Artificial intelligence is a transforming design practice. This research explores human-AI interaction in relation to human centred design principles in early stage design projects. Using a qualitative workshop methodology, this empirical study took a multidisciplinary team of participants from a yacht manufacturer through a series of divergent, discover phase activities that were augmented by AI tools. The results demonstrated how the advanced capabilities of AI to rapidly analyse vast quantities of data could be purposefully implemented to enhance engagement. the role off facilitator as an intermediary between the AI and participants allowed the interface between human and AI to be moderated and provided insights into effective effective use of AI during the fuzzy front end.

This study investigates the relationship between MBTI personality type diversity and team performance in the first-year “Introduction to Design” course at SUTD. Analysis reveals a statistically significant yet weak correlation between greater MBTI diversity and higher final project grades. Additionally, teams with more introverted (I) and intuitive (N) members tended to perform better, consistent with research linking introverts to deep reflection and structured decision-making, and intuitives to creative problem-solving and future-oriented thinking. Result also shows that teams with INTJ and ISTP members performed better, while those with ISFP members showed lower performance. While these findings suggest personality composition influences team performances, measured in terms of grades, further research is needed to establish causation and underlying mechanisms.

Undisciplined Design (UD) is an emerging approach suited for experiment-driven innovation and creative processes, allowing fluid disciplinary engagement in engineering design. However, its openness and adaptability also introduce challenges, particularly when integration, evaluation, and risk mitigation mechanisms are absent. This paper examines the Google Glass project through the lens of boundary objects, identifying two key dangers in UD: overconfidence in technological inevitability and unintended consequences. The analysis highlights the need for structured checkpoints to manage epistemic uncertainty while preserving UD’s exploratory potential. To address these challenges, we propose incorporating participatory design methods to facilitate cross-disciplinary negotiation and present a decision-making checklist to guide UD projects in product design and innovation.

This study explores the impact of new work practices on product development in an Engineering Simulator by comparing traditional practices with new ways of working in a compressed product development process. By introducing flexibility, digital tools, and autonomy, the study highlights improvements in individual productivity and innovation. For example, teams employing new work practices developed their first prototypes 30% faster than control groups. However, challenges in communication and team dynamics emerge, underscoring the need for structured support systems. The findings further suggest that while these modern practices foster creativity and efficiency, successful implementation at the organizational level requires balancing autonomy with clear guidelines and effective management. This study provides actionable insights for adapting new work methods to engineering environments.

This paper investigates the integration of player profiles and gamification elements into knowledge management practices within communities of practice engaged in engineering design. The study proposes a framework combining the MEREX method with gamification, tailored to Marczewski’s player types. The research aims to personalize knowledge sharing, promote user engagement, and structure engineering design knowledge effectively. The framework leverages MEREX sheets with a narrative format structured around phases of the engineering design process. Additionally, it features personalized knowledge maps and contributor profiles to foster collaboration, facilitate knowledge formalization, and encourage knowledge reuse. This integrated approach seeks to improve both community animation and overall knowledge management within engineering design contexts.

This research aimed to explore the challenges designers face when using asynchronous collaboration methods across different time zones. A literature review revealed a knowledge gap in comparing synchronous and asynchronous collaboration methods and in comparing design students and professional practice. To fill this gap, a study was conducted with a group of engineering design students and practitioners asking them to conduct two design exercises, one synchronously and one asynchronously. The results highlighted unique challenges faced and that experience of design process had little effect on performance when using unfamiliar design tasks. The study contributes new insights and firsthand recommendations for design teams, educators and software developers.

This paper presents and experimental study that compares the performance of teams of one, three, and six in terms of generation of requirements from given design prompts. Team size has not been fully explored in the literature in comparative experimental studies for requirements generation. The study was conducted with 116 teams of one, 86 teams of three, and 92 teams of six composed of pre-service engineers in an introductory engineering course. Two prompts were used for the in-class activity. Results indicate that the size of the team did not have significant influence on the number of requirements generated. However, this suggests that there is a difference in efficiency of generating requirements. Analyzing the variety, novelty, and completeness of the requirements generated is reserved for future work. This work helps to lay the foundation for justifying team size.

This study explores the integration of network analysis and CAD/PDM log data to analyze collaboration and activity patterns in a multi-year engineering project. Using logs from a collaborative CAD platform with PDM features, the research examines team interactions and network evolution over time. Key findings reveal that early project stages featured smaller, denser networks, while later stages saw larger, less interconnected structures. Subteam formations were dynamic, with variations in size and number. Individual-level analysis showed that user influence, measured through eigenvector centrality, did not always align with activity volume. This work highlights the potential of CAD/PDM data for understanding collaboration dynamics and lays the groundwork for further studies on team interactions in design processes.

The aim of this research is to analyze the potential of Generative Artificial Intelligence (GenAI) to support the design process and overcome creative fixation in teams during the initial problem framing, ideation and concept exploration stage. Fixation is a common problem in design, and can be exacerbated during collaborative work due to diverse issues such as team dynamics or perceived hierarchy. Current research is exploring whether AI can help teams overcome this problem or on the contrary, might actually contribute to it. Through a creative ideation workshop with design students, we investigate how AI influences team dynamics as well as the creative results. We propose a conceptual model to work with AI in a team setting.

In this paper, we explore the relationship between divergent thinking and stakeholder identification on 15 student engineering design teams. We examine the relationship between fluency, originality, flexibility, and elaboration on the Alternate Uses Task (AUT), a common measure of divergent thinking, and in stakeholder identification. We find fluency and originality to be positively and statistically significantly correlated between the AUT and stakeholder identification task. Flexibility was positively correlated and elaboration was negatively correlated; both lacked statistical significance. Our results suggest that divergent thinking and stakeholder identification may be correlated, and leveraging exercises to improve divergent thinking may also help improve stakeholder identification. Future work can continue to explore this relationship with larger sample sizes and additional tasks.

Hackathons have recently garnered significant research interest. Hackathon teams frequently include developer, business, and designer roles, yet the designer role and experience of design in hackathon teams are poorly understood. In this paper, we present findings from ten interviews with designer hackathon participants. A thematic analysis reveals that the responsibilities of designers at hackathons roughly align with more typical design contexts, although the format of hackathon events forces designers to adapt approaches to design. Hackathon participants value teams with diverse skills, including design skills, yet designers face resistance from peers in developer roles when seeking to use established design methods for validating needs and generating solutions. This tension can make designers feel unwelcome at hackathons, harming efforts to attract a more diverse participant pool.

Teams have been favored due to the diverse knowledge access. However, diversity can also have negative effects, and team outputs can be influenced by many factors, such as psychological safety. While the effects of psychological safety have been studied, its development has received less attention. Prior research in this area has focused either on specific populations or cross-sectional effects. To add to this area, this study examined the longitudinal development of psychological safety in engineering capstone students: how it evolves, and whether this can be influenced by team-related experiences. This study showed that although psychological safety did change meaningfully with time, neither time nor experience alone could capture the change. The results could shed light on the evolution of psychological safety, as well as what factors could potentially influence its development.

This paper explores the role of concept maps in investigating and highlighting project alignment through shared understanding within a multidisciplinary, long-term, creative, practice-based project called “Fish Project.” By combining surveys and concept maps, the case study investigates team dynamics, skill diversity, and evolving project comprehension. The project aims to integrate augmented reality and geo-locative technologies to cultivate care, community, and collaborative design through a digital fish ecosystem. Analysing concept map: metadata, topology, and vocabulary, the research highlights gaps in team alignment and provides areas for cohesive project visioning and execution. The findings underscore the importance of iterative communication tools to bridge interdisciplinary boundaries and strengthen team coherence.

This paper serves as a template for, and argument to, the engineering design research community to pre-register research studies. Pre-registering allows for a research plan to be validated and results published, no matter the findings. To support pre-registering, we propose a case study to study how individual perspectives and decision-making processes interact as design teams collaborate and reach consensus. We explore how narrative misalignments within a design team—disagreements on the best path forward—are shaped by individual perspectives. Driving requirements, requirements that reflect a designer's prime motivations, are used to shed light on individual priorities. A data collection and analysis plan are introduced to explain how the team will examine how consensus was achieved, which divergent personal interests persist, and how future decision-scenarios might be influenced.

AI is increasingly used for systems and companies are integrating Machine Learning methods as well as Generative AI into modern products. For Systems Engineering this leads to new challenges, for example due to the increasing importance of data quality, data privacy or new legislation. This article highlights key challenges arising from the integration of AI components into technical systems and discusses the impact on classical role models for Systems Engineering. The paper presents results from a literature review as well as a view on how the development of AI-based systems is transforming traditional Systems Engineering from perspective of design teams. New demands on data quality assurance and legal risk management as well as establishing new roles in Systems Engineering are discussed. In addition, theses for shaping the future of Systems Engineering are presented.

Teamwork relies on collaboration to achieve goals that exceed individual capabilities, with team cognition playing a key role by integrating individual expertise and shared understanding. Identifying the causes of inefficiencies or poor team performance is critical for implementing targeted interventions and fostering the development of team cognition. This study proposes a teamwork cognitive diagnostic modeling framework comprising 12 specific models—collectively referred to as Team-CDMs—which are designed to capture the interdependence among team members through emergent team cognitions by jointly modeling individual cognitive attributes and a team-level construct, termed teamwork quality, which reflects the social dimension of collaboration. The models can be used to identify strengths and weaknesses in team cognition and determine whether poor performance arises from cognitive deficiencies or social issues. Two simulation studies were conducted to assess the psychometric properties of the models under diverse conditions, followed by a teamwork reasoning task to demonstrate their application. The results showed that Team-CDMs achieve robust parameter estimation, effectively diagnose individual attributes, and assess teamwork quality while pinpointing the causes of poor performance. These findings underscore the utility of Team-CDMs in understanding, diagnosing, and improving team cognition, offering a foundation for future research and practical applications in teamwork-based assessments.

This paper reports the results of an experiment on how team heterogeneity in terms of productivity influences both the revenue sharing proposed by the principal to the team and the employees’ performance. Experimental evidence shows that when the team is heterogeneous, the principal does not try to motivate the agents through her sharing offer. Regardless of the level of team-based compensation, a large amount of free riding occurs since each agent is mainly influenced by his teammate's behavior. In contrast, when the team is homogeneous, agents are better able to cooperate, reciprocating the principal's offer.

Teamwork has become increasingly important in modern organizations and the labor market. Yet, little is known about the role of self-confidence in teamwork. In this paper, we present evidence from a laboratory experiment using a team effort task. Effort and ability are complements and there are synergies between teammates’ efforts. We exogenously manipulate subjects’ self-confidence about their ability using easy and hard general knowledge quizzes. We find that overconfidence leads to more effort, less free riding, and higher team revenue. This finding is primarily due to a direct effect of overconfidence on own effort provision, while there is no evidence that subjects strategically respond to the teammate’s overconfidence.