Course Schedule

The class is structured in four modules.
The dates listed in the tables below are for the Spring 2026 offering of this course.

Module 1: Overview

Module 1 provides a broad introduction to the emerging field of Human–AI Interaction. We will begin by discussing why it is important to study Human–AI Interaction today and the motivation behind creating this course. Next, we will provide a practical lecture on machine learning, with the goal of teaching the basic concepts necessary for students from diverse backgrounds to understand the course content. After that, we will present a lecture on the history of AI and HCI; understanding this history allows us to better comprehend today’s technology and its future trends. We will then introduce the basic principles and guidelines for designing successful, user-facing Human–AI Interaction systems. AI alignment—which is important for designing and evaluating today’s generative AI systems—will be addressed in a separate lecture. Students will then discuss and apply their understanding of these principles to real-world cases. The final lecture in this module focuses on the design process, as a good design process can help prevent failures and limitations in Human–AI systems.

Week 1	Lecture #1	Introduction to Human-AI Interactions
	Lecture #2	Practical Machine Learning	Assignment 1 posted
Week 2	No Class	MLK Day
	Lecture #3	History of AI & HCI Reading 1; Reading 2
Week 3	Lecture #4	Guidelines for Human–AI Interaction Reading 1; Reading 2
	Lecture #5	Designing for Failures: AI Alignment Reading
Week 4	Lecture #6	AI in Social Decision-Making Contexts Reading 1; Reading 2
	Lecture #7	Stakeholder-Driven and Value-Sensitive AI Design Reading	Assignment 1 due (Fri 02/06)

Module 2: Fairness, Accountability, Transparency, and Ethics (FATE)

Module 2 provides a deeper discussion of Fairness, Accountability, Transparency, and Ethics (FATE) in Human–AI systems. We will first discuss fairness: what fairness means in machine learning, how to evaluate an ML model’s performance with respect to fairness criteria, how models may meet or violate those criteria, and how to measure trade-offs among different fairness definitions as well as between fairness and accuracy. In real-world contexts, we will examine what factors shape people’s perceptions of fairness. We will also explore transparency and privacy, and cultivate students’ high-level critical thinking about ethics in AI. Students will complete a hands-on exercise to practice creative speculation about ethics in AI by participating in Black Mirror–style writers’ room activities.

Week 5	Lecture #8	Fairness in Machine Learning Reading 1; Reading 2	Assignment 2 posted
	Lecture #9	Fairness in the Field Reading 1; Reading 2	Project proposal due
Week 6	Lecture #10	Auditing Reading 1; Reading 2
	Lecture #11	Responsible AI in Practice Reading 1; Reading 2
Week 7	Lecture #12	Transparency and Interpretability Reading 1; Reading 2
	Lecture #13	Transparency and Interpretability (cont’d)	Assignment 2 due; Assignment 3 posted
Week 9 (*Week 8 is Spring Break)	Lecture #14	Usable Privacy and Security Reading
	Lecture #15	Black Mirror Writers' Room

Module 3: Capabilities and Limitations of Advanced AI

Since around 2010, we have seen significant advances in AI technologies, including deep learning, convolutional neural networks (CNNs), generative models, reinforcement learning, and large language models. In recent years, these models have achieved major improvements across many benchmarks and application areas. Large language models have demonstrated strong performance on tests designed to assess human capabilities, such as bar exams and the GRE. In this module, we will explain how these models are trained and discuss their capabilities and limitations. From a product designer’s point of view, we will explore how to quickly prototype systems using advanced AI models. We will also discuss topics such as what writing looks like in the age of large language models and explore the concept of AI agents.

Week 10	Lecture #16	A general introduction to deep learning, CNNs, Generative Models and Reinforcement Learning
	Lecture #17	Large Language Models (LLMs) and HCI Reading	Project update due
Week 11	Lecture #18	Prototyping with Advanced AI
	Lecture #19	Writing in the Age of LLMs Reading 1; Reading 2	Assignment 3 due; Assignment 4 posted
Week 12	Lecture #20	Peer Feedback on Projects
	Lecture #21	AI Agents Reading 1; Reading 2

Module 4: AI and Society

The final module explores broader topics such as how AI is impacting our society and how we can shape a positive future. We will focus on areas like AI literacy, and we will host guest lectures on cutting-edge topics in AI for innovation and learning. Finally, students will present their final projects.

Week 13	Lecture #22	AI Literacy Reading
	No Class	Spring Carnival
Week 14	Lecture #23	AI for Innovation and Learning
	Lecture #24	Envisioning Future (Light Mirror Writing room activity II)	Assignment 4 due
Week 15	Lecture #25	Final Presentation
	Lecture #26	Final Presentation