Course Outline

Course Description

AI tools can now write your essays, generate images from a sentence, and hold conversations that feel disturbingly human. You’ve probably already used them. But do you know how they actually work? Do you know who built them, what data they were trained on, and who benefits when you use them? This course takes AI seriously in three ways: technically, critically, and creatively.

On the technical side, you’ll learn what’s actually happening when a language model generates text or an image model turns a prompt into a picture. You don’t need to be a programmer to understand the basic architecture: training data, weights, attention, and the difference between what these systems do and what they appear to do. We’ll trace the history of AI from its origins in the 1950s, through symbolic AI and neural networks, to the large models that are available today.

On the critical side, you’ll wrestle with questions that don’t have easy answers. Who owns the output of a model trained on millions of people’s work? What does it mean when a hiring algorithm discriminates and no one can explain why? How should governments regulate systems that their own engineers don’t fully understand? How can we use AI in an ethical way?

On the creative side, you’ll learn to use these tools. Prompt engineering is a real skill, and the difference between a naïve prompt and a thoughtful one is enormous. You’ll work with language models and image generation tools, learning what they’re good at, where they fail, and how to push them toward results that are actually yours.

Learning Objectives

1. Explain at a conceptual level how large language models and image generation models work, including the roles of training data, model weights, and attention.
2. Trace the historical development of AI from its origins in the 1950s through symbolic AI, neural networks, and the emergence of large-scale models.
3. Write effective prompts for both language and image generation models, and understand why prompt design matters.
4. Analyze questions of bias, fairness, and accountability in AI systems, drawing on concrete cases rather than abstract principles alone.
5. Evaluate the social and economic implications of AI, including its effects on labor, copyright, creative ownership, and the concentration of power.
6. Engage seriously with multiple perspectives on AI policy and governance, understanding the interests and arguments of different stakeholders.
7. Develop a position on what AI should and shouldn’t be used for, grounded in both technical understanding and ethical reasoning.