This course provides an introduction to core principles in the cognitive science of learning, attention, and decision making, and examines how these processes are being reshaped through interactions with artificial intelligence. Rather than focusing on AI as a replacement for human cognition, the course centers on emerging evidence that human performance can be enhanced when AI systems are integrated into real-world cognitive workflows. We will review foundational research on learning, memory, and executive function, alongside contemporary studies demonstrating how AI-supported environments can augment human cognition. Examples will include domains such as individualized tutoring, medical decision making, and complex problem solving, where human-AI collaboration is beginning to produce measurable gains in learning, accuracy, and efficiency. A central theme of the course is understanding the mechanisms by which these gains occur. When does AI support deeper learning versus superficial performance improvements? How do factors such as attention, metacognition, and trust shape effective human-AI interaction? And how can insights from cognitive neuroscience inform the design of AI systems that scaffold, rather than supplant, human thinking? The course will emphasize critical evaluation of current research, with attention to both opportunities and limitations. Students will engage with empirical findings, analyze case studies, and consider broader implications for education, expertise development, and the future of cognitively demanding work.
3 units · Letter or Credit/No Credit
This course provides an introduction to core principles in the cognitive science of learning, attention, and decision making, and examines how these processes are being reshaped through interactions with artificial intelligence. Rather than focusing on AI as a replacement for human cognition, the course centers on emerging evidence that human performance can be enhanced when AI systems are integrated into real-world cognitive workflows. We will review foundational research on learning, memory, and executive function, alongside contemporary studies demonstrating how AI-supported environments can augment human cognition. Examples will include domains such as individualized tutoring, medical decision making, and complex problem solving, where human-AI collaboration is beginning to produce measurable gains in learning, accuracy, and efficiency. A central theme of the course is understanding the mechanisms by which these gains occur. When does AI support deeper learning versus superficial performance improvements? How do factors such as attention, metacognition, and trust shape effective human-AI interaction? And how can insights from cognitive neuroscience inform the design of AI systems that scaffold, rather than supplant, human thinking? The course will emphasize critical evaluation of current research, with attention to both opportunities and limitations. Students will engage with empirical findings, analyze case studies, and consider broader implications for education, expertise development, and the future of cognitively demanding work.
Offered in Winter 2027 at Stanford University.