DesignAbout
Artificial Intelligence
Intelligent UI Design
My Master's thesis in computer science explores the intersection of artificial intelligence and user interface design, specifically focusing on how dashboards can dynamically adapt their layout and functionality based on user context and behavioral patterns. In today's rapidly evolving technological landscape, where AI systems are becoming increasingly sophisticated and autonomous, meta-cognition—the ability to think about thinking—and meta-learning—learning how to learn—have become more critical than ever for both users and systems alike.
Rather than presenting static interfaces with overwhelming arrays of options, intelligent dashboards leverage machine learning algorithms to surface the most relevant tools, information panels, and actionable buttons at precisely the right moment. This approach transforms traditional dashboard design from a one-size-fits-all solution into a personalized, context-aware experience that anticipates user needs—whether surfacing critical alerts during system anomalies, highlighting relevant data visualizations based on current tasks, or intelligently reorganizing navigation elements based on workflow patterns.
As AI systems become more complex, the need for interfaces that can help users understand not just what the system is doing, but how it's learning and adapting, becomes paramount. By applying principles of predictive design and adaptive interfaces, this research demonstrates how AI can enhance the fundamental design goal of reducing cognitive load while maximizing user efficiency, creating dashboards that feel less like complex control panels and more like intelligent assistants that understand and support the user's decision-making process while fostering transparency in AI reasoning.
https://github.com/jhsu08/MARCO