 19 December). QWen 2.5 technical report (ArXiv Preprint No. arXiv:2412.15115). arXiv. https://doi.org/10.48550/arXiv.2412.15115

Reinmann, G., & Watanabe, A. (2024). KI in der universitären Lehre. In De Gruyter eBooks (pp. 29–46). https://doi.org/10.1515/9783111351490-004

Tasnia, T., & Grunwald, G. (2024). Evaluate the outcome of the digital learning platform AUFLADEN. In T. Luhmann & T. Sieberth (Eds.), Photogrammetry, Laser Scanning, Optical 3D Metrology – Contributions to the Oldenburg 3D Days and the BIM Day 2024. VDE Verlag.

Wang, P., Liu, T., Wang, C., Wang, Y., Yan, S., Jia, C., … Yu, Y. (2025, 10 June). A survey on large language models for mathematical reasoning (ArXiv Preprint No. arXiv:2506.08446). arXiv. https://doi.org/10.48550/arXiv.2506.08446

Xu, F., Hao, Q., Zong, Z., Wang, J., Zhang, Y., Wang, J., … Li, Y. (2025). Towards large reasoning models: A survey of reinforced reasoning with large language models (ArXiv Preprint No. arXiv:2501.09686). arXiv. https://doi.org/10.48550/arXiv.2501.09686

Xu, X., Li, M., Tao, C., Shen, T., Cheng, R., Li, J., … Zhou, T. (2024). A survey on knowledge distillation of large language models (ArXiv Preprint No. arXiv:2402.13116). arXiv. https://doi.org/10.48550/arXiv.2402.13116

Yalçın, Z. Ö. (2024). Integrating gamification with BIM for enhancing participatory design. Journal of Computational Design, 5(2), 317–344. https://doi.org/10.53710/jcode.1505309

Yin, S., Fu, C., Zhao, S., Li, K., Sun, X., Xu, T., & Chen, E. (2024). A survey on multimodal large language models. National Science Review, 11(12), nwae403. https://doi.org/10.1093/nsr/nwae403

Zhao, W. X., Zhou, K., Li, J., Tang, T., Wang, X., Hou, Y., … Wen, J. (2023). A survey of large language models (ArXiv Preprint No. arXiv:2303.18223). arXiv. https://doi.org/10.48550/arXiv.2303.18223

Zheng, C., Zhang, Z., Zhang, B., Lin, R., Lu, K., Yu, B., … Lin, J. (2024). ProcessBench: Identifying process errors in mathematical reasoning (ArXiv Preprint No. arXiv:2412.06559). arXiv. https://doi.org/10.48550/arXiv.2412.06559