Sep 2026· Information systems research· 0 citations· 11 references
TL;DR
The five articles in this Special Issue show that the promise of computational systems—in particular, generative AI—lies in configuring human and machine agency so that creative work produces ideas and artifacts that are deemed creative—that is, novel and useful—while also safeguarding the generative, effortful, accountable, and socially meaningful nature of creativity.
Abstract
This editorial presents computational creativity as a durable research program for Information Systems. Defining creativity as a structured, instrumentable, and improvable process of search, recombination, evaluation, and realization, we conceptualize computational creativity as being concerned with computational systems that model, simulate, enact, or augment the creative behavior of human creators. The five articles in this Special Issue show that the promise of computational systems—in particular, generative AI—lies in configuring human and machine agency so that creative work produces ideas and artifacts that are deemed creative—that is, novel and useful—while also safeguarding the generative, effortful, accountable, and socially meaningful nature of creativity. We translate these findings into an IS research agenda focused on process instrumentation, diversity-preserving design, agency-sensitive governance, organizational capability building for human-AI creative systems, and responsible computational creativity. The core challenge is to engineer the practices of computational creativity without forfeiting either the novelty or usefulness of the output, or human agency.
It is concluded that resistance to attributing creativity to GenAI reflects genuine conceptual distinctions alongside persistent anthropocentric assumptions, and that current deployment practices carry ethical implications that demand an institutional response rather than case-by-case management.
It is argued that current generative AI systematically favors combinatorial creativity while offering weaker support for transformational creativity, and that HCI should evaluate AI tools not only by the quality of creative outputs they enable but by the distribution of creative types they support, privilege, or suppre...
Peter Dalsgaard· Proceedings of the 14th Nord...· 0 citations
Abstract This study examines the transition from automation to generative AI in professional communication, focusing on human–AI co-creativity. While earlier research framed AI mainly as an efficiency tool, the rapid emergence of generative AI has reconfigured communication practices toward collaborative and co-creativ...
T. Kiilu, Martin Kuria Githinji· Journal of communication and...· 0 citations
Generative Artificial Intelligence (Generative AI) is fundamentally reshaping media workflows by restructuring creative processes and the institutional conditions of knowledge production. While current research has deeply analyzed AI through the lenses of automation, algorithmic governance, and professional ethics, the...
D. Tran· International Journal of Ped...· 0 citations
This article examines how highly cited scholarship published between 2020 and February 2025 frames the transformation of creativity under the influence of artificial intelligence. To address this question, the study uses a citation-informed targeted review combined with qualitative interpretive synthesis. Records wer...
Iván Sánchez-López, Heleny Méndiz-Rojas, Gemma San Cornelio· AI & SOCIETY· 0 citations
It is argued that creativity in the context of generative AI is best understood as a property of hybrid collectives, or populations of interacting people and algorithms, rather than of individuals.
Mason Youngblood, Katie Mudd, Manuel Anglada-Tort et al.· 0 citations
Related blog posts
MIT News · Artificial Intelligence· news.mit.eduOct 8, 2026
Exploring how generative AI could make machine vision more accessible to businesses. The post GenEye in a Box: Making Machine Vision Something You Can Just Ask For appeared first on GPT-Lab.
MIT News · Artificial Intelligence· news.mit.eduOct 8, 2026
Training AI agents with reinforcement learning can be challenging because their tools, context, and decision-making are managed by complex frameworks. Agent Lightning connects existing agents to RL training, making it easier to improve them without rebuilding them. The post Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses appeared first on Microsoft Research.
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