Nicholas Davis
Co-Creative Artificial Intelligence & HCI

Research Portfolio

Ph.D. in Human-Centered Computing from Georgia Tech

B.A. in Cognitive Science from Case Western Reserve University

I received my PhD in Human-Centered Computing from Georgia Tech in 2017, with a specialization in Cognitive Science and Computational Creativity. I was a Human-Computer Interaction (HCI) Assistant Professor in the Department of Software and Information Systems at the University of North Carolina at Charlotte for 4 years, where I taught human-centered design, co-creative AI, and collaborated on the design and evaluation of co-creative systems

I spent 5 years as a Graduate Research Assistant in the Expressive Machinery Lab at Georgia Tech developing and evaluating co-creative AI prototypes and empirically investigating creative cognition. I spent a year in the Entertainment Intelligence Lab at Georgia Tech studying how AI can be used for education. I also spent a year in the ACME lab at Georgia Tech studying how sketch-based tools can be used to screen for dimentia. I have experience working as a User Experience Research Intern at Google, YouTube (Visioning Team), and Adobe's Creative Technology Lab. I am currently working as an independent research consultant studying and designing co-creative AI systems. 

Co-creative artificial intelligence is a field of study that investigates AI systems that collaborate with users on a shared creative product. With the proliferation of generative AI, co-creative experiences are becoming more prevalent. It is critical to study and understand these co-creative experiences. I design co-creative systems and empirically investigate their impact on the user's creative experience. In particular, my work develops cognitive models of the sense-making processes users engage in as they interact with co-creative agents, and the interaction dynamics between the user and agent. I specialize in modeling the interaction dynamics of co-creation in co-creative AI systems using the cognitive framework I developed called creative sense-making. These interaction models can be used to identify trends and patterns in interaction to help understand co-creativity and design more intelligent co-creative systems.