Museums play a critical role in promoting public understanding of emerging technologies like artificial intelligence (AI), but it is unclear what design features lead to learning about AI in museums. We contribute a design research exploration of how embodiment and creativity foster AI literacy in museum exhibits. We present design prototypes of three museum exhibits—DataBites, Knowledge Net, and LuminAIx—that aim to teach middle schoolers about AI. We present results from a qualitative analysis of an in-museum study in which we examined participants’ understanding of and interest in AI through interviews and video recordings. Our findings illuminate how creativity fosters interest in AI and how different forms of embodiment contribute to learning about AI. We recommend that AI museum exhibits utilize creative and personally relevant activities to engage middle schoolers, support hybrid conceptualizations of AI, and leverage tangible interaction to make AI concepts approachable.

LuminAI is an interactive art installation that allows participants to collaborate with an AI dance partner by improvising movements. During the interaction, the participant dances with an AI dance partner who learns from the participant’s movements in real-time and remixes them into new, unexpected forms of motion. LuminAI blurs the lines between the participant and AI agent, inviting the participant to improvise and engage with AI in a dance of creativity. Recently, we’ve redesigned LuminAI with the educational purpose of enhancing the public’s AI literacy. We separated LuminAI into three panels with AI-related educational goals for each panel and redesigned the user interaction. In this paper, we present the redesign of LuminAI and educational goals centering on enhancing the public’s AI literacy.

Co-creation in embodied contexts is central to the human experience but is often lacking in our interactions with computers. We seek to develop a better understanding of embodied human co-creativity to inform the human-centered design of machines that can co-create with us. In this paper, we ask: What characterizes dancers’ experiences of embodied dyadic interaction in movement improvisation? To answer this, we ran focus groups with 24 university dance students and conducted a thematic analysis of their responses. We synthesize our findings in an Interconnected Model of Improvisational Dance Inputs, where movement choices are shaped by the interplay between in-the-moment influences between the self, partner, and the environment, a set of generative strategies, and heuristics for a successful collaboration. We present a set of design recommendations for LuminAI, a co-creative AI dance partner. Our contributions can inform the design of AI in embodied co-creative domains.

This paper introduces a new method for quantifying open-ended collaborative embodied improvisation: Observable Creative Sense-Making (OCSM). This technique builds on previous work on Creative Sense-Making (CSM), examines its shortcomings, and addresses it by reformalizing and grounding CSM in current literature from embodied social cognition and an intersubjective perspective of creativity. We apply this method to empirical studies of human collaboration in dance improvisation with 16 advanced college dancers and establish the method’s validity. The OCSM method described in this paper includes a qualitative coding technique, a web-based tool for coding the interaction, and the cognitive theory behind its application.

Findings from embodied cognition suggest that our whole body (not just our eyes) plays an important role in how we make sense of data when we interact with data visualizations. In this paper, we present the results of a study that explores how different designs of the ”interaction” (with a data visualization) alter the way in which people report and discuss correlation and causation in data. We conducted a lab study with two experimental conditions: Full body (participants interacted with a 65” display showing geo-referenced data using gestures and body movements); and, Gamepad (people used a joypad to control the system). Participants tended to agree less with statements that portray correlation and causation in data after using the Gamepad system. Additionally, discourse analysis based on Conceptual Metaphor Theory revealed that users made fewer remarks based on FORCE schemata in Gamepad than in Full-Body.

Learning ballet is a complex motor task that can be effectively enhanced by technology. Learning technologies, however, are not typically used for the assessment of ballet technique due to a lack of adequate and non-invasive tools that can be pragmatically adopted. We conducted an interview-based qualitative study with seven expert ballet teachers and six pre-professional dancers to examine their current and future technology use in a ballet technique class. Through inductive and deductive analysis, we identified reasons for technology non-use and derived seven requirements that can inform the design and implementation of ballet assessment technologies including designing for: adaptation to multi-skill/multi-method environments, teacher/dancer skill augmentation, agency, non-invasive design, feedback for gross/fine movements, trust, and proprioception by supporting transformative assessment. We discuss barriers for technology acceptance and unintended consequences that should be considered when designing future technologies for ballet.

Museums have embraced embodied interaction: its novelty generates buzz and excitement among their patrons, and it has enormous educational potential. Human-Data Interaction (HDI) is a class of embodied interactions that enables people to explore large sets of data using interactive visualizations that users control with gestures and body movements. In museums, however, HDI installations have no utility if visitors do not engage with them. In this paper, we present a quasi-experimental study that investigates how different ways of representing the user ("mode type") next-to a data visualization alters the way in which people engage with a HDI system. We consider four mode types: avatar, skeleton, camera overlay, and control. Our findings indicate that the mode type impacts the number of visitors that interact with the installation, the gestures that people do, and the amount of time that visitors spend observing the data on display and interacting with the system.

Intelligent voice assistants (IVAs) have the potential to support older adults' independent living. However, despite a growing body of research focusing on IVA use, we know little about why older adults become IVA non-users. This paper examines the reasons older adults use, limit, and abandon IVAs (i.e., Amazon Echo) in their homes. We conducted eight focus groups, with 38 older adults residing in a Life Plan Community. Thirty-six participants owned an Echo for at least a year, and two were considering adoption. Over time, most participants became non-users due to their difficulty finding valuable uses, beliefs associated with ability and IVA use, or challenges with use in shared spaces. However, we also found that participants saw the potential for future IVA support. We contribute a better understanding of the reasons older adults do not engage with IVAs and how IVAs might better support aging and independent living in the future.

Embodied interaction is particularly useful in museums because it allows to leverage findings from embodied cognition to support the learning of STEM concepts and thinking skills. In this paper, we focus on Human-Data Interaction (HDI), a class of embodied interactions that investigates the design of interactive data visualizations that users control with gestures and body movements. We describe an HDI system that we iteratively designed, implemented, and observed at a science museum, and that allows visitors to explore large sets of data on two 3D globe maps. We present and discuss design strategies and optimization that we implemented to mitigate two sets of design challenges: (1) Dealing with display, interaction, and affordance blindness; and, (2) Supporting multiple functionalities and collaboration.

Dancer-teacher communication in a ballet class can be challenging: ballet is one of the most complex forms of movements, and learning happens through multi-faceted interactions with studio tools (mirror, barre, and floor) and the teacher. We conducted an interview-based qualitative study with seven ballet teachers and six dancers followed by an open-coded analysis to explore the communication challenges that arise while teaching and learning in the ballet studio. We identified key communication issues, including adapting to multi-level dancer expertise, transmitting and realigning development goals, providing personalized corrections and feedback, maintaining the state of flow, and communicating how to properly use tools in the environment. We discuss design implications for crafting technological interventions aimed at mitigating these communication challenges.