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AI MODELS FOR PREDICTING VISUAL ATTENTION IN DIGITAL APPLICATIONS: A COMPARATIVE PILOT ANALYSIS WITH EYE TRACKING RESULTS
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The paper presents a comparative pilot analysis of an AI-based visual attention prediction system with the traditional eye-tracking method in the context of UI/UX design. The study assesses the accuracy and reliability of AI-generated attention heatmaps compared to empirical eye-tracking data. The results indicate that AI systems show a strong ability to approximate human attention, achieving ROC-AUC values between 0.75 and 0.84. The study highlights AI's potential in early-stage design iterations, offering a suitable alternative to time-consuming eye-tracking testing sessions, but recommends combining AI tools with traditional methods for critical design decisions.
UNIVERSITY OF NOVI SAD FACULTY OF TECHNICAL SCIENCES DEPARTMENT OF GRAPHIC ENGINEERING AND DESIGN 21000 Novi Sad, Trg Dositeja Obradovića 6
Title: AI MODELS FOR PREDICTING VISUAL ATTENTION IN DIGITAL APPLICATIONS: A COMPARATIVE PILOT ANALYSIS WITH EYE TRACKING RESULTS
Description:
The paper presents a comparative pilot analysis of an AI-based visual attention prediction system with the traditional eye-tracking method in the context of UI/UX design.
The study assesses the accuracy and reliability of AI-generated attention heatmaps compared to empirical eye-tracking data.
The results indicate that AI systems show a strong ability to approximate human attention, achieving ROC-AUC values between 0.
75 and 0.
84.
The study highlights AI's potential in early-stage design iterations, offering a suitable alternative to time-consuming eye-tracking testing sessions, but recommends combining AI tools with traditional methods for critical design decisions.
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