Human-Centred Generative AI for Affordable Housing Design in Nigeria Through an Architect-led Lifecycle Framework

Affordable housing in Nigeria requires architectural solutions that combine spatial efficiency, climate responsiveness, buildability, adaptability, maintainability and affordability in use. This critical structured narrative review examines how generative artificial intelligence (GenAI) can support architect-led but multidisciplinary affordable-housing design when combined with Building Information Modelling (BIM), conventional building-performance evaluation and post-occupancy learning, including digital-twin functions where technically and economically justified. Searches completed on 30 July 2026 used Scopus and Google Scholar for principal discovery, supplemented by targeted publisher-platform and institutional searches. The substantive analytical corpus comprises 43 peer-reviewed publications; review-methodology papers and policy, standards, legal and governance sources are interpreted separately. Evidence was coded across GenAI-specific option generation, conventional generative/parametric design, spatial efficiency, unit/block/site design, BIM information coordination, building-performance simulation, post-occupancy evaluation (POE), digital-twin applications, affordability, liveability and responsible AI. The synthesis indicates that GenAI is most defensible as a constrained exploratory option-generation layer whose outputs require independent architectural, statutory, performance and constructability verification; BIM supports information coordination and traceability rather than automatic validation; and POE provides the baseline lifecycle-learning mechanism, with digital twins representing an optional higher-maturity layer. Human-centredness is operationalised through household and resident evidence at briefing, option evaluation and post-occupancy stages, while accountable professional judgement remains explicit. The article proposes a six-stage lifecycle framework, Nigerian implementation-readiness conditions, an illustrative comparative pilot scenario and testable indicators for empirical validation against conventional design workflows.

generative artificial intelligence, affordable housing, architectural design, Building Information Modelling, Nigeria.

1. Abdul Mohit, M., & Abbas Iyanda, S. (2017). Low-income housing in Nigeria: A liveability investigation. Asian Journal of Quality of Life, 2(6), 43–51. https://doi.org/10.21834/ajqol.v2i6.45 [Google Scholar] [Crossref]

2. Abioye, S. O., Oyedele, L. O., Akanbi, L., Ajayi, A. O., Delgado, J. M. D., Bilal, M., Akinade, O. O., & Ahmed, A. (2021). Artificial intelligence in the construction industry: A review of present status, opportunities and future challenges. Journal of Building Engineering, 44, Article 103299. https://doi.org/10.1016/j.jobe.2021.103299 [Google Scholar] [Crossref]

3. AlBalkhy, W., Karmaoui, D., Ducoulombier, L., Lafhaj, Z., & Linner, T. (2024). Digital twins in the built environment: Definition, applications, and challenges. Automation in Construction, 162, Article 105368. https://doi.org/10.1016/j.autcon.2024.105368 [Google Scholar] [Crossref]

4. Architects (Registration, Etc.) Act, Cap. A19, Laws of the Federation of Nigeria 2004 (Nigeria). https://arconigeria.gov.ng/wp-content/uploads/2025/03/ARCHITECTS-REGISTRATION-OF-NIGERIA-ACT-1969.pdf [Google Scholar] [Crossref]

5. Arvin, S. A., & House, D. H. (2002). Modeling architectural design objectives in physically based space planning. Automation in Construction, 11(2), 213–225. https://doi.org/10.1016/S0926-5805(00)00099-6 [Google Scholar] [Crossref]

6. Attia, S., Hamdy, M., O’Brien, W., & Carlucci, S. (2013). Assessing gaps and needs for integrating building performance optimization tools in net zero energy buildings design. Energy and Buildings, 60, 110–124. https://doi.org/10.1016/j.enbuild.2013.01.016 [Google Scholar] [Crossref]

7. Batty, M., Axhausen, K. W., Giannotti, F., Pozdnoukhov, A., Bazzani, A., Wachowicz, M., Ouzounis, G., & Portugali, Y. (2012). Smart cities of the future. The European Physical Journal Special Topics, 214, 481–518. https://doi.org/10.1140/epjst/e2012-01703-3 [Google Scholar] [Crossref]

8. Becerik-Gerber, B., Jazizadeh, F., Li, N., & Calis, G. (2012). Application areas and data requirements for BIM-enabled facilities management. Journal of Construction Engineering and Management, 138(3), 431–442. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000433 [Google Scholar] [Crossref]

9. Boje, C., Guerriero, A., Kubicki, S., & Rezgui, Y. (2020). Towards a semantic construction digital twin: Directions for future research. Automation in Construction, 114, Article 103179. https://doi.org/10.1016/j.autcon.2020.103179 [Google Scholar] [Crossref]

10. Brysch, S. L., & Czischke, D. (2022). Affordability through design: The role of building costs in collaborative housing. Housing Studies, 37(10), 1800–1820. https://doi.org/10.1080/02673037.2021.2009778 [Google Scholar] [Crossref]

11. Darko, A., Chan, A. P. C., Adabre, M. A., Edwards, D. J., Hosseini, M. R., & Ameyaw, E. E. (2020). Artificial intelligence in the AEC industry: Scientometric analysis and visualization of research activities. Automation in Construction, 112, Article 103081. https://doi.org/10.1016/j.autcon.2020.103081 [Google Scholar] [Crossref]

12. Deng, M., Menassa, C. C., & Kamat, V. R. (2021). From BIM to digital twins: A systematic review of the evolution of intelligent building representations in the AEC-FM industry. Journal of Information Technology in Construction, 26, 58–83. https://doi.org/10.36680/j.itcon.2021.005 [Google Scholar] [Crossref]

13. Elwy, I., & Hagishima, A. (2024). The artificial intelligence reformation of sustainable building design approach: A systematic review on building design optimization methods using surrogate models. Energy and Buildings, 323, Article 114769. https://doi.org/10.1016/j.enbuild.2024.114769 [Google Scholar] [Crossref]

14. Evins, R. (2013). A review of computational optimisation methods applied to sustainable building design. Renewable and Sustainable Energy Reviews, 22, 230–245. https://doi.org/10.1016/j.rser.2013.02.004 [Google Scholar] [Crossref]

15. Ezennia, I. S., & Hoskara, S. O. (2019). Methodological weaknesses in the measurement approaches and concept of housing affordability used in housing research: A qualitative study. PLOS ONE, 14(8), Article e0221246. https://doi.org/10.1371/journal.pone.0221246 [Google Scholar] [Crossref]

16. Federal Ministry of Communications, Innovation and Digital Economy. (2025, September). National artificial intelligence strategy. Federal Republic of Nigeria. https://ncair.nitda.gov.ng/wp-content/uploads/2025/09/National-Artificial-Intelligence-Strategy-19092025.pdf [Google Scholar] [Crossref]

17. Federal Republic of Nigeria. (2006). National building code. LexisNexis Butterworths. [Google Scholar] [Crossref]

18. Gao, X., & Pishdad-Bozorgi, P. (2019). BIM-enabled facilities operation and maintenance: A review. Advanced Engineering Informatics, 39, 227–247. https://doi.org/10.1016/j.aei.2019.01.005 [Google Scholar] [Crossref]

19. Ghansah, F. A. (2025). Digital twins for smart building at the facility management stage: A systematic review of enablers, applications, and challenges. Smart and Sustainable Built Environment, 14(4), 1194–1229. https://doi.org/10.1108/SASBE-10-2023-0298 [Google Scholar] [Crossref]

20. Grant, M. J., & Booth, A. (2009). A typology of reviews: An analysis of 14 review types and associated methodologies. Health Information & Libraries Journal, 26(2), 91–108. https://doi.org/10.1111/j.1471-1842.2009.00848.x [Google Scholar] [Crossref]

21. Green, B. N., Johnson, C. D., & Adams, A. (2006). Writing narrative literature reviews for peer-reviewed journals: Secrets of the trade. Journal of Chiropractic Medicine, 5(3), 101–117. https://doi.org/10.1016/S0899-3467(07)60142-6 [Google Scholar] [Crossref]

22. Hakimi, O., Liu, H., & Abudayyeh, O. (2024). Digital twin-enabled smart facility management: A bibliometric review. Frontiers of Engineering Management, 11, 32–49. https://doi.org/10.1007/s42524-023-0254-4 [Google Scholar] [Crossref]

23. Hu, R., Huang, Z., Tang, Y., van Kaick, O., Zhang, H., & Huang, H. (2020). Graph2Plan: Learning floorplan generation from layout graphs. ACM Transactions on Graphics, 39(4), Article 118. https://doi.org/10.1145/3386569.3392391 [Google Scholar] [Crossref]

24. Ibem, E. O., & Aduwo, E. B. (2013). Assessment of residential satisfaction in public housing in Ogun State, Nigeria. Habitat International, 40, 163–175. https://doi.org/10.1016/j.habitatint.2013.04.001 [Google Scholar] [Crossref]

25. Intergovernmental Panel on Climate Change. (2022). Climate change 2022: Mitigation of climate change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (P. R. Shukla et al., Eds.). Cambridge University Press. https://doi.org/10.1017/9781009157926 [Google Scholar] [Crossref]

26. International Energy Agency. (2025). Buildings. In Energy efficiency 2025. https://www.iea.org/reports/energy-efficiency-2025/buildings [Google Scholar] [Crossref]

27. International Organization for Standardization. (2017). Buildings and constructed assets—Service life planning—Part 5: Life-cycle costing (ISO Standard No. 15686-5:2017). https://www.iso.org/standard/61148.html [Google Scholar] [Crossref]

28. International Organization for Standardization. (2018). Organization and digitization of information about buildings and civil engineering works, including building information modelling (BIM)—Information management using building information modelling—Part 1: Concepts and principles (ISO Standard No. 19650-1:2018). https://www.iso.org/standard/68078.html [Google Scholar] [Crossref]

29. International Organization for Standardization. (2023). Information technology—Artificial intelligence—Management system (ISO/IEC Standard No. 42001:2023). https://www.iso.org/standard/42001 [Google Scholar] [Crossref]

30. International Organization for Standardization. (2024). Industry Foundation Classes (IFC) for data sharing in the construction and facility management industries—Part 1: Data schema (ISO Standard No. 16739-1:2024). https://www.iso.org/standard/84123.html [Google Scholar] [Crossref]

31. Jang, S., Roh, H., & Lee, G. (2025). Generative AI in architectural design: Application, data, and evaluation methods. Automation in Construction, 174, Article 106174. https://doi.org/10.1016/j.autcon.2025.106174 [Google Scholar] [Crossref]

32. Khajavi, S. H., Motlagh, N. H., Jaribion, A., Werner, L. C., & Holmström, J. (2019). Digital twin: Vision, benefits, boundaries, and creation for buildings. IEEE Access, 7, 147406–147419. https://doi.org/10.1109/ACCESS.2019.2946515 [Google Scholar] [Crossref]

33. Li, J., Liu, Z., Han, G., Demian, P., & Osmani, M. (2024). The relationship between artificial intelligence (AI) and building information modeling (BIM) technologies for sustainable building in the context of smart cities. Sustainability, 16(24), 10848. https://doi.org/10.3390/su162410848 [Google Scholar] [Crossref]

34. Lu, Q., Parlikad, A. K., Woodall, P., Ranasinghe, G. D., Xie, X., Liang, Z., Konstantinou, E., Heaton, J., & Schooling, J. (2020). Developing a digital twin at building and city levels: Case study of West Cambridge Campus. Journal of Management in Engineering, 36(3), Article 05020004. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000763 [Google Scholar] [Crossref]

35. Machairas, V., Tsangrassoulis, A., & Axarli, K. (2014). Algorithms for optimization of building design: A review. Renewable and Sustainable Energy Reviews, 31, 101–112. https://doi.org/10.1016/j.rser.2013.11.036 [Google Scholar] [Crossref]

36. Makinde, O. O. (2014). Housing delivery system, need and demand. Environment, Development and Sustainability, 16, 49–69. https://doi.org/10.1007/s10668-013-9474-9 [Google Scholar] [Crossref]

37. Mulliner, E., & Maliene, V. (2015). An analysis of professional perceptions of criteria contributing to sustainable housing affordability. Sustainability, 7(1), 248–270. https://doi.org/10.3390/su7010248 [Google Scholar] [Crossref]

38. Mulliner, E., Smallbone, K., & Maliene, V. (2013). An assessment of sustainable housing affordability using a multiple criteria decision-making method. Omega, 41(2), 270–279. https://doi.org/10.1016/j.omega.2012.05.002 [Google Scholar] [Crossref]

39. Nagy, D., Lau, D., Locke, J., Stoddart, J., Villaggi, L., Wang, R., Zhao, D., & Benjamin, D. (2017). Project Discover: An application of generative design for architectural space planning. In Proceedings of the Symposium on Simulation for Architecture and Urban Design (pp. 49–56). https://doi.org/10.22360/simaud.2017.simaud.007 [Google Scholar] [Crossref]

40. Nauata, N., Chang, K. H., Cheng, C.-Y., Mori, G., & Furukawa, Y. (2020). House-GAN: Relational generative adversarial networks for graph-constrained house layout generation. In A. Vedaldi, H. Bischof, T. Brox, & J.-M. Frahm (Eds.), Computer Vision—ECCV 2020 (pp. 162–177). Springer. https://doi.org/10.1007/978-3-030-58545-7_10 [Google Scholar] [Crossref]

41. Nguyen, A. T., Reiter, S., & Rigo, P. (2014). A review on simulation-based optimization methods applied to building performance analysis. Applied Energy, 113, 1043–1058. https://doi.org/10.1016/j.apenergy.2013.08.061 [Google Scholar] [Crossref]

42. Nguyen, T. D., & Adhikari, S. (2023). The role of BIM in integrating digital twin in building construction: A literature review. Sustainability, 15(13), Article 10462. https://doi.org/10.3390/su151310462 [Google Scholar] [Crossref]

43. Nigeria Data Protection Act, 2023, Act No. 37 of 2023 (Nigeria). https://ndpc.gov.ng/download/nigeria-data-protection-act-2023 [Google Scholar] [Crossref]

44. Ogundipe, K. E., Owolabi, J. D., Ogunbayo, B. F., & Aigbavboa, C. O. (2024). Exploring inhibiting factors to affordable housing provision in Lagos metropolitan city, Nigeria. Frontiers in Built Environment, 10, Article 1408776. https://doi.org/10.3389/fbuil.2024.1408776 [Google Scholar] [Crossref]

45. Opoku, D. G. J., Perera, S., Osei-Kyei, R., & Rashidi, M. (2021). Digital twin application in the construction industry: A literature review. Journal of Building Engineering, 40, Article 102726. https://doi.org/10.1016/j.jobe.2021.102726 [Google Scholar] [Crossref]

46. Organisation for Economic Co-operation and Development. (2024). OECD AI principles. https://www.oecd.org/en/topics/sub-issues/ai-principles.html [Google Scholar] [Crossref]

47. Pan, Y., & Zhang, L. (2021). Roles of artificial intelligence in construction engineering and management: A critical review and future trends. Automation in Construction, 122, Article 103517. https://doi.org/10.1016/j.autcon.2020.103517 [Google Scholar] [Crossref]

48. Pärn, E. A., Edwards, D. J., & Sing, M. C. P. (2017). The building information modelling trajectory in facilities management: A review. Automation in Construction, 75, 45–55. https://doi.org/10.1016/j.autcon.2016.12.003 [Google Scholar] [Crossref]

49. Pinti, L., Codinhoto, R., & Bonelli, S. (2022). A review of building information modelling for facility management: Implementation in public organisations. Applied Sciences, 12(3), Article 1540. https://doi.org/10.3390/app12031540 [Google Scholar] [Crossref]

50. Sacks, R., Girolami, M., & Brilakis, I. (2020). Building information modelling, artificial intelligence and construction tech. Developments in the Built Environment, 4, Article 100011. https://doi.org/10.1016/j.dibe.2020.100011 [Google Scholar] [Crossref]

51. Shahat, E., Hyun, C. T., & Yeom, C. (2021). City digital twin potentials: A review and research agenda. Sustainability, 13(6), Article 3386. https://doi.org/10.3390/su13063386 [Google Scholar] [Crossref]

52. Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, 333–339. https://doi.org/10.1016/j.jbusres.2019.07.039 [Google Scholar] [Crossref]

53. Ukoha, O. M., & Beamish, J. O. (1997). Assessment of residents’ satisfaction with public housing in Abuja, Nigeria. Habitat International, 21(4), 445–460. https://doi.org/10.1016/S0197-3975(97)00017-9 [Google Scholar] [Crossref]

54. United Nations Department of Economic and Social Affairs. (n.d.). Sustainable Development Goal 11: Make cities and human settlements inclusive, safe, resilient and sustainable. Retrieved July 6, 2026, from https://sdgs.un.org/goals/goal11 [Google Scholar] [Crossref]

55. United Nations Educational, Scientific and Cultural Organization. (2021). Recommendation on the ethics of artificial intelligence. https://unesdoc.unesco.org/ark:/48223/pf0000380455 [Google Scholar] [Crossref]

56. United Nations Environment Programme. (2026). Global status report for buildings and construction 2025–2026: As climate risks rise and cities grow, we must rethink how we build to create better lives for all. https://doi.org/10.59117/20.500.11822/49531 [Google Scholar] [Crossref]

57. United Nations Human Settlements Programme. (2026). World cities report 2026: The global housing crisis: Pathways to action. https://unhabitat.org/world-cities-report-2026 [Google Scholar] [Crossref]

58. Weil, C., Bibri, S. E., Longchamp, R., Golay, F., & Alahi, A. (2023). Urban digital twin challenges: A systematic review and perspectives for sustainable smart cities. Sustainable Cities and Society, 99, Article 104862. https://doi.org/10.1016/j.scs.2023.104862 [Google Scholar] [Crossref]

59. White, G., Zink, A., Codecá, L., & Clarke, S. (2021). A digital twin smart city for citizen feedback. Cities, 110, Article 103064. https://doi.org/10.1016/j.cities.2020.103064 [Google Scholar] [Crossref]

60. Yan, S., Wu, C., & Zhang, Y. (2026). Generative design for architectural spatial layouts: A review of technical approaches. Journal of Asian Architecture and Building Engineering, 25(4), 2775–2795. https://doi.org/10.1080/13467581.2025.2512235 [Google Scholar] [Crossref]

61. Yigitcanlar, T., Kamruzzaman, M., Foth, M., Sabatini-Marques, J., da Costa, E., & Ioppolo, G. (2019). Can cities become smart without being sustainable? A systematic review of the literature. Sustainable Cities and Society, 45, 348–365. https://doi.org/10.1016/j.scs.2018.11.033 [Google Scholar] [Crossref]

• Impact of AI Tools on Student Learning
• Repogent: An Autonomous Multi-Agent System for End-To-End Repository Maintenance
• Trust-Centric Federated Learning Using Blockchain-Enabled Smart Contracts: An Analysis of Data Privacy, Model Integrity, and Decentralized AI Governance
• Can AI Care? Rethinking Ethics, Clinical Integrity, and Professional Responsibility in the Digital Age of Mental Healthcare
• AI-Based Smart Irrigation System Using IoT and Machine Learning for Precision Agriculture