The impact of generative modeling on the efficiency of digital artistic expression: An experimental study in technologically enhanced learning environments

Abstract

A b s t r a c t :Learners struggle with the efficiency of digital artistic expression when using traditional digital tools. Creative processes are time-consuming, and many suffer from a limited range of ideas and artistic compositions. Furthermore, current learning environments lack a systematic approach to generative modeling techniques, despite their crucial role in automating parts of the creative process, reducing production time, increasing rapid experimentation, and enriching learners' expressive repertoire by suggesting unexpected artistic alternatives. This, in turn, improves accuracy, creativity, and flexibility. This paper aims to measure the impact of using generative modeling (as DALL-E model) on the digital artistic expression proficiency of digital arts students in technologically enhanced learning environments. The quasi-experimental methodology was adopted with the design of two groups (experimental and control) with pre- and post-testing. The experiment was applied to a sample of digital arts students ps (experim during six training sessions, using a digital platform equipped with a generative model for the experimental group versus traditional tools (such as Photoshop without generative artificial intelligence) for the control group, with efficiency being measured across five dimensions: expressive fluency, originality, suitability to the subject, technical accuracy, and speed. Statistical processing was carried out using means, standard deviations, and the t-test. The scientific novelty of this paper lies in presenting a quantitative experimental model that compares the impact of generative modeling and traditional tools in digital arts education, along with developing a multidimensional measurement tool for the efficiency of digital artistic expression. The results showed...