AI Is Reshaping Who Gets Credit for Creativity, Major Review Finds

Artificial intelligence has become one of the most consequential forces acting on human creativity, but the most influential research on the topic is not really asking whether machines can be creative anymore. Instead, according to a new review published in AI & Society, the highest-impact scholarship published between 2020 and early 2025 has shifted decisively toward a different set of questions: how generative systems reorganize the conditions under which creative work is produced, evaluated, and legitimized, and who ultimately gets to decide what counts as creative at all.

The study, conducted by Iván Sánchez-López and Gemma San Cornelio of the Universitat Oberta de Catalunya in Barcelona together with Heleny Méndiz-Rojas of the Catholic University of the North in Chile, took a deliberately targeted approach. Rather than attempting an exhaustive census of the literature, the team mined Scopus and Web of Science for publications explicitly engaging both artificial intelligence and creativity, screened them through a PRISMA-informed procedure, de-duplicated the results down to 71 unique records, and then ranked them by citation prominence. The final corpus comprised the 40 most citation-prominent studies, spanning journal articles and full conference papers, all published between 2020 and 5 February 2025.

Once the corpus was assembled, the researchers analyzed it in ATLAS.ti using Grounded Theory and the Constant Comparative Method. Initial coding produced 227 descriptive code entries, refined into 203 first-order codes, which axial coding then organized into nine categories through 230 code-category assignments. A final narrative synthesis distilled eight cross-cutting transformation features. The team used the software’s AI-assisted coding only as a supplementary first pass; every machine suggestion was manually checked, and the final codes, categories, and interpretations were settled by the researchers themselves.

The central finding is that creativity, as portrayed across this influential literature, has become a socio-technical assemblage rather than an individual capacity. Creative outcomes now emerge from the interplay of human-model interaction, institutional gatekeeping, infrastructural asymmetries, and normative conflict. Generative AI is consistently associated with expanded productivity: faster ideation, rapid prototyping, broader exploration of possibility spaces, and new formats of human-AI collaboration. But the same studies repeatedly document a parallel intensification of trouble around authorship, originality, recognition, labour, bias, access, legitimacy, and sustainability.

The reorganization of creative work follows a recognizable pattern. As the cost of producing drafts and variants collapses, the bottleneck migrates from generation to framing, prompting, selecting, editing, and justifying outputs. In human-computer interaction and design research, this appears as iterative prompt craft, co-creative interfaces, and curation as the central creative acts. In education, generative tools are framed simultaneously as scaffolds for writing, feedback, and idea generation, and as threats to assessment, academic integrity, and critical thinking. In management and innovation studies, the emphasis falls on productivity and capability building, with experimental and empirical work showing that augmentation outcomes depend heavily on task conditions, organizational resources, and workflow integration rather than unfolding as a uniform boost to creativity.

The review argues that these expansions do not distribute their benefits evenly. Generative tools may lower the barriers to producing plausible artifacts, yet sector reviews suggest they can concentrate advantage around computing infrastructure, technical expertise, and validation authority. Barriers shift from production toward evaluation, credibility, and institutional endorsement, and access to tools does not automatically translate into recognition or effective use. The review also identifies a mismatch between productive acceleration and evaluative capacity: when systems flood the pipeline with candidates, criteria for quality, originality, and appropriateness do not scale automatically, and selection can drift toward popularity defaults, raising the risk of homogenization even as output volume rises.

Questions of evaluation are further complicated by evidence of human bias against machine-made work. Studies in the corpus position evaluators as gatekeepers whose identity-based perceptions of whether an artifact is human- or AI-produced shape recognition and uptake. In Csikszentmihalyi’s systems view of creativity, creative status is conferred by a field of evaluators, and generative AI disrupts that field function by multiplying and obscuring the sites of evaluation: merit, originality, and responsibility are now negotiated across platforms, curators, clients, reviewers, and model providers, while institutions cling to accountability logics designed for individualized authorship.

Reading the corpus against contemporary scholarship exposes two significant gaps. First, sustainability receives comparatively thin treatment. While the reviewed studies address it mainly through urban and infrastructural perspectives, recent research has developed systematic accounts of the material externalities of large-scale generative systems, including electricity demand, carbon and water footprints, data-centre infrastructure, and disclosure obligations. Second, Global South and minoritized perspectives remain weakly integrated into the dominant corpus, even though training data distributions, dominant-language resources, and uneven access to computation may act as technical and cultural priors that determine which outputs and epistemic positions become visible in the first place.

The authors are candid about the limits of their design. Focusing on the most-cited publications maps dominant framings but may reproduce existing epistemic hierarchies, since citational prominence is treated as an indicator of visibility and influence rather than quality or truth. The corpus also reflects indexed, anglophone, predominantly Global North academic infrastructures, and the 2025 slice of coverage is partial by construction. The researchers call for future work using ethnographies of everyday use, comparative studies across languages and cultures, and mixed methods connecting computational traceability with critical analysis of labour and environmental costs.

The study’s principal interpretive conclusion is that expanded productivity and contested authority are not separate outcomes but interrelated dimensions of AI-mediated creativity. Artificial intelligence does not simply automate creative tasks; it relocates where creative agency sits, changes how contribution is evaluated, and reshapes which actors and infrastructures participate in producing legitimacy. High-impact scholarship, the authors argue, does not merely describe this transformation. It also helps stabilize influential definitions of creative value and redistribute forms of recognition, making it all the more important to examine how authority, visibility, and material conditions determine which forms of AI-mediated creativity become visible and legitimate.

Subject of Research: A citation-informed review of how highly cited research frames the transformation of creativity under artificial intelligence

Article Title: Expanded productivity, contested authority: creativity and AI in high-impact research

Article References: Sánchez-López, I., Méndiz-Rojas, H., & San Cornelio, G. (2026). Expanded productivity, contested authority: creativity and AI in high-impact research. AI & SOCIETY. https://doi.org/10.1007/s00146-026-03213-4

Image Credits: AI Generated

DOI: 10.1007/s00146-026-03213-4

Keywords: artificial intelligence, creativity, generative AI, authorship, human-AI collaboration, creative industries, evaluation, legitimacy, bias, sustainability, research review, AI & Society