AI for Beginners: Key Terms Every Teacher Should Know
In the faculty lounge, a history teacher furrows her brow at her colleague’s enthusiastic description of how he’s using AI in his science classes. “Wait, what’s a ‘large language model’? And what’s the difference between AI and machine learning?” she asks.
If you’ve found yourself in similar conversations, wondering about the terminology flying around education technology circles, you’re not alone. As artificial intelligence transforms education, understanding its basic vocabulary has become as essential as knowing how to create a lesson plan.
Let’s demystify the key AI terms every educator should know:
Artificial Intelligence (AI) refers to computer systems designed to perform tasks that typically require human intelligence. Think of AI as the umbrella term covering various technologies that enable machines to perceive, learn, and problem-solve. In education, AI manifests in many forms—from personalized learning platforms to automated grading systems.
Machine Learning (ML) is a subset of AI where computers learn patterns from data without being explicitly programmed. Rather than following set instructions, ML systems improve through experience. For example, a reading program that adapts to a student’s performance by analyzing their progress over time is using machine learning.
Deep Learning takes machine learning further by using neural networks with multiple layers (hence “deep”) to process information in ways that mimic the human brain. This technology powers sophisticated image recognition, natural language processing, and speech recognition systems.
Large Language Models (LLMs) like ChatGPT are deep learning systems trained on vast amounts of text. They can generate human-like text, answer questions, and even create content. Many teachers are using LLMs to create worksheets, brainstorm lesson ideas, or help students with research.
Natural Language Processing (NLP) allows computers to understand, interpret, and generate human language. When a student uses a digital assistant to check their writing for grammatical errors, they’re benefiting from NLP.
Generative AI creates new content—including text, images, music, and videos—based on what it’s learned from existing data. Tools like DALL-E for image creation or various AI writing assistants fall into this category.
Prompt Engineering is the skill of crafting effective instructions for AI systems. Learning to write clear, specific prompts can dramatically improve the quality of AI-generated content for your classroom.
Algorithm is a step-by-step procedure or formula for solving a problem. In AI, algorithms determine how systems process information and make decisions. Understanding that algorithms aren’t neutral—they reflect the data they’re trained on—is crucial for teaching digital literacy.
Bias in AI occurs when AI systems reflect or amplify unfair prejudices present in their training data or design. Teaching students to recognize and question potential biases in AI tools is becoming an important part of media literacy.
AI Ethics encompasses the moral principles and guidelines governing AI development and use. For educators, this includes considerations about privacy, fairness, transparency, and appropriate use of AI in educational settings.
Understanding these terms won’t just help you navigate faculty meetings—it will empower you to make informed decisions about incorporating AI into your teaching practice. Rather than viewing AI as either a miraculous solution or an existential threat, you can approach it as what it truly is: a powerful tool that, when used thoughtfully, can enhance education while preserving the irreplaceable human elements of teaching.
As you explore AI in education, remember that you don’t need to become a technical expert overnight. Start with small, purposeful steps, and don’t hesitate to learn alongside your students. After all, modeling lifelong learning might be the most valuable lesson of all.