Dingwen Kong

Dingwen Kong is a graduate student in electrical engineering and computer science rethinking how AI fundamentally transforms human behavior and learning. As AI systems become increasingly deployed across society, understanding their impact on knowledge acquisition and skill development remains critical yet poorly understood. He develops theoretical frameworks to analyze human-AI collaboration, investigating when AI assistance amplifies human judgment versus when it impairs performance through cognitive biases. Dingwen explores how reliance on language models affects human learning and investment in skill-building, modeling scenarios where individuals delegate simple tasks to AI, potentially eroding knowledge necessary for future work. Using MATLAB, he simulates model dynamics and visualizes regime boundaries to translate abstract theory into concrete insights before formal proof. He contributes to reinforcement learning with human feedback by developing methods to incorporate heterogeneous user preferences through personalization and preference aggregation. As a MathWorks Fellow, Dingwen will advance these theoretical frameworks. His research could fundamentally reshape our understanding of human-AI coevolution and long-term implications of intelligent systems.