Background: Generative Artificial Intelligence (AI) is rapidly becoming integral to design practice, yet its integration into interior architecture and design education faces challenges, creating a potential gap between academic preparation and industry needs. There is an urgent need for evidence-based strategies to integrate AI effectively while upholding core pedagogical values. Objective: This study systematically reviews recent literature on AI integration in interior architecture and design education to identify current patterns, pedagogical models, challenges, and opportunities, with specific implications for CIDA-accredited interior architecture and design programs. Methods: Following PRISMA 2020 guidelines, the author searched five databases (2021–2025) and screened records based on pre-defined eligibility criteria. Data from 80 included peer-reviewed studies were extracted and synthesized using thematic analysis, guided by postdigital pedagogy and human-centricity lenses. Results: Eight key themes emerged: (1) AI use is heavily concentrated in early design phases; (2) integration paradoxically reinforces the need for human-centric skills and judgment; (3) literacy demands are evolving from prompt fluency toward broader AI literacy; (4) pedagogical models are diversifying but often lack formal structure; (5) AI's role is expanding beyond generation to analysis and critique; (6) AI increasingly operates within larger technological ecosystems; (7) a quantifiable education-practice gap persists, particularly in later design phases; (8) Affective factors (motivation, anxiety) significantly influence adoption. Conclusion: AI in interior architecture and design education is advancing fastest in early ideation but remains comparatively underdeveloped in later, standards-intensive phases, indicating that the central curricular challenge is not whether to adopt AI, but how to position, bound, and assess it so that it strengthens auditable human judgment. In response, this paper proposes a CIDA-aligned curricular framework and phased implementation roadmap that link early creative leverage to later-stage rigor through phase-aware AI roles, transparency artifacts, ecosystem thinking, and human-centered evaluation.

Open-plan workplaces are often associated with increased sensory exposure, which may present challenges for adults with Attention-Deficit/Hyperactivity Disorder (ADHD), a condition characterized by atypical arousal regulation and sensory sensitivity. Although the Prospect–Refuge Theory suggests that spatial configuration may influence perceived security and attentional states, objective neurophysiological evidence in workplace contexts remains limited. This exploratory pilot study employed a mixed design to examine whether desk orientation and office enclosure were associated with differences in neural activity among adults with ADHD (n = 6). Four desk configurations were tested within each office setting, while two office types (Open Office and Enclosed Private Office) were examined between participants. Neurophysiological data were collected using portable electroencephalography (EEG), and power spectral density (PSD) across canonical frequency bands was analyzed during standardized cognitive tasks. Results indicated context-dependent spatial effects. In the Open Office setting, configurations providing both outward visibility and visual backing were associated with lower beta and gamma power relative to orientations lacking these features. In the Enclosed Private Office, orientation-related differences were not statistically significant. These preliminary findings suggest that desk orientation may influence neural indicators of cognitive demand in open-plan environments. Given the small sample size, results should be interpreted cautiously but contribute initial physiological evidence to neurodiversity-informed workplace research.

This study explores possibilities for the integration of artificial intelligence (AI) image generators into architectural education, examining the intersection of traditional design processes and emerging AI technologies. While practitioners increasingly adopt AI in architectural practice, its incorporation in educational settings remains relatively unexplored. The research addresses the question of how to offer students the opportunity to use AI image generators while maintaining essential traditional educational content. The methodology combines the lived experience and the exploratory case study approaches, spanning three semesters. Initial observations revealed student skepticism and resistance, with a controlled integration strategy allowing AI usage only after the initial 3D modeling. Subsequent semesters witnessed increased student acceptance and curiosity, showcasing improved 3D visualization quality while maintaining design integrity. Design charrettes utilizing AI underscored the importance of clear design goals, albeit with occasional deviations from the original concept. The discussion emphasizes the nuanced dynamics between AI-enhanced and traditional approaches, offering guidelines for AI integration. Prohibiting initial AI usage and strategic instructor guidance emerged as effective strategies. The study highlights the need for a balanced integration approach, empowering students with AI tools while preserving core architectural principles. The importance of fundamental visualization skills is underscored, positioning AI as a complementary enhancement rather than a replacement. In conclusion, this research contributes to the discourse on responsible AI integration in architectural education. The controlled integration approach demonstrates improved visualization quality without compromising design integrity. The study encourages educators, practitioners, and policymakers to envision a future where AI enhances foundational skills, fostering a collaborative dialogue on the evolving role of technology in architectural education.