AI-GENERATED VISUAL CONTENT FOR EDUCATIONAL POSTERS - Jena, Mr. Sanjay Kumar; Punia, Ankit; Malarvel, Muthukumaran; Jha, Rakhi; Anute, Dr. Nilesh; Barkade, Shrikant Shrimant; Kaur, Gurpreet (ShodhKosh: Journal of Visual and Performing Arts, 2025)
The study examines how AI-generated visual output can be used to transform the educational poster design process to a more creative, pedagogical, and automated one. As AI products are rapidly adopted in creative education, diffusion models, generative adversarial networks (GANs), and multimodal transformers are beginning to be used more and more to generate visually high-quality, topic-aligned images. The research is done to assess how these systems can ease poster-making among teachers, students and content designers and provide the aesthetic integrity and semantic correctness. Various educational themes and poster templates were collected and trained and tested the AI-based generation pipelines using the data. The AI-based new design is proposed as an automation of the design process based on the architecture combining the modules of prompt engineering, content alignment, and layout synthesis and converting textual or conceptual input into structured visual forms. The quantitative and qualitative analyses were performed to assess the correctness of the concept visualization, graphical balance, and engagement of the learner with the help of the user survey and expert evaluation. In the results, it is found that AI-interactive poster graphics can be hugely helpful in enhancing the learner attention, their visual understanding and retention in comparison with the hand-designed graphics.