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Evaluating Creative Work with Artificial Intelligence: Evidence from Constrained Innovation Tasks

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  • Valerio Fedele Addis
  • Giuseppe Attanasi
  • Giovanni Di Bartolomeo
  • Michele Mariella
  • Valentina Peruzzi

Abstract

We study whether a large language model can reliably evaluate human creativity in constrained, innovation-like tasks. Using expert-generated creative outputs from a validated experiment with workers in cultural and creative industries, we embed ChatGPT as an evaluator and benchmark its assessments against expert human judgments obtained through the Consensual Assessment Technique. In Study 1, we show that AI-based creativity evaluations exhibit internal consistency comparable to that of expert judges across repeated and independent runs, even under conservative scenarios. Replacing a human judge with an AI evaluator does not reduce inter-rater reliability across drawing, mathematical, and verbal tasks. In Study 2, we find that AI evaluations are systematically structured along fluency, flexibility, originality, and elaboration, with task-specific weighting of these dimensions. Overall, the results indicate that AI can serve as a reliable and structured evaluator of creativity in constrained innovation environments.

Suggested Citation

  • Valerio Fedele Addis & Giuseppe Attanasi & Giovanni Di Bartolomeo & Michele Mariella & Valentina Peruzzi, 2026. "Evaluating Creative Work with Artificial Intelligence: Evidence from Constrained Innovation Tasks," CIMEO Working Paper Series 197, Centre for Investigation and Modelling of Experimental Observations (CIMEO).
  • Handle: RePEc:ter:wpaper:00197
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    1. Giuseppe Attanasi & Ylenia Curci & Patrick Llerena & Maria del Pino Ramos-Sosa & Adriana Carolina Pinate & Giulia Urso, 2019. "Looking at Creativity from East to West: Risk Taking and Intrinsic Motivation in Socially and Culturally Diverse Countries," Working Papers of BETA 2019-38, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
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    16. Attanasi, Giuseppe & Curci, Ylenia & Llerena, Patrick & Urso, Giulia, 2026. "Intrinsic vs. extrinsic motivators on creative collaboration: The effect of sharing rewards," Journal of Economic Behavior & Organization, Elsevier, vol. 245(C).
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    Keywords

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    JEL classification:

    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • M14 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - Corporate Culture; Diversity; Social Responsibility
    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior

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