NEGOTIA: Developing Visual Literacy and Bias Awareness for GenAI

Images generated by artificial intelligence recombine visual fragments learned from datasets, producing representations based on criteria of semantic proximity and aesthetic familiarity. These images lie in an intermediate zone between verisimilitude and statistical construction, requiring new interpretative skills to understand their nature and limitations. This paper explores the need to develop visual literacy for generative AI, understood as the critical ability to analyse generation processes, recognise implicit biases, and verify the consistency of the representations produced. Through some case studies, prompting is analysed as a dialogical and reflective practice that highlights recurring patterns in datasets and diffusion models. The cases highlight how automatic composition tends to reproduce dominant cultural patterns related to gender, posture, and professional role. This paper introduces NEGOTIA, a seven-step framework designed to foster critical and operational visual literacy, applicable in educational and design contexts where synthetic images function as tools for representation, communication, and verification. NEGOTIA offers a replicable model for education and design practice.