AI Fundamentals for Educators: AI, Language Models, Capabilities and Ethics

Part of: How to Design AI Professional Development That Changes Practice, a guide to AI training that lasts beyond the workshop.

This page brings together the four lessons of the AI Foundations module from the Cambridge AI Lunch and Learn series: what AI is, how large language models work, what they can and cannot do, and how to use them responsibly.

Lesson 1, AI Foundations Module

In our first foundational session, we demystified artificial intelligence by exploring what it actually is—and what it isn’t. The key insight? AI doesn’t follow rules like traditional software; it recognizes patterns learned from vast amounts of data, much like how you learned to recognize cats through examples rather than explicit instructions.

We distinguished between different types of AI (Large Language Models, Computer Vision, Predictive AI, and Generative AI) and discovered that AI is already embedded in our daily lives – from email spam filters to smartphone facial recognition. Through practical exercises, participants experienced how AI can adjust its communication style for different audiences and solve multi-constraint problems like planning office pizza orders with dietary restrictions.

The takeaway: AI is a powerful tool for augmentation, not replacement, and like any tool, it requires understanding its capabilities and limitations before we can use it
effectively.

This post is part of the Cambridge AI Lunch & Learn series, a 24-week upskilling programme designed to transform participants from AI curious to AI confident. Through bite-sized
30-minute weekly sessions, we’re building practical AI literacy across Cambridge University Press & Assessment, equipping our team with the skills to work effectively and ethically with artificial intelligence.

Each session follows a proven learn-by-doing approach: 10 minutes of instruction, 10 minutes of demonstration, and 10 minutes of hands-on practice. This isn’t about becoming AI experts but rather it’s about understanding how to augment our work, make better decisions, and navigate the evolving landscape of AI tools with confidence and responsibility.

Lesson 2: Understanding large language models

This session opened the hood on how ChatGPT, Claude and similar tools work, in three stages: training (learning patterns from vast amounts of text), processing (breaking your input into tokens, roughly 0.75 words each, which explains usage limits and costs) and generation (predicting the most likely next word, thousands of times). We covered context windows, the model’s working memory that fills up in long conversations, and the hallucination problem: models generate likely text, not checked facts. Exercises with made-up product and company names showed how plausible an invented answer can look. The lesson: verify specific facts, dates and citations. Lesson 2 slides (PDF).

Lesson 3: Capabilities and limitations

We mapped where AI is strong (pattern-based work such as summarising and drafting, data-heavy tasks, repetitive formatting and variations) and where human judgement is still essential (organisational context, specialist knowledge, emotional intelligence and precise details). The working model is augmentation: AI as a tireless assistant that gets you to a first draft, not a decision-maker. We used a five-step collaboration workflow (Define, Draft, Refine, Validate, Deliver) and named the checkpoints that need a human: high-stakes decisions, accuracy-critical content, sensitive situations and creative strategy. Lesson 3 slides (PDF).

Lesson 4: Ethics and responsible use

The final module session moved from “can we?” to “should we?” across four pillars: bias (models learn and amplify it), privacy (never share personal data, credentials or confidential information), integrity (disclose AI use and respect intellectual property) and accountability (you own AI-assisted output). Participants worked through real dilemmas, such as confidential reports, biased job-advert language and time pressure versus verification, then wrote a personal AI ethics framework: what they will always do, never do, when they will disclose, and how they will check for bias and verify outputs. Lesson 4 slides (PDF).

The full learning pathway is described in Designing a 24-Week AI Training Programme for Professional Development.