Striking the appropriate balance between humans and co-creative AI is an open research question in computational creativity. Co-creativity, a form of hybrid intelligence where both humans and AI take action proactively, is a process that leads to shared creative artifacts and ideas. Achieving a balanced dynamic in co-creativity requires characterizing control and identifying strategies to distribute control between humans and AI. We define control as the power to determine, initiate, and direct the process of co-creation. Informed by a systematic literature review of 172 full-length papers, we introduce MOSAAIC (Managing Optimization towards Shared Autonomy, Authority, and Initiative in Co-creation), a novel framework for characterizing and balancing control in co-creation. MOSAAIC identifies three key dimensions of control: autonomy, initiative, and authority. We supplement our framework with control optimization strategies in co-creation. To demonstrate MOSAAIC's applicability, we analyze the distribution of control in six existing co-creative AI case studies and present the implications of using this framework.
This paper investigates whether large language models (LLMs) show agreement in assessing creativity in responses to the Alternative Uses Test (AUT). While LLMs are increasingly used to evaluate creative content, previous studies have primarily focused on a single model assessing responses generated by the same model or humans. This paper explores whether LLMs can impartially and accurately evaluate creativity in outputs generated by both themselves and other models. Using an oracle benchmark set of AUT responses, categorized by creativity level (common, creative, and highly creative), we experiment with four state-of-the-art LLMs evaluating these outputs. We test both scoring and ranking methods and employ two evaluation settings (comprehensive and segmented) to examine if LLMs agree on the creativity evaluation of alternative uses. Results reveal high inter-model agreement, with Spearman correlations averaging above 0.7 across models and reaching over 0.77 with respect to the oracle, indicating a high level of agreement and validating the reliability of LLMs in creativity assessment of alternative uses. Notably, models do not favour their own responses, instead they provide similar creativity assessment scores or rankings for alternative uses generated by other models. These findings suggest that LLMs exhibit impartiality and high alignment in creativity evaluation, offering promising implications for their use in automated creativity assessment.
Storytelling is a fundamental aspect of human communication, relying heavily on creativity to produce narratives that are novel, appropriate, and surprising. While large language models (LLMs) have recently demonstrated the ability to generate high-quality stories, their creative capabilities remain underexplored. Previous research has either focused on creativity tests requiring short responses or primarily compared model performance in story generation to that of professional writers. However, the question of whether LLMs exhibit creativity in writing short stories on par with the average human remains unanswered. In this work, we conduct a systematic analysis of creativity in short story generation across LLMs and everyday people. Using a five-sentence creative story task, commonly employed in psychology to assess human creativity, we automatically evaluate model- and human-generated stories across several dimensions of creativity, including novelty, surprise, and diversity. Our findings reveal that while LLMs can generate stylistically complex stories, they tend to fall short in terms of creativity when compared to average human writers.
This paper explores how people make sense of generative AI for creating art, and how various meanings that people associate with generative AI for creating art relate to their trust toward generative AI for creating art, general orientation toward technologies, and the level of creativity. Taking a perspective of communication and media studies, the present research reports the findings of an exploratory survey (n = 112) conducted in April 2024. The results suggest that positive orientation toward technologies appears to be most relevant for positive meanings and higher trust toward generative AI in the context of creating art, and more frequent use of AI for creating art was also related to higher level of trust. Proficiency was the factor found to be related to creativity, but positive meanings and trust were not. The present study, although exploratory, shows the relevance of these factors, along with a communication and media perspective, to researching computational creativity, as technology continues to be appropriated into our everyday life.
