Ming Yin

Office: LWSN 2142B
Email: mingyin [AT]
Office Hours: By Appointment

Course Description

Human-AI interaction sits at the intersection of human-computer interaction and artificial intelligence, and relate to psychology, communication, cognitive science, and design. In this course, we will focus on learning how to develop empirical understandings of humans' interactions with AI systems, and how to incorporate user-centered design principles to design AI systems that can enable effective interactions between people and the systems. This course starts with a brief review of fundamentals of human cognition and artificial intelligence, as well as a discussion of user-centered design lifecycle and general principles for designing human-AI interactions. Then, we will delve into a wide range of specific topics on human-AI interaction, including how to design explainable, trustworthy, fair and ethical AI systems, how to enable effective human-AI collaboration and teaming, and what new opportunities and challenges do the rise of large language models bring to human-AI interaction.

Course Schedule

See the calendar page.


Paper Reading, Presentation and Discussion

Most classes in this course consist of paper reading, presentation and discussion. Specifically, in a typical class, we will have 2 required papers to read on one topic, and 2 students will be assigned as the leading presenter on this topic.

Responsibility of leading-presenters of one class includes:

Responsibility of non-leading-presenters of one class includes:

Final Project

Final project serves as an opportunity for students to get hands-on experience in human-centered computing research. Projects are open-ended; sample projects include:

Students are also encouraged to connect the final project with their own research.

Students are recommended to complete the project in a group of 2-4 persons. Tasks related to the final project include:

More detailed instruction on the final project will be provided through project guidelines.


Basic programming skills required. Students should be comfortable with at least one programming language (e.g., C, Java, Python, etc.). Knowledge with artificial intelligence and machine learning is welcome.

Required Texts

No textbook is required for this course.