This interdisciplinary project explores the rapidly evolving field of Human-AI Interaction through the lens of human-computer interaction (HCI), artificial intelligence (AI), and neuroscience, with a particular focus on how people think with, through, and alongside intelligent systems. Drawing from human-centered design principles, machine learning methods, and cognitive neuroscience, teachers will investigate how AI systems can be created to align with human goals, values, and cognitive processes. The project emphasizes a human-centered approach to the design of AI systems, grounded in an understanding of users’ needs, contexts, and mental models, and considers how intelligent systems can support human agency, collaboration, and decision-making. To make this work concrete, teachers may explore applications in judicial decision-making, mental health clinical practice, and education, three domains where high-stakes decisions and human values are critical. The project also considers how insights from neuroscience, such as how the brain processes information, manages attention, and supports learning, can inform the design of more effective and human-compatible AI systems. 

Faculty Advisor: Erin Solovey | WPI (Computer Science)

Teacher Component: Projects may involve designing or implementing novel human-AI systems, developing cognitively inspired AI models, or conducting empirical studies of user interaction with intelligent tools. Teachers will get experience with machine learning fundamentals, prompt engineering, LLM interaction design, human-centered evaluation methods, ethics and alignment in human-AI systems, trust and safety, conversational agents and explainability, multimodal interfaces, affective computing, neuroadaptive systems, and more. Background material will cover brain function, machine learning fundamentals, and user-centered methods for evaluating human-AI systems. Projects will follow a human-centered research process that may include need-finding, prototyping, user testing, or critical reflection, depending on teachers’ interests and backgrounds. By the end of the summer, teachers will be able to critically analyze and design AI systems that augment human cognition, applying insights from machine learning, neuroscience, and interaction design with a human-centered lens.