This research proposes a novel conceptual framework that combines the concepts of Human-Computer Interaction (HCI) and Ambient Intelligence (AmI). The proposed framework aims to shed light on the importance of considering the needs and the social interactions of various building occupants in different types of buildings and designing HBI strategies accordingly. Specifically, we take educational buildings as a case that is less explored in the HBI research and apply the proposed framework, investigating how HBI strategies and interactions should be designed to address the needs of students, as primary occupants. Focus groups and semi-structured interviews were conducted among students in a flagship smart engineering building at Virginia Tech. Qualitative coding and concept mapping were used to analyze the qualitative data and determine the impact of occupant-specific needs on the learning experience of students. “Finding study space” was found to have the highest direct impact on the learning experience of students, and “Indoor Environment Quality (IEQ)” was found to have the highest indirect impact. The results show a clear need to integrate occupant needs in designing HBI strategies in different types of buildings. Finally, we discuss new ideas for designing potential Intelligent User Interfaces (IUI) to address the identified needs.

Analysing hierarchical design processes is difficult due to the technical and organizational dependencies spanning over multiple levels. The V-Model of Systems Engineering considers multiple levels. It is, however, not quantitative. We propose a model for simulating hierarchical product design processes based on the V-Model. It includes, first, a product model which structures physical product properties in a hierarchical dependency graph; second, an organizational model which formalizes the assignment of stakeholder responsibility; third, a process model which describes the top-down and bottom-up flow of design information; fourth, an actor model which simulates the combination of product, organization and process by using computational agents. The quantitative model is applied to a simple design problem with three stakeholders and three separate areas of responsibility. The results show the following phenomena observed in real-world product design: design iterations occur naturally as a consequence of the designers’ individual behaviour; inconsistencies in designs emerge and are resolved. The simple design problem is used to compare point-based and interval-based requirement decomposition quantitatively. It is shown that development time can be reduced significantly by using interval-based requirements if requirements are always broken down immediately.

The design provides innovative solutions to problems in the medical field. Collaboration between design and medicine can be fostered in several ways; however, educational programs linking these two academic fields are limited, and their frameworks and effectiveness are unknown. Hence, we launched an educational project to address medical problems through design. The framework and creative outcomes are based on the results of two consecutive one-year programs. The research subjects were 35 participants from three departments. The majority (22/35, 63%) were master’s and doctoral students in design. Eight participants were doctoral students and researchers who volunteered from the surgery, oral surgery, neurology and nursing departments at the Graduate School of Medicine and Hospital. The impact of the program on creativity was evaluated by the quality of ideas and the participants’ assessments. In total, 424 problems were identified and 387 ideas were created. Nine prototypes with mock-ups and functional models of products, games or service designs were created and positively evaluated for novelty, workability and relevance. Participants benefitted from the collaboration and gained new perspectives. Career expectations increased after the class, whereas motivation and skills remained high. A framework for a continuing educational program was suggested.

Text-to-image models are enabling efficient design space exploration, rapidly generating images from text prompts. However, many generative AI tools are imperfect for product design applications as they are not built for the goals and requirements of product design. The unclear link between text input and image output further complicates their application. This work empirically investigates design space exploration strategies that can successfully yield product images that are feasible, novel and aesthetic – three common goals in product design. Specifically, users’ actions within the global and local editing modes, including their time spent, prompt length, mono versus multi-criteria prompts, and goal orientation of prompts, are analyzed. Key findings reveal the pivotal role of mono versus multi-criteria and goal orientation of prompts in achieving specific design goals over time and prompt length. The study recommends prioritizing the use of multi-criteria prompts for feasibility and novelty during global editing while favoring mono-criteria prompts for aesthetics during local editing. Overall, this article underscores the nuanced relationship between the AI-driven text-to-image models and their effectiveness in product design, urging designers to carefully structure prompts during different editing modes to better meet the unique demands of product design.