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ICCC 2026: International Conference on Computational Creativity

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Submission Date:
2026-03-01 Extended
Notification Date:
2026-04-20
Conference Date:
2026-06-29
Location:
Coimbra, Portugal
Years:
17
ICORE: C   Viewed: 900   Tracked: 0   Attend: 0

Conference Partner Index (CP-I)

52.8 / 100
Ranked #1,099 of 5,687 conferences · Top 20%
Academic recognition (35%)
58
Submission selectivity (20%) No data - scored at the neutral baseline of 50 —
Editions held (20%)
78
Community attention (10%)
16
Public record completeness (15%)
35

Inputs used: Listed as ICORE C · Editions on record: 17 · Researchers who opened this page in the past 24 months: 8

Missing from the public record: Historical acceptance rates (+4.5) · Past editions (+3.0) · Best-paper records (+2.3)
Organizers can add these from this page after claiming the conference; scores are recomputed nightly. How to raise this score

Confidence 80% - the share of the score backed by observed data rather than the neutral baseline. How this score is calculated · Browse the ranking · Algorithm version 1.1 · Computed 2026-09-29

Call For Papers

ICCC 2026 (International Conference on Computational Creativity) is a ICORE C conference held in Coimbra, Portugal on 2026-06-29. The paper submission deadline is 2026-03-01 (extended). Acceptance notifications are sent on 2026-04-20.

Themes and Topics Original research contributions are solicited in all areas related to Computational Creativity research and practice, including, but not limited to: Foundations of Computational Creativity: theories, models, and principles of computational creativity. Interdisciplinary Perspectives: perspectives on computational creativity which draw from philosophical and/or sociological studies in the context of creative AI systems. Computational Paradigms: computational approaches for modelling cognitive aspects of creativity, such as heuristic search, analogical and meta-level reasoning, cognitive architectures, and re-representation. Human-Machine Co-Creativity: systems, studies, frameworks, or methodologies related to co-creativity between humans and AI, with emphasis on systems in which the machine acts as a creative partner. Social Models: computational models of social aspects of creativity, including: social creativity, the diffusion of ideas, collaboration, team dynamics, and creativity in social settings. Psychological Factors: computational models of psychological factors that enhance creativity, including emotion, surprise (unexpectedness), reflection, conflict, diversity, motivation, knowledge, intuition, reward structures. Additionally, social or experiential factors related to novelty and originality, such as innovation, improvisation, and virtuosity. Societal Impact: ethical considerations in the design, deployment or testing of creative AI systems, as well as studies that explore the societal impact of computational creativity and generative AI. Computational Creativity Evaluation: metrics, frameworks, formalisms and methodologies for the evaluation of creativity in computational systems, or for the evaluation of how such systems are perceived/accepted in society. Applications of Computational Creativity: computational applications of creativity in areas such as music, language (e.g, narrative, poetry, humor), games, visual arts, design, architecture, entertainment, education, mathematical invention, scientific discovery, programming. Applications should be evaluated for their creativity using methods of the CC field, and the papers should carry a message relevant for the CC community. Data and Creativity: data science approaches to computational creativity: resource development and data gathering/knowledge curation for creative AI. There is a need for datasets and resources that are scalable, extensible and freely available/open-source. Provocations: raising new issues not on this list that bring the foundations of the discipline into question or throw new light on seemingly settled debates. A note on generative AI models: while the study of generative AI models is both welcomed and encouraged, such models and their application must be properly situated in the CC literature and evaluated according to acceptable practices in the field. Papers that fail to do this are unlikely to be reviewed favorably.
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