More Like this

1. Exploring the Role of Social Robot Behaviors in a Creative Activity

   https://doi.org/10.1145/3461778.3462116  

   Hu, Yaxin; Feng, Lingjie; Mutlu, Bilge; Admoni, Henny (June 2021, Designing Interactive Systems Conference (DIS))

   null (Ed.)

   Robots are increasingly being introduced into domains where they assist or collaborate with human counterparts. There is a growing body of literature on how robots might serve as collaborators in creative activities, but little is known about the factors that shape human perceptions of robots as creative collaborators. This paper investigates the effects of a robot’s social behaviors on people’s creative thinking and their perceptions of the robot. We developed an interactive system to facilitate collaboration between a human and a robot in a creative activity. We conducted a user study (n = 12), in which the robot and adult participants took turns to create compositions using tangram pieces projected on a shared workspace. We observed four human behavioral traits related to creativity in the interaction: accepting robot inputs as inspiration, delegating the creative lead to the robot, communicating creative intents, and being playful in the creation. Our findings suggest designs for co-creation in social robots that consider the adversarial effect of giving the robot too much control in creation, as well as the role playfulness plays in the creative process. 

   more » « less
2. Team Learning as a Lens for Designing Human-AI Co-Creative Systems

   https://doi.org/10.48550/arXiv.2207.02996  

   Gmeiner, Frederic; Holstein, Kenneth; Martelaro, Nikolas (July 2022, ACM CHI 2022 Workshop on Generative AI and HCI)

   Generative, ML-driven interactive systems have the potential to change how people interact with computers in creative processes - turning tools into co-creators. However, it is still unclear how we might achieve effective human-AI collaboration in open-ended task domains. There are several known challenges around communication in the interaction with ML-driven systems. An overlooked aspect in the design of co-creative systems is how users can be better supported in learning to collaborate with such systems. Here we reframe human-AI collaboration as a learning problem: Inspired by research on team learning, we hypothesize that similar learning strategies that apply to human-human teams might also increase the collaboration effectiveness and quality of humans working with co-creative generative systems. In this position paper, we aim to promote team learning as a lens for designing more effective co-creative human-AI collaboration and emphasize collaboration process quality as a goal for co-creative systems. Furthermore, we outline a preliminary schematic framework for embedding team learning support in co-creative AI systems. We conclude by proposing a research agenda and posing open questions for further study on supporting people in learning to collaborate with generative AI systems. 

   more » « less
3. Understanding Avatar Identification through Visual Similarity for Richer Story Creation

   Park, Hannah; Brown, Sarah; Chu, Sharon Lynn (January 2019, Communications in computer and information science)

   This paper explores avatar identification in creative story- telling applications where users create their own story and environment. We present a study that investigated the effects of avatar facial similarity to the user on the quality of the story product they create. The children told a story using a digital puppet-based storytelling system by inter- acting with a physical puppet box that was augmented with a real-time video feed of the puppet enactment. We used a facial morphing technique to manipulate avatar facial similarity to the user. The resulting morphed image was applied to each participants puppet character, thus creating a custom avatar for each child to use in story creation. We hypothesized that the more familiar avatars appeared to participants, the stronger the sense of character identification would be, resulting in higher story quality. The proposed rationale is that visual familiarity may lead participants to draw richer story details from their past real-life experiences. Qualitative analysis of the stories supported our hypothesis. Our results contribute to avatar design in children's creative storytelling applications. 

   more » « less
4. Child-Robot Interaction to Integrate Reflective Storytelling Into Creative Play

   https://doi.org/10.1145/3450741.3465254  

   Hubbard, Layne Jackson; Chen, Yifan; Colunga, Eliana; Kim, Pilyoung; Yeh, Tom (June 2021, Proceedings of the 13th Conference on Creativity and Cognition)

   When young children create, they are exploring their emerging skills. And when young children reflect, they are transforming their learning experiences. Yet early childhood play environments often lack toys and tools to scaffold reflection. In this work, we design a stuffed animal robot to converse with young children and prompt creative reflection through open-ended storytelling. We also contribute six design goals for child-robot interaction design. In a hybrid Wizard of Oz study, 33 children ages 4-5 years old across 10 U.S. states engaged in creative play then conversed with a stuffed animal robot to tell a story about their creation. By analyzing children’s story transcripts, we discover four approaches that young children use when responding to the robot’s reflective prompting: Imaginative, Narrative Recall, Process-Oriented, and Descriptive Labeling. Across these approaches, we find that open-ended child-robot interaction can integrate personally meaningful reflective storytelling into diverse creative play practices. 

   more » « less
5. Bringing LuminAI to Life: Studying Dancers’ Perceptions of a Co-Creative AI in Dance Improvisation Class

   https://doi.org/10.1145/3698061.3726925  

   Kaur, Jasmine; Trajkova, Milka; Knowlton, Andrea; Magerko, Brian (June 2025, ACM)

   Not AvailableThe intersection of dance and artificial intelligence presents fertile ground for exploring human-machine interaction, co-creation, and embodied expression. This paper reports on a seven month four-phase collaboration with fifteen dancers from a university dance department, encompassing a preliminary study, redesign of LuminAI-a co-creative AI dance partner-, a contextual diary study, and a culminating public performance. Thematic analysis of responses revealed LuminAI’s impact on dancers’ perceptions, improvisational practices, and creative exploration. By blending human and AI interactions, LuminAI influenced dancers’ practices by pushing them to explore the unexpected, fostering deeper self-awareness, and enabling novel choreographic pathways. The experience reshaped their creative sub processes, enhancing their spatial awareness, movement vocabulary, and openness to experimentation. Our contributions underscore the potential of AI to not only augment dancers’ immediate improvisational capabilities but also to catalyze broader transformations in their creative processes, paving the way for future systems that inspire and amplify human creativity. 

   more » « less