Bridging the Gap between Neuroarchitecture, Artificial Intelligence, and Sustainability: A Systematic Review of Mental Health in the Built Environment | ||||
| JES. Journal of Engineering Sciences | ||||
| Article 13, Volume 53, Issue 5, September and October 2025, Page 688-705 PDF (434.61 K) | ||||
| Document Type: Review Paper | ||||
| DOI: 10.21608/jesaun.2025.364554.1438 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Esraa Elazab   1; Ahmed ELtawil2
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| 1Dept. of Arch. Eng., Mansoura University, Mansoura, Egypt | ||||
| 2Dept. of Arch. Eng., Delta university for science and technology, Mansoura, Egypt | ||||
| Abstract | ||||
| Abstract This paper presents a systematic review exploring the intersection of neuroarchitecture, artificial intelligence (AI), and sustainability, with a specific focus on their collective impact on mental health within the built environment. As urban populations grow, the need for architectural designs that integrate both environmental sustainability and psychological well-being has become increasingly important. Neuroarchitecture draws from neuroscience to design spaces that influence cognitive function, emotional states, phycology and behaviour , while AI provides advanced tools to optimize these spaces for both mental health outcomes and resource efficiency. This review synthesizes recent advancements in neuroarchitectural design, highlighting the role of AI in dynamically adjusting architectural elements based on real-time cognitive mapping data (e.g., EEG, fMRI, and wearable sensors). Additionally, the integration of AI with sustainable design principles allows for adaptive environments that not only reduce environmental impact but also enhance occupant well-being by minimizing stress and promoting cognitive clarity. The scientific contribution of this review lies in identifying critical research gaps, particularly the limited empirical studies connecting these three fields into a unified framework, and proposing future directions for the use of AI-driven neuroarchitecture-sustainable adaptive designs, that support mental health and sustainability. Ethical considerations related to data privacy and consent are also discussed, providing a comprehensive understanding of the potential and challenges in designing AI-driven neuroarchitecture-sustainable adaptive designs. | ||||
| Keywords | ||||
| Neuroarchitecture; Artificial intelligence; sustainable built environment; mental health | ||||
| References | ||||
|
[1] WHO. (2022). The Impact of COVID-19 on Mental Health: A WHO Report. World Health Organization.
[2] Smith, K. R. (2021). Urban mental health disparities: A systematic review of global trends. The Lancet Psychiatry. Built Environment Journal.
[3] WorldEconomicForum. (2021). The Global Economic Burden of Mental Health Disorders. World Economic Forum.
[4] Jones, A. T. (2020). Urban stress and its effects on cognitive function: A global review. Journal of Urban Mental Health.
[5] Lederbogen, F. K.-L. (2019). City living and urban upbringing affect neural social stress processing in humans. Nature.
[6] Singh, M. M. (2022). Air pollution, stress, and neurodegenerative diseases: Insights from the developing world. Environmental Health Perspectives.
[7] Assem, H. M. (2023). Designing for human wellbeing: The integration of neuroarchitecture in design – A systematic review. Ain Shams Engineering Journal, 102102.
[8] Gou, Z. &. (2021). "The Influence of Neuroarchitecture on Cognitive Function and Emotional Well-being in Built Environments." . Frontiers in Psychology.
[9] Alex Coburn, O. V. (2017). Buildings, Beauty, and the Brain: A Neuroscience of Architectural Experience . Journal of Cognitive Neuroscience , 1521-1531.
[10] Küller, R. (2004). An Emotional Model of Human-Environment Interaction. Evaluation in Progress - Strategies for Environmental Research and Implementation (IAPS 18 Conference Proceedings (pp. 56-60). Vienna: International Association for People-Environment Studies (IAPS).
[11] Kaplan, S. (1995). The restorative benefits of nature: Toward an integrative framework. Journal of Environmental Psychology, 169-182.
[12] Wang, J. &. (2021). The impact of indoor environmental quality on mental health: A review of current literature. Journal of Environmental Psychology.
[13] Valentine, C. (2024). The Impact of Architectural Form on Physiological Stress: A Systematic Review. Sec. Human-Media Interaction.
[14] Kweon, B. S. (2021). "The Impact of Sustainable Urban Design on Mental Health and Well-being: A Review of Current Literature.". Journal of Environmental Psychology.
[15] López, C. &. (2021). The social dimension of sustainability in the built environment: A semi-systematic review. Sustainable Cities and Society, 103212.
[16] Barton, H. &. (2020). "A Health- and Sustainability-Oriented Approach to Urban Design: The Role of Green Spaces in Enhancing Mental Health.". Journal of Urban Health.
[17] Jiang, B. L. (2020). Connecting urban green spaces to mental health: A multi-dimensional framework. Environmental Research.
[18] González, D. &. (2022). "Artificial Intelligence in Architecture: Advancements and Applications in Sustainable Urban Design.". Journal of Building Performance.
[19] Zhao, X. &. (2020). "AI and Stress Management in the Built Environment.". Journal of Sustainable Design.
[20] Hu, X. M.-L. (2023). Using hidden Markov modeling to analyze neurocognitive activation patterns in design concept generation. AI EDAM.
[21] Chiu, T. L. (2023). Natural language processing for visualizing design project progress: Capturing cognitive processes in design education. AI EDAM.
[22] Han, J. C. (2024). Applications of artificial intelligence and cognitive science in design. AI EDAM.
[23] Sim, K. Y. (2025). Envisioning an AI-enhanced mental health ecosystem. arXiv preprint arXiv, 14883.
[24] Adityo, A. (2024). The role of neuroscience and artificial intelligence in biophilic architectural design based on the principle of symbiosis. JARINA, 3(2), 81–94.
[25] Gibson, J. (2021). "The Role of Artificial Intelligence in Optimizing Human Well-Being in Smart Buildings.". AI in Architecture.
[26] Moher, D. L. (2015). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Med.
[27] Turner, R. G. (2020). Human time-activity patterns and implications for indoor environmental quality. . Indoor Air Quality Journal.
[28] Wang, P. W. (2020). Neurocognition-inspired Design with Machine Learning. Design science, e33.
[29] UN. (2021). World Urbanization Prospects 2021: Highlights. Department of Economic and Social Affairs, Population Division.
[30] Jiang, B. (2022). The role of AI and AR in optimizing green building designs. Sustainable architecture review.
[31] Zhou, X. Y. (2021). The mental health impact of urbanization: A review of emerging evidence and preventive strategies. Urban studies.
[32] Kaplan, R. (2019). The restorative benefits of nature: Toward an integrative framework. Journal of Environmental Psychology.
[33] Gonzalez, R. S. (2020). Sustainable building design and mental health outcomes: A review. Sustainable Cities and Society.
[34] Santos, V. C. (2023). Neuroarchitecture: how the built environment influences the human brain. Revista Científica Multidisciplinar Núcleo do Conhecimento.
[35] Hartig, T. M. (2020). Nature and Health. Annual Review of Public Health.
[36] Huang, Y. &. (2021). Indoor Air Quality and the Rise of Sick Building Syndrome. Environmental Health Perspectives.
[37] Wheeler, K. T. (2022). Green Space and Urban Health Inequalities: A Meta-Analysis. Journal of Urban Sustainability
[38] Garcia-Rodriguez, M. T. (2022). The Impact of Classroom Design on Cognitive Performance: An AI and Neuroscience Perspective. Journal of Environmental Psychology.
[39] Smith, P. L. (2020). Architecture, Stress, and Wellbeing: A Cross-Sectional Study of Urban Living.
[40] Kellert, S. R. (2020). Biophilic Design: The Theory, Science, and Practice of Bringing Buildings to Life. John Wiley & Sons.
[41] Jiang, S. L. (2022). The impact of sustainable design strategies on mental well-being: A systematic review. Building and Environment, 108926.
[42] Ono, H. Y. (2021). Impact of Repetitive Architectural Patterns on Visual Stress and Cognitive Function. Journal of Environmental Psychology.
[43] Gao, H. W. (2023). AI-Based Energy Management for Smart Buildings: Enhancing Sustainability through Real-Time Adjustments. Energy and Buildings.
[44] Chen, M. Z. (2024). AI-Driven Urban Planning for Green Infrastructure Optimization: A Path to Sustainable Cities. Journal of Urban Technology.
[45] Jiang, W. X. (2024). Leveraging AI for Sustainable Material Selection in Building Design: A Life- Cycle Approach. Journal of Cleaner Production.
[46] Lee, S. K. (2023). Digital Twin Applications in Building Operations: AI-Driven Solutions for Sustainability. Journal of Building Engineering.
[47] Nguyen, L. L. (2023). Machine Learning for Neuroarchitectural Design: Analyzing Brain Responses to Architectural Environments. Journal of Cognitive Neuroscience.
[48] Martinez-Soto, J. S. (2023). AI-Enhanced Personalization of Neuroarchitectural Spaces: Reducing Stress Through Tailored Environmental Design. Frontiers in Neuroscience.
[49] Lee, M. (2023). Wearable technology and AI for dynamic environmental adjustment in architecture. Journal of Sustainable Design and Technology.
[50] Zhang, X. W. (2022). AI-Driven Predictive Analytics for Green Space Design: Impacts on Urban Mental Health. Urban Ecology Journal.
[51] Kumar, S. P. (2024). Neurofeedback-Driven Adaptive Environments: AI Applications in Workspace Design for Cognitive Health. AI in Architecture Journal.
[52] Bratman, G. N. (2019). Nature and mental health: An ecosystem service perspective. Science Advances, eaax0903.
[53] Alhasoun, F. A. (2021). Machine learning for pedestrian dynamics: Modeling and anomaly detection in urban environments. IEEE Transactions on Intelligent Transportation Systems, 5665–5675.
[54] Liu, Y. &. (2021). Smart soundscape design: AI-driven adaptive soundscapes for urban public spaces. Journal of Urban Technology, 95–114.
[55] Zheng, Y. C. (2014). Urban computing: Concepts, methodologies, and applications. ACM Transactions on Intelligent Systems and Technology, 38.
[56] Zhou, W. H. (2017). Does spatial configuration matter? Understanding the effects of land cover pattern on urban heat mitigation. Landscape.
[57] Sharifi, A. (2021). Co-benefits and synergies between urban climate change mitigation and adaptation measures: A literature review. Sustainable Cities and Society, 72, 103027.
[58] Asdrubali, F. D. (2015). A review of unconventional sustainable building insulation materials. Sustainable Materials and Technologies, 1-17.
[59] Küller, R. (2004). An Emotional Model of Human-Environment Interaction. Evaluation in Progress - Strategies for Environmental Research and Implementation (IAPS 18 Conference Proceedings (pp. 56-60). Vienna: International Association for People-Environment Studies (IAPS).
[60] Garcia, R. T. (2021). Designing for Wellbeing: The Influence of Architectural Elements on Health. Health and Architecture Research Journal.
[61] Vartanian, O. N.-M. (2015). Neuroarchitecture: how the built environment influences the human brain. Journal of Cognitive Neuroscience, 1051-1056. | ||||
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