Understanding User Perceptions, Collaborative Experience and User
Engagement in Different Human-AI Interaction Designs for Co-Creative Systems
- URL: http://arxiv.org/abs/2204.13217v1
- Date: Wed, 27 Apr 2022 22:37:44 GMT
- Title: Understanding User Perceptions, Collaborative Experience and User
Engagement in Different Human-AI Interaction Designs for Co-Creative Systems
- Authors: Jeba Rezwana and Mary Lou Maher
- Abstract summary: Human-AI co-creativity involves humans and AI collaborating on a shared creative product as partners.
In many existing co-creative systems users can communicate with the AI, usually using buttons or sliders.
This paper presents a study with 38 participants to explore the impact of two interaction designs on user engagement, collaborative experience and user perception of a co-creative AI.
- Score: 0.7614628596146599
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Human-AI co-creativity involves humans and AI collaborating on a shared
creative product as partners. In a creative collaboration, communication is an
essential component among collaborators. In many existing co-creative systems
users can communicate with the AI, usually using buttons or sliders. Typically,
the AI in co-creative systems cannot communicate back to humans, limiting their
potential to be perceived as partners rather than just a tool. This paper
presents a study with 38 participants to explore the impact of two interaction
designs, with and without AI-to-human communication, on user engagement,
collaborative experience and user perception of a co-creative AI. The study
involves user interaction with two prototypes of a co-creative system that
contributes sketches as design inspirations during a design task. The results
show improved collaborative experience and user engagement with the system
incorporating AI-to-human communication. Users perceive co-creative AI as more
reliable, personal, and intelligent when the AI communicates to users. The
findings can be used to design effective co-creative systems, and the insights
can be transferred to other fields involving human-AI interaction and
collaboration.
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