Conference Agenda

Product engineers need creativity to come up with solutions that have high innovative potential. Today product development often takes place in these distributed settings, making tasks that involve creativity more critical. With the distributed settings come oftentimes also intercultural teams, since talent is recruited from all over the world. To support such intercultural distributed product development teams with their creative tasks, the Cultural Synergy Spectrum (CSS) method has been designed. This contribution aims at validation the method’s support performance and applicability through application in an intercultural team in a live-lab environment. Three teams composed of engineers from different fields in an international university program worked on solving the practice problem.

The traditional ideation techniques used in complex engineering design challenges have been practiced for a long time. However, with the advent of generative AI, numerous supporting tools have emerged, some of which attempt to mimic cognitive processes. To explore how AI tools can be integrated into the early design phase, an ideation bootcamp was set up. This bootcamp examined the input values from a human collaborative technique known as “Brainwriting” and two forms of AI tools: one focused on text and the other on visuals. Designed to equip students with a better understanding of how to utilise and benefit from AI-powered design tools, the activity showcased the significant potential of merging human creativity with machine intelligence. Participants, 22 fifth year engineering design students, engaged in a multifaceted ideation process, leveraging insights from the Brainwriting activity using paper and pen before moving on to test the AI-powered tools, Copilot (text) and Vizcom (visuals). While the Brainwriting method encouraged collective brainstorming and idea generation, Copilot provided detailed feedback and suggestions for refinement, enhancing the quality and direction of the ideas. Meanwhile, Vizcom offered visual representations of these early phase ideas, promoting rapid prototyping and iterative exploration, some visualised concepts being quite out-of-the-box.

Digital Fabrication environments, such as FabLabs or Makerspaces, are dynamic environments that provide hands-on, collaborative work among individuals with diverse roles. Quality teamwork is essential for successful prototype development. Teamwork quality refers to how effectively a team collaborates, including aspects such as Communication, Coordination, Mutual Support, Effort, Cohesion, and Balance of Member Contribution. Teamwork with high-quality can foster shared understanding, effective problem-solving, and synergy among team members. This is essential for achieving the innovation and productivity goals of makerspaces.

While teamwork quality is recognised as critical for effective collaboration, only limited research investigated how different roles affect perceptions of teamwork quality in FabLabs, where cross-functional collaboration takes place. This study aims to fill this gap by investigating how specific team roles—namely Manager, Designer, Programmer, and Prototyper, influence perceptions of teamwork quality, team member's success, and team performance in designing and building prototypes in FabLabs.

To achieve this, the study explores teamwork quality from a role-specific perspective. It provides a deeper understanding of how distinct roles within FabLabs contribute to collaborative efforts and influence team outcomes.

This study analyses data collected from 76 students enrolled in a Digital Fabrication course. The students were divided into 19 groups, each student selecting one of four designated roles based on their preferences. In the mid-term of the course, the students were asked to complete an online questionnaire about their selected roles and teamwork quality. The research investigates three main questions: (1) How do perceptions of teamwork quality dimensions differ by role? (2) What is the relationship between teamwork quality and team outcomes, such as success and performance? (3) Which dimensions of teamwork quality are the most effective predictors of positive team outcomes in FabLabs?

To address these questions, the study performed a series of statistical tests. One-way ANOVA was used to determine role-based differences in mean scores for teamwork quality, which indicated that Designers, due to the nature of their role, reported slightly higher levels of Communication and Coordination than other roles. However, there were no significant differences between individual roles across teamwork dimensions, indicating equal contribution to the overall team’s success. Pearson correlation analysis revealed a strong correlation between teamwork quality, team member success, and team performance, which were statistically significant. Further, multiple regression analyses found that Mutual Support was the significant predictor for both Team Member’s Success and Team Performance across all roles. Teamwork dimensions, such as Cohesion and Effort, also contributed meaningfully to team members’ success and performance, reflecting the distinct teamwork needs associated with various functional roles.

These findings are valuable for makerspace leaders, educators, and facilitators, as they emphasise the importance of role-specific interventions to promote a cohesive and productive team atmosphere. The results also have practical implications for design education, suggesting that integrating role-aware teamwork training into design education could better prepare students for collaborative work in makerspaces. By addressing role-based teamwork needs, this research offers practical insights into optimising collaboration, innovation, and satisfaction, making makerspaces more effective for individual and collective success.

This paper investigates and reviews innovative techniques and tools to teach different design approaches to engineering students, allowing them to compare and critique methodology, while enhancing knowledge and skills. The paper reviews several years of feedback and development on authentic project-led approaches to teaching user-centred design, systematic design and social innovation to second year engineering students; as a means to provide core knowledge and skills required for different industries. The paper concludes with suggestions as to how enhance the relevance and authenticity of the learning within this approach-and to develop and master such design skills and knowledge into more advanced modules as part of a programme narrative.

STEM-based learning is widely recognized globally as an effective approach for fostering interdisciplinary skills and preparing students for complex, real-world challenges. Taiwan’s technology education system has increasingly incorporated STEM principles, especially within secondary technology courses, where it is viewed as a strategy that fosters meaningful learning outcomes. This approach actively engages students in engineering and design projects, encouraging the integration and application of interdisciplinary knowledge in practical contexts. STEM instruction not only strengthens students' theoretical foundations but also enhances their hands-on skills and creative thinking. By applying learned concepts to solve practical problems, students validate their knowledge, building skills and resilience that better prepare them for careers in science, technology, and engineering fields.

However, STEM teaching in Taiwan relies heavily on technology teachers, who are responsible for preparing content that spans multiple disciplines, such as science, engineering, and mathematics. This cross-disciplinary workload places a significant burden on lesson planning and preparation, making it challenging for teachers to maintain high instructional quality and effectiveness over time. Such pressures may lead some educators to discontinue STEM teaching altogether or struggle to sustain its benefits. In recent years, advancements in generative artificial intelligence (GenAI) have encouraged teachers to explore this technology’s potential in educational settings. Many teachers have begun incorporating GenAI tools, especially for lesson planning and curriculum design, aiming to ease preparation demands and reduce the time required. Despite these advancements, most educators currently limit their use of GenAI to basic tasks such as course planning and ideation, rather than fully utilizing its capabilities for more advanced instructional needs, including assessing student performance.

This study employs a quantitative research approach, using a survey to investigate how technology teachers across Taiwan utilize GenAI in STEM teaching and to identify the primary sources from which they learn about this technology. Survey responses were gathered from 67 active technology teachers from various regions across Taiwan, all of whom teach students aged 13 to 15. This study addresses two main questions: How do technology teachers integrate GenAI within STEM education, and what are the primary sources through which they gain knowledge of GenAI?

Key findings reveal that (1) 44% of Taiwan’s technology teachers use GenAI primarily for lesson planning and creative content generation, while only 8% apply it to assess student performance; (2) Teachers primarily acquire knowledge of GenAI through Professional Development courses and workshops organized by universities, Educational Technology Conferences and Exhibitions, and Online Learning Platforms; and (3) Online Learning Platforms play an especially significant role in supporting teachers’ use of GenAI for evaluating student performance. Based on these findings, this study suggests that strengthening teachers' engagement with GenAI through online platforms could improve their ability to apply it effectively in student performance evaluation. These insights underscore the importance of providing targeted AI-driven tools to support a broad range of instructional tasks in STEM teaching, offering valuable contributions to advancing design education and engineering pedagogy.