Glossary
AI comes with its own vocabulary. This glossary breaks down the essential terms every educator should know — no technical jargon, just clear explanations.
Artificial Intelligence (AI)
Technology that enables machines to perform tasks that typically require human intelligence, such as understanding language, recognizing patterns, and making decisions.
Machine Learning (ML)
A subset of AI where systems improve through experience. Instead of being explicitly programmed, ML systems learn from data to make predictions or decisions.
Generative AI
AI systems that can create new content — text, images, code, audio — rather than just analyzing existing content. ChatGPT, Claude, and DALL-E are examples.
Large Language Model (LLM)
An AI model trained on vast amounts of text that can understand and generate human-like language. The technology behind chatbots like ChatGPT and Claude.
Prompt
The input or instruction you give to an AI system. The quality of the prompt directly affects the quality of the output.
Hallucination
When an AI generates information that sounds plausible but is factually incorrect. A critical limitation to be aware of when using AI for educational content.
Natural Language Processing (NLP)
AI technology that enables machines to understand, interpret, and generate human language. Powers tools like automated essay scoring and language translation.
Adaptive Learning
Educational technology that adjusts content, difficulty, and pace based on individual student performance and learning patterns.
Algorithmic Bias
When an AI system produces unfair or discriminatory results due to biased training data or flawed design, potentially perpetuating existing inequalities.
Fine-tuning
The process of taking a pre-trained AI model and training it further on specific data to improve its performance for a particular task or domain.
Clear terminology enables precise communication. Too many AI conversations in education are hampered by misunderstood terms.
Notice that 'algorithmic bias' and 'hallucination' are in this glossary. Understanding AI's failures is as important as understanding its capabilities.
Bookmark this page. You'll come back to it more than you think. And share it with colleagues who are just starting their AI journey.
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