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<p>This study presents an innovative AI-powered framework that integrates Generative Adversarial Networks, Convolutional Neural Networks, Reinforcement Learning, and Big Data analytics to personalize and enhance the emotional resonance of visual designs. A hybrid GAN + RL model dynamically adapts to user feedback, while clustering and sentiment analysis enable emotion-driven personalization. A/B testing and surveys reveal that AI-generated designs achieved 92% design accuracy and significantly higher emotional engagement and user satisfaction compared to traditional and generic AI designs (<i>p</i> &lt; 0.01). This work demonstrates AI’s potential to augment human-centered design while acknowledging its creative limitations.</p>

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Reinforcement Learning–Enhanced GAN Framework for Data-Driven Personalization in Visual Design

  • 期刊论文

  • 发表日期：

    2026年4月9日

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  • Zhe Yun Wu

摘要

This study presents an innovative AI-powered framework that integrates Generative Adversarial Networks, Convolutional Neural Networks, Reinforcement Learning, and Big Data analytics to personalize and enhance the emotional resonance of visual designs. A hybrid GAN + RL model dynamically adapts to user feedback, while clustering and sentiment analysis enable emotion-driven personalization. A/B testing and surveys reveal that AI-generated designs achieved 92% design accuracy and significantly higher emotional engagement and user satisfaction compared to traditional and generic AI designs (p < 0.01). This work demonstrates AI’s potential to augment human-centered design while acknowledging its creative limitations.