A Hybrid AHP–Machine Learning Framework for Integrating Generative AI as a Co-Creative Partner in Interior Design Engineering Studios

Authors

  • Aseel Al-Ayash Architectural and Interior Design Engineering Department, College of Engineering, Gulf University, Sanad 26489, Kingdom of Bahrain Author
  • Mazin Arabasy Al-Ahliyya Amman University, Faculty of Architecture and Design/ Digital Interior Design Department; AsSalt, Jordan, Post code 19111 Author
  • Mayyadah Hussein University of Petra, Faculty of Architecture and Design, Department of Interior Design; Amman, Jordan, P.O Box 961343, Post Code 11196 Author
  • Yasser Farghaly Egyptian Russian University, Faculty of Applied Arts, Interior Design Department, Cairo, Egypt, Post code: 12566 Author
  • Mariam Edwar Architectural and Interior Design Engineering Department, College of Engineering, Gulf University, Sanad 26489, Kingdom of Bahrain Author

DOI:

https://doi.org/10.70917/fce-2026-016

Keywords:

Generative AI, Multi-criteria decision-making, Machine learning

Abstract

This research focuses on the increasing but poorly specified use of Generative Artificial Intelligence in interior design engineering studios, which in the past have been focused on adopting the tool and perception-based evaluation rather than conducting engineering-level reproducible evaluation of the learning that is taking place in the co-creative processes. It evaluates a framework for assessing co-creativity using three stages (Dialogical Exploration, Critical Synthesis, and Critical Synthesis) along with an Analytic Hierarchy Process (AHP) model for weighting the co-creativity criteria, the resulting Co-Creative Performance Index (CCPI), and machine learning with explainability for predicting performance based on process behaviors. Outcome analysis from the analytic hierarchy process (AHP) indicated that the relative weights were on Creativity and Conceptual Diversity (weight w = 0.20), Critical Thinking and Justification (weight w = 0.19), and AI Literacy and Prompting Competence (weight w = 0.18), with sound expert consistency (CR = 0.06). The CCPI increased dramatically from .329 (SD = .032) prior to intervention to .599 (SD = .027), securing a mean gain of .270 (SD = .046). Predictive analysis showed that co-creative performance is a behaviorally learnable process with XGBoost achieving strong cross-validated accuracy (R^2 = 0.82, RMSE = 0.041, MAE = 0.033).

References

Ahmed, S., Alam, M., & Sarker, J. (2025). LIME and SHAP interpreters with ML-based diabetes predictions. IEEE Access. https://doi.org/10.1109/ACCESS.2024.3422319 DOI: https://doi.org/10.1109/ACCESS.2024.3422319

Alharthi, A. (2025). Generative AI in game design: Enhancing creativity or constraining innovation? Journal of Intelligence, 13. https://doi.org/10.3390/jintelligence13060060 DOI: https://doi.org/10.3390/jintelligence13060060

Aliouane, N., Belkadi, F., & Cheikhrouhou, M. (2025). Integrating deep learning and SHAP for model explainability. IEEE Access.

Alnusairat, S., & Abu Qadourah, J. (2025). Moving to green technologies: Acceptance and perception of architectural solar photovoltaic technology for residential buildings. Architectural Engineering and Design Management, 21(2), 338–355. https://doi.org/10.1080/17452007.2024.2361819 DOI: https://doi.org/10.1080/17452007.2024.2361819

Amro, D., & Ammar, S. (2018). The role of university education in directing architects towards conserving the sustainability of the urban cultural heritage (comparative study). Al-Balqa Journal for Research and Studies, 21(2), 87–108. https://doi.org/10.35875/1105-021-002005 DOI: https://doi.org/10.35875/1105-021-002-005

Anantrasirichai, N., & Bull, D. (2020). Artificial intelligence in the creative industries: A review. Artificial Intelligence Review, 55, 589–656. https://doi.org/10.1007/s10462-021-10039-7 DOI: https://doi.org/10.1007/s10462-021-10039-7

Arabasy, M., & Ghoneim, R. S. (2025). Enhancing sustainable urban design using machine learning: Comparative analysis of seven metaheuristic algorithms in energy-efficient digital architecture. Frontiers in Built Environment, 11, Article 1526209. https://doi.org/10.3389/fbuil.2025.1526209 DOI: https://doi.org/10.3389/fbuil.2025.1526209

Banh, L., & Strobel, G. (2023). Generative artificial intelligence. Electronic Markets, 33, 1–17. https://doi.org/10.1007/s12525-023-00680-1 DOI: https://doi.org/10.1007/s12525-023-00680-1

Chen, Y., Qin, Q., & Zhang, R. (2025). GDT framework: Integrating generative design and design thinking for sustainable development in the AI era. Sustainability. https://doi.org/10.3390/su17010372 DOI: https://doi.org/10.3390/su17010372

Chiu, T. K. F., Meng, C., & Wong, J. (2021). Creation and evaluation of a pretertiary artificial intelligence curriculum. IEEE Transactions on Education, 65, 30–39. DOI: https://doi.org/10.1109/TE.2021.3085878

Davis, N., & Rafner, J. (2025). AI drawing partner: Co-creative drawing agent and research platform to model co-creation. arXiv. https://doi.org/10.48550/arXiv.2501.06607

Dubey, P., Sharma, S., & Karsol, S. (2024). A novel conceptualization of AI literacy and empowering employee experience using generative AI and augmented analytics. Journal of Electrical Systems.

Epstein, Z., Hertzmann, A., & Herman, L. (2023). Art and the science of generative AI. Science, 380, 1110–1111. https://doi.org/10.52783/jes.2031 DOI: https://doi.org/10.1126/science.adh4451

Fitriawijaya, A., & Jeng, T. (2024). Integrating multimodal generative AI and blockchain for enhancing generative design in the early phase of architectural design process. Buildings. https://doi.org/10.3390/buildings14082533 DOI: https://doi.org/10.20944/preprints202407.1437.v1

Fleischmann, K. (2024). Generative artificial intelligence in graphic design education: A student perspective. Canadian Journal of Learning and Technology. https://doi.org/10.21432/cjlt28618 DOI: https://doi.org/10.21432/cjlt28618

Ghoneim, R. (2025). Machine learning for adaptive facade design: Enhancing thermal performance in urban architecture. Civil Engineering and Architecture, 13(2), 1273–1288. https://doi.org/10.13189/cea.2025.130239 DOI: https://doi.org/10.13189/cea.2025.130239

Gozalo-Brizuela, R., & Garrido-Merchán, E. C. (2023). A survey of generative AI applications. arXiv. https://doi.org/10.48550/arXiv.2306.02781

Gupta, P., & Kyaw, K. (2025). Insights informed generative AI for design: Incorporating real-world data for text-to-image output. arXiv. https://doi.org/10.48550/arXiv.2506.15008

Jain, A., Kerne, C., & Gassaway, L. (2024). How could AI support design education? A study across fields fuels situating analytics. arXiv. https://doi.org/10.48550/arXiv.2404.17390

Kabir, M. (2024). Unleashing human potential: A framework for augmenting co-creation with generative AI. In International Conference on AI Research. https://doi.org/10.34190/icair.4.1.3074 DOI: https://doi.org/10.34190/icair.4.1.3074

Kioskli, M., Papastergiou, S., & Frangos, K. (2023). A swarm-based clinical validation framework of artificial intelligence solutions. Journal of Engineering Research and Science. https://doi.org/10.55708/js0209001 DOI: https://doi.org/10.55708/js0209001

Li, C., Zhang, T., Du, X., Zhang, Y., & Xie, H. (2024). Generative AI models for different steps in architectural design: A literature review. https://doi.org/10.1016/j.foar.2024.10.001 DOI: https://doi.org/10.1016/j.foar.2024.10.001

Liang, A., Qin, M., & Feng, Y. (2024). Mathematics and machine creativity. arXiv.

Lin, K. (2025). Cognitio Emergens: Agency, dimensions, and dynamics in human–AI knowledge co-creation. arXiv.

Liu, H., Bansal, R., & Liang, J. (2025). Generative AI in artistic enterprises. International Journal on Semantic Web and Information Systems. https://doi.org/10.4018/IJSWIS.367444 DOI: https://doi.org/10.4018/IJSWIS.367444

Liu, H., Wu, P., Li, X., & Mo, W. (2024). Application and renovation evaluation of Dalian's industrial architectural heritage based on AHP and AIGC. PLOS ONE, 19. https://doi.org/10.1371/journal.pone.0312282 DOI: https://doi.org/10.1371/journal.pone.0312282

Lorenz, P., Perset, K., & Berryhill, J. (2023). Initial policy considerations for generative artificial intelligence (OECD Artificial Intelligence Papers). https://doi.org/10.1787/fae2d1e6-en DOI: https://doi.org/10.1787/fae2d1e6-en

Mittal, U., Sai, S., Chamola, V., & Sangwan, D. (2024). A comprehensive review on generative AI for education. IEEE Access, 12, 142733–142759. https://doi.org/10.1109/ACCESS.2024.3468368 DOI: https://doi.org/10.1109/ACCESS.2024.3468368

Morris, M. (2023). Scientists' perspectives on the potential for generative AI in their fields. arXiv. https://doi.org/10.48550/arXiv.2304.01420

Niu, D., Li, P., & Wang, Z. (2025). Decoding student cognitive abilities via explainable artificial intelligence. Scientific Reports. https://doi.org/10.1038/s41598-025-12514-5 DOI: https://doi.org/10.1038/s41598-025-12514-5

Ochoa, M., & Huang, X. (2025). Exploring the potential of generative AI to support non-experts in learning analytics practice. Journal of Learning Analytics, 12, 65–90. https://doi.org/10.18608/jla.2025.8615 DOI: https://doi.org/10.18608/jla.2025.8615

Onatayo, D., Onososen, A., Oyediran, A., Oyediran, H., Arowoiya, V., & Onatayo, E. (2024). Generative AI applications in architecture, engineering, and construction: Trends, implications for practice, education & imperatives for upskilling—A review. Architecture. https://doi.org/10.3390/architecture4040046 DOI: https://doi.org/10.3390/architecture4040046

Ponce-Bobadilla, A. V., Redondo, L., & Torres, F. (2024). A practical guide to SHAP analysis for interpretable machine learning. Clinical and Translational Science. https://doi.org/10.1111/cts.70056 DOI: https://doi.org/10.1111/cts.70056

Rathod, R. (2024). Renaissance on generative AI. International Journal for Research in Applied Science and Engineering Technology. DOI: https://doi.org/10.22214/ijraset.2024.63324

Schneider, J., Kilic, S., & Stockhammer, D. (2024). Empowering clients: Transformation of design processes due to generative AI. arXiv. https://doi.org/10.48550/arXiv.2411.15061

Shanmugam, D., Agrawal, M., & Movva, R. (2024). Generative AI in medicine. arXiv. https://doi.org/10.48550/arXiv.2412.10337

Singh, S., & Hindriks, V. (2025). A systematic review of human–AI co-creativity. arXiv. https://doi.org/10.48550/arXiv.2506.21333

Stadelmann, T., Keuzenkamp, M., & Gomez, F. (2021). The AI-Atlas: Didactics for teaching AI and machine learning on-site, online, and hybrid. Education Sciences. https://doi.org/10.3390/educsci11070318 DOI: https://doi.org/10.3390/educsci11070318

Talib, M., Al-Hgaish, M., & Rahman, M. (2025). Evaluating critical success factors in AI-driven drug discovery using AHP. IEEE Access. https://doi.org/10.1109/ACCESS.2025.3546925 DOI: https://doi.org/10.1109/ACCESS.2025.3546925

Wan, H., Zhang, J., Chen, Y., Xu, W., & Feng, F. (2024). Generative AI application for building industry. https://doi.org/10.1007/s12273-025-1279-x DOI: https://doi.org/10.1007/s12273-025-1279-x

Zhang, S., & Li, S. (2024). Confrontation or acceptance: Understanding novice visual artists' perception towards AI-assisted art creation. arXiv. https://doi.org/10.48550/arXiv.2410.14925

Zine, M., Harrou, M., & Sun, Y. (2025). Evaluating e-learning readiness using explainable machine learning. Education and Information Technologies. https://doi.org/10.1007/s10639-025-13335-9 DOI: https://doi.org/10.1007/s10639-025-13335-9

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2026-07-31 — Updated on 2026-10-21

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A Hybrid AHP–Machine Learning Framework for Integrating Generative AI as a Co-Creative Partner in Interior Design Engineering Studios. (2026). Future Cities and Environment, 12, 16. https://doi.org/10.70917/fce-2026-016 (Original work published 2026)