Conference Agenda

In the dynamic landscape of artificial intelligence (AI) development, the intersection of technology, design, and management emerges as a critical frontier for fostering innovation that is not only technologically advanced but also ethically aligned and strategically integrated. In this research, we take a service design approach to explore this intersection of technology, design, and management for a responsible AI implementation from a service ecosystem perspective and introduce TDM principles. By introducing this integrated framework, this study makes contributions in the following ways: first, it offers an integrated perspective derived from technology, design, and management, providing a holistic approach that considers the perspectives of multiple stakeholders involved in democratising AI-driven products and services. Secondly, its service design-led service ecosystem perspective allows it to explore designing with AI to

design for AI through an interdisciplinary lens of TDM principles. Doing so extends the existing human-centred AI frameworks that are focused on a dyadic relationship between humans and AI. Lastly, its integrated perspective grounded in technology, design, and management offers a sector-agnostic approach that can be easily applicable across different sectors for responsible implementation of AI.

With various types of mobile service robots gradually taking their place in our homes and public spaces, robot designs are diversifying to address a wide range of tasks and usage contexts. While research on robot morphology within human-robot interaction (HRI) has primarily focused on anthropomorphic design, studies on robot appearance and human perception yield conflicting findings regarding the effect of anthropomorphism and the desired level of human-likeness across contexts. This study hypothesizes that the optimal level of human-likeness varies depending on the nature of the context. By exploring the design of mobile service robots across three different service contexts - restaurants, supermarkets, and delivery services - this study examines the relationship between usage context, perceived capabilities, and the desired level of human-likeness. Generative image artificial intelligence (AI) tools were employed to facilitate the development of design variations and their visualization in context as photorealistic renderings. A total of nine renderings were created and presented in an online survey, from which 36 responses were collected and analyzed. The survey results indicate a preference for low-level human-likeness for robots in supermarket and delivery contexts. However, the restaurant context had mixed results, exhibiting no clear preference for a certain human-likeness level.

The research explores how human intelligence can effectively mentor Artificial Intelligence(AI) software, proposing strategies to elevate design education and the design workflow. Based on the detailed exploration of human-AI collaboration in AI-assisted rendering, this study presents a nuanced investigation into the integration of artificial intelligence in a design process, focusing on the responsible use of AI, and augmenting human creativity with AI capabilities.

Through a two-step experimental study, AI's rendering capabilities in response to ideation sketches were explored. By utilizing the AI software Vizcom, a series of experiments and comparison studies were designed to examine AI's response to human-generated sketches and the impact of computational thinking on guiding AI to enhance design ideation workflows. By selecting an unconventional project - a micro-electric vehicle with retractable wheel suspension - the research highlights the essential role of human input in navigating AI's limitations and steering it toward innovative outcomes. This study underscores the crucial role of sketching and computational thinking in driving innovative design outcomes. This study contributes to understanding how human designers can mentor AI to achieve better workflow efficiency and creativity, offering insights into the dynamics of human-AI collaboration in the design field.