This interdisciplinary course 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, students will investigate how AI systems can be created to align with human goals, values, and cognitive processes. The course 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.
The course 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. In particular, cognitive neuroscience offers a foundation for understanding cognitive load, attention, and user experience, while also inspiring new models and methods in AI, such as attention mechanisms and memory modeling.
Coursework includes weekly reading and critique of research papers, with students taking turns leading in-depth discussions. Topics may include 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.
A major component of the course is a semester-long project that pushes the boundaries of how we think with AI. 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. Projects will follow a human-centered research process that may include need-finding, prototyping, user testing, or critical reflection, depending on students’ interests and backgrounds.
By the end of the course, students 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.
Prerequisites: Familiarity with human-computer interaction, machine learning, or cognitive neuroscience recommended.