Dr Yuan Yin is the Lecturer in Goldsmiths, University of London and a visiting researcher in Imperial College London. She was the research fellow at University of Exeter, research assistant at Imperial College London, research assistant at Newcastle University, and Phd at Imperial College London.

Her general interests include design cognition and neuroscience, creativity tools and innovation, design engineering, human-AI collaboration, human-computer interaction (HCI), Brain-computer interaction (BCI), and digital health intervention.

She is the director of IICL-Lab (Intelligent innovation and cognition lab). She was nominated and awarded “Early Career Research Achievements in Design Research Award” by Design society in 2025. She was the co-founder of AI & Design Special interest group. 

She was the global talent recognised by UK Engineering & Physical Sciences Research Council, and owned Chinese government award for outstanding self-financed students abroad set by China Scholarship Council of Ministry of Education of the People's Republic of China.
 
She has contributed to over 30 journal and conference papers, and several books and book chapters. 

Generative Artificial Intelligence (Generative AI) is a collection of AI technologies that can generate new information such as texts and images. With its strong capabilities, Generative AI has been actively studied in creative design processes. However, limited studies have explored the roles of humans and Generative AI in conceptual design processes, which leaves a gap for human–AI collaboration investigation. To address this gap, this study attempts to uncover the contributions of different Generative AI technologies in assisting humans in the conceptual design process. Novice designers were recruited to complete two design tasks in the condition of with or without the assistance of Generative AI. The results revealed that Generative AI primarily assists humans in the problem definition and idea generation stages, while the idea selection and evaluation stage remains predominantly human-led. Additionally, with the assistance of Generative AI, the idea selection and evaluation stages were further enhanced. Based on the findings, we discussed the role of Generative AI in human–AI collaboration and the implications for enhancing future conceptual design support with Generative AI’s assistance.

This study aims to take higher-education students as examples to understand and compare artistic and engineering mindsets in creative processes using EEG. Fifteen Master of Fine Arts (MFA) visual arts and fifteen Master of Engineering (MEng) design engineering students were recruited and asked to complete alternative uses tasks wearing an EEG headset. The results revealed that (1) the engineering-mindset students responded to creative ideas faster than artistic-mindset students. (2) Although in creative processes both artistic- and engineering-mindset students showed Theta, Alpha, and Beta wave activity, the active brain areas are slightly different. The active brain areas of artistic-mindset students in creative processes are mainly in the frontal and occipital lobes; while the whole brain (frontal, oriental, temporal, and occipital lobes) was active in creative processes of engineering-mindset students. (3) During the whole creative process, the brain active level of artistic-mindset students was higher than that of engineering-mindset students. The results of this study fills gaps in existing research where only active brain areas and band waves were compared between artistic- and engineering-mindset students in creative processes. For quick thinking in terms of fluency of generating creative ideas, engineering students have an advantage in comparison to those from the visual arts. Also, the study provided more evidence that mindset can affect the active levels of the brain areas. Finally, this study provides educators with more insights on how to stimulate students’ creative ability.

Cognitive factors such as association, memory, and combination have been verified to be related to the creative design process. However, limited research has considered the effects of cognitive factors and their interaction on creative processes in practical creative design processes. This study aimed to detect the interactive effects of cognitive factors on creative processes in a practical creative design process. In particular, how the sequence of cognitive factors affects creativity quality levels of the creative solutions was investigated. Seventy-one participants were recruited to undertake a design task using the think-aloud method. The results of this study are as follows. (i) The sequences of cognitive factors can contribute to different creativity quality levels of solutions. The sequence of semantic memory, common association, remote association, episodic memory, remote combination, idea expression, and idea evaluation is more likely to lead to a higher creativity quality level of solutions. (ii) The repetition of the same cognitive factor in a creative design process, especially semantic memory, does not necessarily contribute to a high-creativity-quality-level solution. (iii) Creativity quality levels of solutions are related to how many cognitive factors categories are involved in the creative design process. The more cognitive factors included, the higher the creativity quality of the solutions will be.