This study investigates the perception of spaciousness in interior environments using screen-based assessments with eye tracking, and virtual reality (VR) technologies. The research explores how four key design elements -view access, view content, materiality, and ceiling geometry- influence perceived spaciousness. Thirty-five college students participated in screen-based and VR-based evaluations of 16 photorealistic interior settings. Eye tracking data were collected during screen-based assessments to analyze visual attention patterns. Statistical analyses included repeated measures ANOVAs, pairwise comparisons, and correlations between screen and VR assessments. Results showed that view access significantly affected perceived spaciousness in both screen and VR conditions, with larger windows correlating to higher spaciousness ratings. Materiality also demonstrated significant effects, with natural and textured materials perceived as more spacious than concrete surfaces. View content and ceiling geometry showed trends towards influencing spaciousness perception but did not reach statistical significance. VR presentations generally yielded higher spaciousness ratings compared to screen-based presentations, suggesting that immersive technologies may enhance spatial perception. Eye tracking analyses revealed common gaze patterns and variations in visual attention across different design conditions. This study contributes to the understanding of how design elements influence spatial perception and demonstrates the potential of integrating eye tracking and VR technologies in environmental psychology research. The findings have implications for evidence-based design practices aimed at enhancing perceived spaciousness in interior environments.

Given the increasing prevalence of anxiety and depression, this research aims to identify design features that enhance the sense of restoration, with the goal of supporting mental and behavioral healthcare facility design. This study employed both screen-based and virtual reality (VR) stimuli to evaluate the perceived restorativeness of different interior settings. The key variables analyzed included window view access, view content, materiality, and room geometry. Thirty-five undergraduate and graduate students assessed 16 distinct interior environments. Findings indicate that the VR presentations generally produced higher restorativeness scores compared with screen-based presentations, though this effect varied across stimuli. Repeated-measures ANOVA revealed that larger windows consistently correlated with higher restorativeness scores in both presentation modes. Views of water were rated as most restorative, followed by wooded areas. Natural materials were perceived as significantly more restorative than other materials, particularly in VR presentations. Varied ceiling designs, especially vaulted ceilings, were associated with evaluations of higher restorativeness compared with flat ceiling designs, with this effect more pronounced in VR. This research underscores the potential of VR technology to simulate and assess interior design interventions, offering insights into creating more effective and personalized restorative environments in mental health treatment facilities. The findings can inform evidence-based design strategies for healthcare spaces, supporting treatment processes and patient well-being.

Design studio education requires sustained attention and prolonged occupancy of a shared workspace, making environmental opportunities for brief recovery potentially relevant to students’ well-being. This exploratory study used affordance theory as an interpretive lens to examine the environmental modifications students proposed from their assigned desk positions and the extent to which sketch content was associated with academic year, studio location, and desk-level window view access. The dataset comprised 71 structured sketches, geometric view metrics for 85 workstations that were linked to the 71 sketch participants for inferential analyses, and a nonmatched lighting survey administered to the same cohorts one year later (n = 57). Sketches were coded into 17 modification categories. Water and Collaborative/Social Space were retained descriptively but excluded from inferential interpretation because of quasi-complete separation. Fifteen ordinary logistic regression models were evaluated with Benjamini–Hochberg false-discovery-rate correction, together with a complementary specification omitting studio location. Plants (73.2%), natural shapes/patterns (52.1%), and electric light quality (52.1%) were the most frequent modifications. In the primary models, Technology (q = 0.033) and Plants (q = 0.047) retained FDR-supported omnibus fit; only the academic-year coefficient for Technology retained FDR support (OR = 0.17, 95% CI [0.06, 0.50], q = 0.021). The study identifies environmental preferences and hypotheses for future intervention in restorative learning environments research.

Generative artificial intelligence (AI) is rapidly reshaping studio-based design education, yet relatively little is known about how students decide when and how AI should support their design process. This study examined students’ use, adaptation, and perceptions of generative AI, with particular attention to task-specific use, contextual adaptation, and factors associated with AI-related decision-making. An online survey of 112 undergraduate and graduate design students combined structured questionnaire items with optional open-ended responses to examine AI use across studio tasks, project stages, and learning contexts. Students reported higher AI use for concept development, research, and written articulation than for visual refinement, technical documentation, and later stages of design development. Students also reported becoming more selective in their AI use as projects progressed, while instructor expectations, peer influence, and concern about negative judgment were associated with students’ AI-related decisions. Qualitative responses further highlighted authorship, learning, and professional responsibility as important considerations. Together, these findings suggest that students’ selective and context-dependent AI use can be interpreted as boundary-setting around when and how AI participates in the design process. Rather than viewing AI use simply as technology adoption, the findings suggest value in a judgment-centered, process-based approach to AI pedagogy that emphasizes accountability and reflective decision-making.

Editorial Board to volume 9, issue 2 of Journal of Daylighting Header of solarlits.com