In this demonstration, we present a holographic projected version of LuminAI, which is an interactive art installation that allows participants to collaborate with an AI dance partner by improvising movements together. By utilizing a mix of a top-down and bottom-up approach, we seek to understand embodied co-creativity in an improvisational dance setting to better develop the design of the modular AI agent to creatively collaborate with a dancer. The purpose of this demonstration is to describe the five-module agent design and investigate how we can design an immersive experience that is design-efficient, portable, light, and duo-user participation. Through this installation in an imitated black box space, audience members and dancers engage in an immersive co-creative dance experience, inspiring discussion on the limitless applications of dance and technology in the realms of learning, training, and creativity.
more »
« less
This content will become publicly available on June 22, 2026
Bringing LuminAI to Life: Studying Dancers’ Perceptions of a Co-Creative AI in Dance Improvisation Class
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
- Award ID(s):
- 2123563
- PAR ID:
- 10644768
- Publisher / Repository:
- ACM
- Date Published:
- Page Range / eLocation ID:
- 859 to 871
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Wearable electronics expand the ways learners can create with computing as they gain proficiency with programming and electronics. Dance is one domain where wearables can support creative, embodied practices in computing education. However, wearable electronics need to be small, durable, and easily integrated into clothing to meet the constraints of dance contexts. These features are challenging to achieve, especially when working with novices. We present DanceBits, a wearable prototyping kit for dance that was co-developed with a justice-oriented, computing and dance education organization. DanceBits’ plug-and-play system uses small PCBs with solderless connectors to support dancers in rapidly designing, building, and performing with electronic costumes. Our user studies exploring the system with dance instructors and youth participants show that DanceBits enabled fast development of wearables, offered users a breadth of expressivity through computational and choreographic choices, and empowered dancers to see wearables as a tool for developing their movement practices.more » « less
-
Analyzing dance moves and routines is a foundational step in learning dance. Videos are often utilized at this step, and advancements in machine learning, particularly in human-movement recognition, could further assist dance learners. We developed and evaluated a Wizard-of-Oz prototype of a video comprehension tool that offers automatic in-situ dance move identification functionality. Our system design was informed by an interview study involving 12 dancers to understand the challenges they face when trying to comprehend complex dance videos and taking notes. Subsequently, we conducted a within-subject study with 8 Cuban salsa dancers to identify the benefits of our system compared to an existing traditional feature-based search system. We found that the quality of notes taken by participants improved when using our tool, and they reported a lower workload. Based on participants’ interactions with our system, we offer recommendations on how an AI-powered span-search feature can enhance dance video comprehension tools.more » « less
-
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
-
Lissajous figures are parametric equations that deconstruct into equations of simple harmonic motion. They were a source of inspiration by artists and mathematicians alike, well before the digital age, due to their esthetic forms and simple equations that could be easily deconstructed. Here for the first time in literature, we present Lissajous pattern analysis in the context of modern dance movement, thereby expanding the physical understanding of dance and redefining the creative choreographic process. Through the implementation of wearable sensors, specifically wireless accelerometers, we have collected movement data from professional dancers to serve as an additional lens to visualize dance in a novel way and to analyze dance mechanics. The resulting Lissajous figures from the movement phrases were used to both inform and inspire creativity in the choreographic process of the Artistic Director of the Bowen McCauley Dance Company to create a new piece of work entitled Lissajous.more » « less
An official website of the United States government
