A-EYE MAP; A COMPARATIVE ANALYSIS OF AI-PREDICTED ATTENTION MAPS IN CROSS-CULTURAL DESIGN

Understanding visual attention is essential for designers to create effective communication. Recent advancements in deep learning have enabled AI-driven attention mapping tools to simulate visual focus and assist in improving design outcomes. While these tools rely on neuroscience and eye-tracking data, their predominantly Western-centric datasets raise concerns about cultural bias and limited semantic understanding. This study explores the cultural adaptability of AI tools—Attention Insight, Neurons AI, and Expoze—by applying them to Persian designs featuring Farsi typography, traditional motifs, and culturally specific color palettes. Using a mixed-methods approach including case studies, interviews, surveys, and experimental analysis, the research examined the effectiveness of these tools in interpreting cultural elements. Findings revealed that while algorithms can support the foundational phases of design, they often overlooked symbolic elements, and contextual relevance. This study highlights the strengths and limitations of current AI tools and offers actionable insights for developing inclusive technologies that that support culturally responsive and globally impactful design.