Bringing LuminAI to Life: Studying Dancers’ Perceptions of a Co-Creative AI in Dance Improvisation ClassThe 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.2025JKJasmine Kaur et al.Generative AI (Text, Image, Music, Video)Dance & Body Movement ComputingC&C
Perceptions of Interaction Dynamics in Co-Creative AI: A Comparative Study of Interaction Modalities in DrawctoThis paper explores how different interaction modalities with AI agents affect human perception of the co-creative process. We utilize the Wizard of Oz methodology within Drawcto, a co-creative drawing system, to examine co-creativity across three scenarios: human-human, human-robot, and human-software interactions. Using a mixed-methods approach, we combined insights using the Observable Creative Sensemaking (OCSM) method with data from structured interviews. The study involved 20 participants engaging in a collaborative drawing task under each interaction scenario. Key findings reveal an average OCSM curve indicative of typical human-human interactions, varied themes in human-AI collaboration, and a notable influence of AI embodiment on participant perceptions, with the robot interface resembling human-human collaboration more closely than the software interface. Overall, this research offers valuable insights into how different interaction modalities influence the perceived role of AI in the co-creative process and provides design considerations for enhancing human-AI co-creative interactions.2024MDManoj Deshpande et al.Human-LLM CollaborationAI-Assisted Creative WritingC&C
Testing, Socializing, Exploring: Characterizing Middle Schoolers’ Approaches to and Conceptions of ChatGPTAs generative AI rapidly enters everyday life, educational interventions for teaching about AI need to cater to how young people, in particular middle schoolers who are at a critical age for reasoning skills and identity formation, conceptualize and interact with AI. We conducted nine focus groups with 24 middle school students to elicit their interests, conceptions of, and approaches to a popular generative AI tool, ChatGPT. We highlight a) personally and culturally-relevant topics to this population, b) three distinct approaches in students' open-ended interactions with ChatGPT: AI testing-oriented, AI socializing-oriented, and content exploring-oriented, and 3) an improved understanding of youths' conceptions and misconceptions of generative AI. While misconceptions highlight gaps in understanding what generative AI is and how it works, most learners show interest in learning about what AI is and what it can do. We discuss the implications of these conceptions for designing AI literacy interventions in museums.2024YBYasmine Belghith et al.Georgia Institute of TechnologyGenerative AI (Text, Image, Music, Video)Live Streaming & Spectating ExperienceSTEM Education & Science CommunicationCHI
Exploring Collaborative Movement Improvisation Towards the Design of LuminAI—a Co-Creative AI Dance PartnerCo-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 interplays such as in-the-moment influences between the self, partner, and the environment as well as 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.2024MTMilka Trajkova et al.Georgia Institute of TechnologyGenerative AI (Text, Image, Music, Video)Human-LLM CollaborationDance & Body Movement ComputingCHI
Is It AI or Is It Me? Understanding Users’ Prompt Journey with Text-to-Image Generative AI ToolsGenerative Artificial Intelligence (AI) has witnessed unprecedented growth in text-to-image AI tools. Yet, much remains unknown about users' prompt journey with such tools in the wild. In this paper, we posit that designing human-centered text-to-image AI tools requires a clear understanding of how individuals intuitively approach crafting prompts, and what challenges they may encounter. To address this, we conducted semi-structured interviews with 19 existing users of a text-to-image AI tool. Our findings (1) offer insights into users’ prompt journey including structures and processes for writing, evaluating, and refining prompts in text-to-image AI tools and (2) indicate that users must overcome barriers to aligning AI to their intents, and mastering prompt crafting knowledge. From the findings, we discuss the prompt journey as an individual yet a social experience and highlight opportunities for aligning text-to-image AI tools and users’ intents.2024AGAtefeh Mahdavi Goloujeh et al.Georgia Institute of TechnologyGenerative AI (Text, Image, Music, Video)Human-LLM CollaborationCHI
Xylocode: A Novel Approach to Fostering Interest in Computer Science via an Embodied Music SimulationFostering learners’ interest remains an important challenge in computer science (CS) education. In this paper, we explore how creative music-making, tangible interfaces, and embodiment can be used toward this end. The primary contribution of this paper is Xylocode, a novel exhibit that introduces middle school age learners to computing concepts and fosters interest in CS via a tangible playspace for making music using an embodied simulation. We additionally present an in-museum evaluation of Xylocode with 29 middle school age children. Our results indicate that the exhibit fosters situational interest in computer science and leads to recognition of certain computing concepts, including arrays and global variables. Future research is needed to assess whether the exhibit leads to longer-term learning and/or interest gains and to explore why other computing concepts were not recognized by as many learners. We identify several implications and directions for future work based on our findings.2024DLDuri Long et al.Northwestern UniversityProgramming Education & Computational ThinkingDigital Art Installations & Interactive PerformanceCHI
Observable Creative Sense-Making (OCSM): A Method For Quantifying Improvisational Co-Creative InteractionThis 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.2023MDManoj Deshpande et al.Full-Body Interaction & Embodied InputDance & Body Movement ComputingC&C
Active Prolonged Engagement EXpanded (APEX): A Toolkit for Supporting Evidence-Based Iterative Design Decisions for Collaborative, Embodied Museum ExhibitsThis article presents Active Prolonged Engagement eXpanded (APEX), a framework and toolkit for informing evidence-based decisions about the iterative design of embodied, collaborative museum exhibits. We provide an overview of APEX, a framework that builds on both prior work and experimentally derived data to provide an understanding of how visitors’ physical, social, emotional, and intellectual engagement transform during the course of their interaction with an exhibit. We present two case studies demonstrating how to apply APEX in practice, analyzing video recordings of participant interactions with different design iterations of TuneTable—an interactive exhibit for co-creative computational music-making—at both a macro- and micro-level. In the case studies, we explore how APEX reveals important features of participant interaction that suggest implications and directions for design. Finally, we present a toolkit of resources to aid researchers in operationalizing APEX as a framework for video analysis, in-situ observation, and iterative design and evaluation.2022DLDuri Long et al.Feedback-giving & Decision-making; Feedback-giving & Decision-makingCSCW
The role of creativity, collaboration, and embodiment in AI learning experiencesFostering public AI literacy (i.e. a high-level understanding of artificial intelligence (AI) that allows individuals to critically and effectively use AI technologies) is increasingly important as AI is integrated into individuals’ everyday lives and as concerns about AI grow. This paper investigates how collaborative, creative, and embodied interactions can foster AI learning and interest development. We designed three prototypes of collaborative, creative, and/or embodied learning experiences that aim to communicate AI literacy competencies. We present the design of these prototypes as well as the results from a user study that we conducted with 14 family groups (38 participants). Our data analysis explores how collaboration, creativity, and embodiment contributed to AI learning and interest development across the three prototypes. The main contributions of this paper are: 1) three designs of AI literacy learning activities and 2) insights into the role creativity, collaboration, and embodiment play in AI learning experiences.2022DLDuri Long et al.Full-Body Interaction & Embodied InputHuman-LLM CollaborationIntelligent Tutoring Systems & Learning AnalyticsC&C
Family Learning Talk in AI Literacy Learning ActivitiesThe unique role that AI plays in making decisions that affect humans creates a need for public understanding of AI. Informal learning spaces are important contexts for fostering AI literacy, as they can reach a broader audience and provide spaces for children and parents to learn together. This paper explores 1) what types of dialogue familes engage in when learning about AI in an at-home learning environment to inform our understanding of 2) how to design AI literacy activities for informal learning contexts. We present an analysis of family dialogue surrounding three AI education activities and use our findings to update existing principles for designing AI literacy educational interventions. Our findings indicate that embodied interaction, collaboration, and lowering barriers to entry were effective at fostering learning talk. Our results also reveal emergent areas for future research on how to support parents and design visualizations and datasets for AI learning.2022DLDuri Long et al.Georgia Institute of TechnologyHuman-LLM CollaborationProgramming Education & Computational ThinkingK-12 Digital Education ToolsCHI
Co-Designing AI Literacy Exhibits for Informal Learning Spaces AI is becoming increasingly integrated in common technologies, which suggests that learning experiences for audiences seeking a “casual” understanding of AI—i.e. understanding how a search engine works, not necessarily understanding how to program one—is an increasingly important design space. Informal learning spaces like museums are particularly well-suited for such public science communication efforts, but there is little research investigating how to design AI learning experiences for these spaces. This paper explores how to design museum experiences that communicate key concepts about AI, using collaboration, creativity, and embodiment as inspirations for design. We present the design of five low-fidelity AI literacy exhibit prototypes and results from a thematic analysis of participant interactions during a co-design workshop in which family groups interacted with the prototypes and designed exhibits of their own. Our findings suggest new topics and design considerations for AI-related exhibits and directions for future research.2021DLDuri Long et al.Interpreting and Explaining AICSCW
Empirically Evaluating Creative Arc Negotiation for Improvisational Decision-makingAction selection from many options with few constraints is crucial for improvisation and co-creativity. Our previous work proposed creative arc negotiation to solve this problem, i.e., selecting actions to follow an author-defined `creative arc' or trajectory over estimates of novelty, unexpectedness, and quality for potential actions. The CARNIVAL agent architecture demonstrated this approach for playing the Props game from improv theatre in the Robot Improv Circus installation. This article evaluates the creative arc negotiation experience with CARNIVAL through two crowdsourced observer studies and one improviser laboratory study. The studies focus on subjects' ability to identify creative arcs in performance and their preference for creative arc negotiation compared to a random selection baseline. Our results show empirically that observers successfully identified creative arcs in performances. Both groups also preferred creative arc negotiation in agent creativity and logical coherence, while observers enjoyed it more too.2021MJMikhail Jacob et al.Creative Collaboration & Feedback SystemsDigital Art Installations & Interactive PerformanceC&C
The role of creativity, collaboration, and embodiment in AI learning experiencesFostering public AI literacy (i.e. a high-level understanding of artificial intelligence (AI) that allows individuals to critically and effectively use AI technologies) is increasingly important as AI is integrated into individuals’ everyday lives and as concerns about AI grow. This paper investigates how collaborative, creative, and embodied interactions can foster AI learning and interest development. We designed three prototypes of collaborative, creative, and/or embodied learning experiences that aim to communicate AI literacy competencies. We present the design of these prototypes as well as the results from a user study that we conducted with 14 family groups (38 participants). Our data analysis explores how collaboration, creativity, and embodiment contributed to AI learning and interest development across the three prototypes. The main contributions of this paper are: 1) three designs of AI literacy learning activities and 2) insights into the role creativity, collaboration, and embodiment play in AI learning experiences.2021DLDuri Long et al.Full-Body Interaction & Embodied InputHuman-LLM CollaborationC&C
What is AI Literacy? Competencies and Design ConsiderationsArtificial intelligence (AI) is becoming increasingly integrated in user-facing technology, but public understanding of these technologies is often limited. There is a need for additional HCI research investigating a) what competencies users need in order to effectively interact with and critically evaluate AI and b) how to design learner-centered AI technologies that foster increased user understanding of AI. This paper takes a step towards realizing both of these goals by providing a concrete definition of AI literacy based on existing research. We synthesize a variety of interdisciplinary literature into a set of core competencies of AI literacy and suggest several design considerations to support AI developers and educators in creating learner-centered AI. These competencies and design considerations are organized in a conceptual framework thematically derived from the literature. This paper's contributions can be used to start a conversation about and guide future research on AI literacy within the HCI community.2020DLDuri Long et al.Georgia Institute of TechnologyGenerative AI (Text, Image, Music, Video)AI-Assisted Decision-Making & AutomationIntelligent Tutoring Systems & Learning AnalyticsCHI