Collaborative Algorithmic Creations between Humans and Machines: Studies of Automatic Image Composition based on Artificial Intelligence for Art Education

Authors

  • Ana María Marqués Ibáñez Facultad de educación, Universidad de La Laguna (Spain)

DOI:

https://doi.org/10.5281/zenodo.16381901

Keywords:

Art Education, Artificial Intelligence, Generative Text-to-image Models, Generative AI, Generative Tools, Machine Learning

Abstract

Technological developments in AI foster creativity in the world of liquid modernity in education. As an introduction, the relationship between AI and previous educational experiences is analysed, as well as how the creation of images with generative models in AI has evolved. This article presents a practice based on images created with AI generators and their application in the early childhood education degree programme. The methodology used is quantitative, with Likert surveys to verify its applicability and a description of the activity in a portfolio. Before designing the AI practice, similar experiences with AI in art and education are reviewed to see if it is an appropriate medium. The overall objective of the project is to analyse the impact and creative possibilities of using AI to create children’s storybook covers as a teaching resource in teacher training. Among the specific objectives, AI tools for illustrating stories are explored and critical thinking about the construction of these images is promoted, not as a final art form, but based on the process. The result shows an educational experience implemented in the early childhood education degree programme with the creation of children’s book covers. I conclude that AI is a resource that provides skills and allows for the exploration of alternatives in the process of creating an image.

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Published

2025-07-28

How to Cite

Ana María Marqués Ibáñez. (2025). Collaborative Algorithmic Creations between Humans and Machines: Studies of Automatic Image Composition based on Artificial Intelligence for Art Education. Comunicar, 33(82), 230–244. https://doi.org/10.5281/zenodo.16381901

Issue

Section

Research Article