Course Content
Course Structure Overview
The course is organized into five modules, each culminating in a dual-track assignment that uses the assisted/unassisted framework. Each module includes: Conceptual material Guided interaction with AI tools One structured assignment with: - Assisted component - Unassisted component - Reflective comparison
Module 1: What Is a Language Model, Really?
This module establishes conceptual grounding. Its goal is not to make learners fluent in AI terminology, but to ensure they understand what language models actually do, what they do not do, and why their outputs can be simultaneously impressive and misleading. This module also introduces the core pedagogical pattern of the course: working with AI tools while remaining epistemically independent of them.
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1. What a Language Model Does
2. Why Explanations Are a Dangerous Comfort
3. Why This Matters for Small Language Models
Module Assignment 1: Explaining a Language Model
4. Post-Quiz Lesson: Exploring How Language Models Attempt the Same Work
Introduction to Small Language Models

At its core, a language model predicts the next token in a sequence based on patterns learned from training data. It does not:

  • Possess beliefs or intentions

  • Understand meaning in the human sense

  • Verify truth claims against reality

Despite this, language models often appear to reason because:

  • Human language encodes reasoning patterns

  • Statistical regularities mirror argumentative structures

  • Confidence and coherence are easy to mistake for understanding

This tension—between appearance and mechanism—is central to all professional work involving language models.

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