With the growing complexity of design technology and the emergence of intelligent design assistance, architectural studio classes are facing a new pedagogical paradigm. The digital literacy of younger generations and availability of scientific simulation have the potential to transform the traditional master–apprentice model. In our experiment, we had students perform a museum layout task and observed their behaviors from three perspectives: (1) how students utilize an assistance tool and whether we can group their behavior, (2) how the new simulation-aided design process is different from a traditional one, particularly in terms of the evolution of solution over iteration, and (3) whether students' behavior is affected by the type of design problem given. Protocol analysis on design processes and interviews revealed that individual’s characteristic design processes in terms of iteration ranged from distinct iterations guided by simulation to monotonous progress with little simulation. When comparing between an instructor and software, it was not the given environment but the designer’s subjectivity that determined their attitudes toward either type of feedback. Lastly, the challenge of integrating design concepts with performance requirements stemmed from a misalignment between their true evaluative measures. We propose that a versatile design platform implement real-time, non-intrusive mechanisms for performance reporting and solution branching, and include social and psychological measures as well as physical ones in order to expand designers’ concept choices.

Despite the widespread application of Autonomous Vehicles (AV) to various services, there has been relatively little research carried out on pedestrian–AV interaction and trust within the context of service provided by AV. This study explores the communication design strategy promoting a pedestrian’s trust and positive attitude to driverless services within the context of pedestrian–AV interaction using non-verbal social cues. An empirical study was conducted with an experimental VR environment to measure participants’ intimacy, trust, and brand attitude toward AV. Further understanding of their social interaction experiences was explored through semi-structured interviews. As a result of the study, the interaction effect of social cues was found, and it was revealed that brand attitude was formed by the direct effects of intimacy and trust as well as the indirect effects of intimacy through trust’s mediation. Furthermore, ‘Conceptual Definition of Space’ was identified to generate differences in the interplay among intimacy, trust, and brand attitude according to social cues. Quantitative and qualitative results were synthesized to discuss implications considering the service context. Practical implications were also addressed suggesting specific design strategies for utilizing the sociality of AV.

Machine learning, particularly classification algorithms, constructs mathematical models from labeled data that can predict labels for new data. Using its capability to identify distinguishing patterns among multi-dimensional data, we investigated the impact of three factors on the observation of architectural scenes: Individuality, education, and image stimuli. An analysis of the eye-tracking data revealed that (1) a velocity histogram was unique to individuals, (2) students of architecture and other disciplines could be distinguished via endogenous parameters, but (3) they were more distinct in terms of seeking structural versus symbolic elements. Because of the reverse nature of the classification algorithms that automatically learn from data, we could identify relevant parameters and distinguishing eye-tracking patterns that have not been reported in previous studies.