VISUAL COMMUNICATION THROUGH AI-GENERATED INFOGRAPHICS - Marwah, Aparna; Suryawanshi, Ashok Rajaram; Rahate, Dr. Ganesh Ramkrishna; Singh, Anuj Kumar; Patil, Suvarna; Desai, Anita; Dhabliya, Dharmesh (ShodhKosh: Journal of Visual and Performing Arts, 2025)
Visual communication is already an essential tool used to deliver sophisticated information in data-driven societies in the most effective way. As a combination of visual design, narrative, and presentation of data, infographics is at the heart of creating the understanding, interest, and judgment. As a result of recent developments in the sphere of artificial intelligence (AI), the work of infographics creation is becoming more automated, which alters the traditional design processes. This paper examines the theme of visual communication in terms of AI-generated infographics, how computational models redefine the time-tested principles of perception, semiotics, and information design. The paper incorporates cognitive theory of the visual perception along with semiotic encoding theory to put into perspective how AI systems transform the data into meaningful visual representation. It also examines how machine learning and deep learning algorithms, e.g., pattern recognition, layout optimization, style transfer, etc., can be used to automate the process of infographic creation. The outcomes are the visual quality, clarity, the aesthetic coherence, and the storyline effectiveness, with the results of AI-generated visuals and human-designed infographics being compared. The results show that the AI systems show high consistency, scalability, and accuracy in data, and have low-contextual creativity, cultural sensitivity, and interpretability. Other issues identified in the study include data bias problems, automation-creativity trade-offs and design decision explainability.