EVALUATING AI-GENERATED IMAGES IN FINE ART EDUCATION
DOI:
https://doi.org/10.29121/shodhkosh.v6.i4s.2025.6840Keywords:
AI-Generated Imagery, Fine Art Education, Aesthetic Evaluation, Human–Computer Co-Creativity, Generative Models, Digital PedagogyAbstract [English]
This paper discusses the ways in which AI image-generation software, including DALL•E, Midjourney and Stable Diffusion can be assessed and used in art-learning contexts successfully. The study deals with a significant gap in the comprehension: although AI systems can create aesthetically vivid images, their educative, artistic authenticity, and the effect they have on the creative imagination of students is a poorly studied issue. In the form of a mixed-method approach, the research gathers both numerical metrics of performance and anecdotal insights of art teachers, learners, and digital media professionals. The four main criteria of the evaluation of the AI-generated artworks include originality, expressiveness, technical polish, and emotional impact. Based on the constructivist theory of learning, the aesthetic evaluation traditions, and the new model of human and computer co-creativity, the research examines how learners engage with, evaluate, and develop AI-enabled outputs in the studio practice. The experimental modules created to work in the context of a fine-art classroom indicate that AI tools have the potential to increase ideation speed, increase visual experimentation, and assist multimodal aesthetical thinking. Nevertheless, it is also shown that there are strains on authorship, dependence on automated output, and critical digital literacy is required. The comparative analysis shows that there are significant differences in perception of human-made and AI-generated art, especially in such aspects as narrative purpose and perceived genuineness.
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Copyright (c) 2025 Mr. Anup Kumar Singh, Vasanth Kumar Vadivelu, Kalpana Rawat, K. France, Dr. Prabhat Kumar Sahu, Saksham Sood
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