Question asking is a crucial human skill, influencing social cognition, creative problem solving, and information seeking. Yet, its cognitive mechanisms remain poorly understood due to challenges in studying it naturally. We developed The Martian Game, an open-ended online question-asking game that simulates creative problem solving in realistic contexts. Players design a solar energy system for a Martian city through two stages: (1) a problem finding phase where they ask an AI chatbot (“Mark”) questions to gather information, and (2) a solution-planning phase producing written and visual designs. Questions are coded for complexity, originality, and relevance; solutions are rated for originality and appropriateness. This game offers an ecologically valid, interdisciplinary tool to study question asking and supports the hypothesis that complex questions promote effective problem solving. A pilot study validates its potential for examining open-ended cognition beyond the lab.

With the rapid advancement of generative AI, co-creation has emerged as a key interaction paradigm, enabling humans and AI to collaborate in creative processes. However, despite decades of research on co-creativity, recent AI developments often lack a structured framework to integrate these insights effectively. To address this gap, we propose the Co-Creative Design Framework (CCDF), which formalizes human-AI co-creation through cognitive and interaction principles. The framework is structured around three core dimensions: agency, which defines the balance of autonomy and control between user and AI; interaction dynamics, which describe the evolving relationship between collaborators and their shared creative product; and communication, which governs information exchange between human and AI. The CCDF provides a systematic approach to modeling co-creative AI and hybrid intelligence systems, defining key dimensions of variance that shape the interaction space of co-creation. In particular, it highlights agency and interaction dynamics, which have been underexplored in recent co-creative AI frameworks. This paper details the iterative development of CCDF, synthesizing insights from co-creativity literature and AI research. We apply the framework in a comparative analysis of Traditional ChatGPT, ChatGPT Canvas Mode, and DALL-E, demonstrating its ability to capture fine-grained differences in system design and user experience.

Hackathons have become popular collaborative events for accelerating the development of creative ideas and prototypes. There are several case studies showcasing creative outcomes across domains such as industry, education, and research. However, there are no large-scale studies on creativity in hackathons which can advance theory on how hackathon formats lead to creative outcomes. We conducted a computational analysis of 193,353 hackathon projects. By operationalizing creativity through usefulness and novelty, we refined our dataset to 10,363 projects, allowing us to analyze how participant characteristics, collaboration patterns, and hackathon setups influence the development of creative projects. The contribution of our paper is twofold: We identified means for organizers to foster creativity in hackathons. We also explore the use of large language models (LLMs) to augment the evaluation of creative outcomes and discuss challenges and opportunities of doing this, which has implications for creativity research at large.

In this paper, we explore the potential of AI-assisted visualization during the problem identification and construction phase of the creative problem-solving process. We examine this within the context of the ongoing crea.visions research project, which employs AI technologies to visualize citizens' visions of the future. Our findings underscore various factors contributing to the effectiveness of assisted visualization in this setting, such as: 1) the tool's dual role as both a visual and ideational aid, 2) the introduction of innovative collaborative elements like prompt engineering, 3) the enhancement of visual expression without requiring artistic skills, and 4) the facilitation of idea communication. We also recognize limitations related to the tool and the problem context such as abstract concepts. This study serves as a foundation for future research on AI-assisted image generation as a resource in creative problem-solving, laying the groundwork for the creation of increasingly effective and user-friendly tools.

Digital games used to assess creativity represents an emerging but underexplored topic. These games could allow for the combination of scalability through crowdsourcing and potentially higher ecological testing using fine-grained data acquisition from more natural settings. This development could also provide a promising a testbed for exploring human-AI creativity. However, success both in the design and implementation phases hinges on the games being both valid instruments for measuring creativity as well as engaging enough for the general public to want to play. This paper presents my initial PhD work focused on designing CREA: a suite of freely accessible digital games and tasks to understand and assess creativity. First I describe the CREA games and tasks, then discuss my considerations and challenges for operationalizing creativity within the games, present initial participant feedback, and discuss future work.

The rise of digital technologies and Machine Learning (ML)-tools for creative expression brings about novel opportunities for studying creativity and cognition at scale. In this paper, we present a pilot study of crea.blender SDG - an online GAN based image generation game. We designed crea.blender SDG with two goals in mind: The first, to let people create images relating to the United Nations Sustainable Development Goals (SDGs) and through them, engage in large-scale conversations on complex socioscientific problems. The second, as a fun and inspiring gateway for public participation in research, generating data for the creativity and cognition research and design community. Specifically in this pilot, we study and affirm that the design of crea.blender SDG is flexible enough to allow users to create images that express both anxiety and hope for the future; affirm that user generated images express these ideas in ways that are meaningful to people other than the original creator; and begin to investigate which specific features of images are more closely related to dystopian or utopian ideas of the future. Finally, we discuss implications for future design and research with ML-based creativity tools.