Generative AI (GenAI) has shown remarkable capabilities in generating diverse and realistic content across different formats like images, videos, and text. In Generative AI, human involvement is essential, thus HCI literature has investigated how to effectively create collaborations between humans and GenAI systems. However, the current literature lacks a comprehensive framework to better understand Human-GenAI Interactions, as the holistic aspects of human-centered GenAI systems are rarely analyzed systematically. In this paper, we present a survey of 154 papers, providing a novel taxonomy and analysis of Human-GenAI Interactions from both human and Gen-AI perspectives. The dimension of design space includes 1) Purposes of Using Generative AI, 2) Feedback from Models to Users, 3) Control from Users to Models, 4) Levels of Engagement, 5) Application Domains, and 6) Evaluation Strategies. Our work is also timely at the current development stage of GenAI, where the Human-GenAI interaction design is of paramount importance. We also highlight challenges and opportunities to guide the design of Gen-AI systems and interactions towards the future design of human-centered Generative AI applications.
An HCI-Centric Survey and Taxonomy of Human-Generative-AI Interactions
Authors: Jingyu Shi*, Rahul Jain*, Hyungjun Doh, Ryo Suzuki, Karthik Ramani
Submitted to ACM Computing Surveys
https://arxiv.org/pdf/2310.07127.pdf
Jingyu Shi
I am a Ph.D. student in Electrical and Computer Engineering from Purdue University, under the supervision of Prof. Karthik Ramani. I obtained my M.S. degree in ECE in Georgia Institute of Technology, working with Prof. Patricio Vela. Prior to that, I studied Instrument Science and Technology in Beihang University and became a B.Eng.
My research interests lie in human-AI interaction and its applications across various platforms including Augmented Reality, Mixed Reality, Robots, etc. Currently, I am studying the causal effects of exogenous variables in the human-AI decision making process.