Faculty path

Guided course for teaching and research staff. 8 lessons, about 6 h.

For instructors, TAs and teaching staff. The course moves from how these systems work to what they mean for your teaching, your assessments and your research. Each lesson is self-contained, so you can skip ahead to what's most urgent for you, and the tutor can explain any section in more depth.

By the end, you'll be able to

• Explain to colleagues and students how language models work and where they fail.
• Use AI to prepare teaching materials, with checks that keep them accurate.
• Redesign an assessment so it still measures what you intend.
• Write a clear, fair AI policy for a course.

Unit 1: Foundations

What these systems are and how they fail. This is the ground your teaching decisions will stand on, and what students will ask you about.

• What generative AI actually is: The mental model that makes everything else make sense.
• How large language models work: Tokens, transformers, training and why context windows matter.
• What AI gets wrong: Hallucinations, bias, sycophancy, and how to catch them.

Unit 2: Teaching with AI

Practical use in your own preparation and in class, followed by the harder question of what assessment should look like now.

• Prompting that works: Context, examples, constraints and iteration.
• Teaching with AI: Course design, materials and feedback, for faculty and TAs.
• Assessment in the AI era: Designing assignments where AI use is clear, fair and visible.

Unit 3: Research and policy

Use AI in your research without lowering your standards, and set expectations for your students that you can explain and enforce.

• Research and verification: Use AI to find and read faster without losing rigor.
• Ethics and academic integrity: Privacy, copyright, disclosure and USC policy.

Capstone project: Redesign one assignment and write its AI policy

Take one assignment you teach, test it against current AI tools, redesign it so it measures what you intend, and write the AI policy that goes with it.