Module 2

Introduction to Large Language Models

How LLMs work, why they matter, and how to use them

📚 3 Lessons 🛠️ 8 Platforms

🎯 Learning Objective

Define how large language models work — including transformer architecture, tokenization, and context windows — and evaluate leading AI platforms for classroom use, understanding their strengths, limitations, and pedagogical fit.

💡 Why This Matters

Most educators use AI tools without understanding what drives them. Knowing how LLMs actually work — how they predict text, why they hallucinate, and what “context window” means in practice — transforms you from a passive user into an informed practitioner. You'll make better tool choices, set more accurate expectations with students, and identify AI-generated errors before they reach your classroom.

👥 How This Fits Your Role

Instructors

Explain to students why AI makes confident mistakes, which model to use for which task, and how to critically evaluate AI outputs — critical digital literacy for any discipline.

Researchers

Understand the technical foundations well enough to evaluate AI research claims, select appropriate models for academic tasks, and discuss LLM limitations with scholarly precision.

Administrators

Make informed purchasing and policy decisions about AI tools for your institution — understanding the difference between models, pricing tiers, and data privacy implications.

📋 What You'll Explore