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.

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.

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.

Convergent Cross-Mapping (CCM) has shown high potential to perform causal inference in the absence of detailed models. This has implications for the understanding of complex information systems, as well as complex systems more generally. This article assesses the strengths and weaknesses of the CCM algorithm by varying coupling strength and noise levels in a model system consisting of two coupled logistic maps. As expected, it is found that CCM fails to accurately infer coupling strength and even causality direction in strongly coupled synchronized time-series, but surprisingly also in the presence of intermediate coupling. It is further found that the presence of noise reduces the level of cross-mapping fidelity, where the converged value of the CCM correlation decreases roughly linearly as a function of the noise, while the convergence rate of the CCM correlation shows little sensitivity to noise. The article proposes controlled noise injections in intermediate-to-strongly coupled systems could enable more accurate causal inferences. Initial investigation of an external driving signal indicates robustness of CCM toward this potentially confounding influence. Given the inherent noisy nature of real-world systems, the findings enable a more accurate evaluation of CCM applicability and the article advances suggestions on how to overcome the method’s weaknesses.

Convergent Cross-Mapping (CCM) has shown high potential to perform causal inference in the absence of models. We assess the strengths and weaknesses of the method by varying coupling strength and noise levels in coupled logistic maps. We find that CCM fails to infer accurate coupling strength and even causality direction in synchronized time-series and in the presence of intermediate coupling. We find that the presence of noise deterministically reduces the level of cross-mapping fidelity, while the convergence rate exhibits higher levels of robustness. Finally, we propose that controlled noise injections in intermediate-to-strongly coupled systems could enable more accurate causal inferences. Given the inherent noisy nature of real-world systems, our findings enable a more accurate evaluation of CCM applicability and advance suggestions on how to overcome its weaknesses.