Biophilic Design 4.0 in Smart Buildings: A Conceptual Review of AI, IoT, and Neuroarchitecture
Keywords:
Artificial intelligence, Biophilic design smart buildings, Digital biophilia, Internet of Things, Neuroarchitecture, Nature dose, Occupant well-beingAbstract
The integration of biophilic design with artificial intelligence (AI), the Internet of Things (IoT), and neuroarchitecture has created an opportunity to rethink smart buildings as environments that can support both operational performance and occupant well-being. Conventional biophilic design has emphasized natural materials, vegetation, daylight, water, views, biomorphic forms, and spatial qualities that evoke nature. However, many applications remain static, visually driven, or checklist-based, and are rarely evaluated through comparable measures of exposure, human response, and long-term building performance. This narrative review synthesizes literature on biophilic design, smart buildings, digital nature, indoor environmental quality, AI-supported assessment, IoT-enabled sensing, and neuroarchitectural evaluation. It argues that Biophilic Design 4.0 is not simply a digital extension of earlier biophilic practice, but a governance-aware framework in which natural stimuli are measured, interpreted, adapted, and validated through human-centered evidence. The review is organized around four transitions: from static natural features to measurable nature dose; from passive exposure to adaptive multisensory intervention; from subjective evaluation to psychophysiological triangulation; and from energy-centered smart buildings to human-centered smart environments. Evidence from nature-dose studies, VR and EEG experiments, AI-supported curation, and tropical smart-building case studies suggests that adaptive biophilic systems may improve perceived restoration, comfort, affect, attention, and energy-performance trade-offs. Yet the evidence remains fragmented, often short-term, and difficult to compare across methods, cultures, climates, and building types. The article proposes an integrative framework and research agenda emphasizing standardized metrics, longitudinal field validation, privacy-preserving AI, equity, and climate-sensitive implementation.
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