1. Title of the paper(s): 
Teaching Creativity in the Age of AI: Introducing Generative AI Image Generation  


2. Authors (full details):
Y. Dianey Rueda-Arango, Independent/Artist
Full postal address: Carrera 95A #90-42 Sur/Conjunto la Unión 1 de Marlene, apartamento 403 Torre 20, Bogotá, Bogotá 110700, Colombia
Email: yenlhyn24@gmail.com
Phone: +57 31 75 81 25 64


László Szabó, Independent/Artist
Full postal address: NDSM-plein 105 1033WC AMsterdam, The Netherlands
Email:info@lensofthestorm.com 
Phone: +31 6 36 46 20 83


Alberto Tonda, INRAE
Full postal address: INRAE – Université Paris‑Saclay, 12 route 128, 91190 Gif‑sur‑Yvette, France 
Email: alberto.tonda@inrae.fr
Phone: +33 6 95 24 52 93


Alejandro Lopez-Rincon, Utrecht University
Full postal address:Department of Pharmaceutical Sciences (Pharmacology), Utrecht University, ,Universiteitsweg 99 3584 CG Utrecht, The Netherlands
Email: a.lopezrincon@uu.nl
Phone: +33 6 43 19 53 33


3. Corresponding author: 
Alejandro Lopez-Rincon Email: a.lopezrincon@uu.nl


4. Abstract: 
This chapter builds upon research about human-made versus AI-generated art and emotional response. The authors conduct a Turing test to assess whether individuals perceive CS-generated images as human-made or AI-based and whether they consider them art. The chapter highlights their study, as well as a gallery exhibition of human and AI art.


5. Criteria claimed: D 


6. Statement of human-competitiveness: 
The result satisfies Criterion D because it achieves competitive selection in a regulated artistic evaluation involving human contestants under external, arms-length conditions. The proposed Interactive Evolutionary System (IES) was applied in the context of the “I Art My Science” (IAMS) exhibition, an open-call juried event associated with Utrecht University. In this process, hundreds of artworks are submitted by human participants and are evaluated by an independent jury and curator, and only a limited subset of works is selected for exhibition based on aesthetic merit. Artworks created through the human–AI co-design process using the proposed evolutionary computation framework were included within this juried selection context. Evidence of selected works from this exhibition can be found here: https://www.iartmyscience.nl/laszlo-szabo  This demonstrates that outputs produced using the proposed evolutionary system are capable of meeting externally defined aesthetic standards in direct comparison with human-produced works, satisfying the “arms length” requirement specified in the competition rules. 


7. Full citation: 
"Teaching Creativity in the Age of AI: Introducing Generative AI Image Generation " in Artificial Intelligence in Arts Administration: Cases in Practice and Pedagogy. Eds. Alicia Jay, Youngaah Koh, & Erin Hoppe. Routledge, expected 2026. Peer reviewed. In Press. 


8. Prize distribution: 
Any prize money, if any, is to be divided equally among the co-authors. 


9. Why this entry should be the best: 
This entry demonstrates human-competitive performance in one of the most challenging domains for computational systems: subjective aesthetic evaluation. Unlike traditional optimization problems, artistic evaluation cannot be explicitly formalised as a fixed objective function. This work shows that evolutionary computation, combined with human-in-the-loop evaluation, can successfully operate in this domain by integrating human judgement directly into the optimisation process. Furthermore, the results are validated not only experimentally, but through participation in a real-world juried artistic selection process involving human participants and external evaluation. This provides strong arms-length evidence that the outputs generated by the system are competitive with human-created artworks. The combination of evolutionary computation, human-guided optimisation, real-world deployment, and external validation demonstrates a novel and impactful application of evolutionary computation beyond traditional domains. 






10. Type of evolutionary computation used:
Interactive Evolutionary Systems (IES) combine evolutionary algorithms with human-in-the-loop evaluation; the evolutionary component is Bayesian optimization coupled with Covariance Matrix Adaptation Evolution Strategy (CMA-ES) to explore the internal surrogate model. Please, see the manuscript Image Generation with Interactive Evolutionary System using Bayesian Optimization, attached as reference.  


11. Date of publication:
May 27, 2026 (in press), attached confirmation and editorial comments for the final version, hope this is enough.