Human-AI Collaborative Adaptive Typeface Generation: Eye-Tracking Fixation Metrics in Instagram Branding
DOI:
https://doi.org/10.51903/ijgd.v4i1.3289Keywords:
Human-AI Collaboration, Adaptive Typeface, Eye-Tracking Metrics, Visual Engagement, Instagram BrandingAbstract
Integrating artificial intelligence into typographic design opens new opportunities for social media branding, especially on visually intensive platforms like Instagram. This study investigates how typefaces developed in collaboration with AI may influence visual engagement metrics. We designed an assessment framework that couples collaborative typeface generation with simulated eye-tracking analysis. Three adaptation scenarios were evaluated: static, semi-adaptive, and fully adaptive typefaces, based on fixation duration, fixation count, and heatmap visualization. Interestingly, when analyzing the simulation results, a pattern emerged: the typefaces generated through iterative human-AI processes tended to attract longer, more frequent visual fixations. These preliminary results thus point to the potential value of collaborative approaches to typographic design in social media contexts. The present study provides a methodological framework to assess AI-assisted visual assets using human attention metrics.
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