AI-Driven Culturally Aware Interactive Visualization: A Design Methodology for Cross-Cultural User Experience.

Area of Science:

• Human-Computer Interaction
• Artificial Intelligence
• Cultural Studies

Background:

• Current interactive information visualization systems lack cultural integration, hindering cross-cultural communication and user engagement.
• Diverse global audiences require culturally sensitive and adaptive visualization tools for effective interaction.

Purpose of the Study:

• To develop and validate a methodology for creating culturally appropriate interactive visualization systems using artificial intelligence.
• To enhance user engagement, satisfaction, and reduce cognitive load in multicultural contexts through AI-driven visualization.

Main Methods:

• Developed an intelligent cultural element identification algorithm with 91.2% accuracy.
• Created an adaptive visualization generation framework incorporating cultural preferences.
• Implemented real-time interface optimization based on user behavior patterns.
• Conducted experimental validation with 600 participants from diverse cultural backgrounds.

Main Results:

• Achieved a 23.5% increase in user satisfaction and a 31.7% enhancement in user engagement.
• Demonstrated an 18.3% reduction in cognitive load compared to traditional methods.
• Evaluation showed superior performance in aesthetic appeal, functional usability, and cultural appropriateness.

Conclusions:

• AI-driven, culturally informed interactive visualizations significantly improve user experience and engagement in multicultural settings.
• The research provides a practical framework for designing culturally sensitive AI systems for global audiences.
• Findings contribute to understanding cultural influences on visualization perception and design.