CAREER: Modeling Trust Dynamics and System Adaptation for Human-AI Collaboration in Engineering Design

About This Grant

This Faculty Early Career Development Program (CAREER) project supports research and education focused on trust in human-artificial intelligence (AI) collaboration during early-stage engineering design. In open-ended, creative design contexts, trust governs how designers engage with and rely on AI-generated suggestions, yet this reliance is often implicit, context-dependent, and difficult to capture by traditional acceptance-based or post-hoc survey measures. This project advances fundamental knowledge by modeling trust as a continuous, time-varying latent state variable grounded in designers’ affective and cognitive processes, creating a foundation for trust-aware human-AI interaction in early-stage design. This CAREER award advances theory and methods for trust in human-AI collaboration through two integrated research activities: (1) developing empirical datasets and computational inference models that estimate trust dynamics as a continuous, normalized quantity from synchronized behavioral, self-report, and psychophysiological indicators during real-time design interaction; and (2) formalizing trust-aware AI adaptation strategies that specify how AI feedback behavior should adjust to support calibrated reliance and effective collaboration. Education and outreach activities will deploy AI-assisted design tools in undergraduate engineering design courses and pre-college design bootcamps, and will integrate multimodal sensing to enable adaptive, personalized feedback that supports cognitively demanding learning. This research includes advancing the design and deployment of trust-aware AI systems that improve engineering decision quality and reliability, reduce design cycle time and downstream rework, and support effective human-AI collaboration across high-stakes industrial domains such as manufacturing, healthcare, and infrastructure. In parallel, the project will enhance STEM education and workforce development by integrating AI-assisted design and personalized learning tools into undergraduate and pre-college programs, broadening participation and preparing an AI-literate engineering workforce. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.