Human-Centered AI

Course Number: AT82.09
Credits: 3
Slides:  Google Drive
Related Link (recommended): Stanford HAI

Course Description

This course explores Human-Centered AI, focusing on designing ethical and effective AI systems. We'll begin with HCI fundamentals, including experimental design and statistical analysis, alongside "the design of everyday things." Subsequent weeks will delve into Large Language Models (LLMs) and their psychological impact, Brain-Computer Interfaces (BCI), and the physical integration of robotics, machines, and haptics. Through projects and discussions, students will learn to foster trust and create beneficial AI, emphasizing practical skills for human-AI collaboration.

Instructor Information

Course Objectives

By the end of this course, students will be able to: 

1. Analyze and apply foundational Human-Computer Interaction (HCI) principles including experimental design, statistical analysis, and principles of intuitive design, to evaluate and improve AI systems.

2. Critically assess the capabilities, limitations, and psychological impacts of Large Language Models (LLMs), considering ethical implications and human interaction patterns.

3. Explain the core concepts and applications of Brain-Computer Interfaces (BCI), discussing their potential to enhance human capabilities and redefine human-AI interaction.

4. Evaluate the human-centered design challenges and opportunities in robotics, machines, and haptic technologies, developing strategies for creating more intuitive and human-friendly physical AI systems.

Recommended Resource(s)

1. Norman, Don. The Design of Everyday Things. Basic Books.

2. Krug, Steve. Don't Make Me Think, Revisited: A Common Sense Approach to Web Usability. 3rd ed., New Riders, 2014.

3. MacKenzie, I. Scott. Human-Computer Interaction: An Empirical Research Perspective. Morgan Kaufmann, 2024.

4. Shneiderman, Ben. Human-Centered AI: Reliable, Safe & Trustworthy Systems. Oxford University Press, 2022.

5. Stanford Human-Centered Artificial Intelligence (HAI) Institute. Online Publications, Research, and Seminars.

Grading

Grading scale is based on normal distribution.

Course Schedule

Course Policies

Attendance
Physical attendance in class is a crucial component of your learning in this course. Students are required to attend a minimum of 70% of all class sessions in person. Failure to meet this attendance requirement will result in an automatic F grade for the course, regardless of performance in other assessments.

• Attendance will be taken at the beginning of each class
• Medical emergencies and university-approved activities must be documented
• Contact the TA before class when possible for any anticipated absence

Late Work 

• All assignments must be submitted through the course submission system by the specified due date and time
• The submission system will automatically close at the deadline
• No extensions will be granted except for documented emergencies
• Late work will not be accepted for credit under any circumstances
• While the submission portal will remain accessible after deadlines, you may continue to submit work for learning purposes and feedback.  No points will be awarded for any submissions after the deadline

Best Practices

• Start assignments early to avoid technical issues
• Submit well before the deadline to avoid last-minute complications
• Check your submission confirmation to ensure successful upload
• Set up calendar reminders for all due dates listed in the course schedule

Academic Integrity

All submitted work must be your own original work. This includes code, analysis, and documentation.

Violations leading to automatic F grade:

• Copying or sharing code/solutions
• Submitting others' work

Acceptable:

• Discussing concepts with classmates
• Seeking help from instructors/TAs

Maintain academic integrity - it's fundamental to your learning and professional development.

Note: This syllabus is subject to change. Any modifications will be announced in class and posted online.