A friendly guide to AI terms, no jargon decoder required.
The AI world can feel like alphabet soup. Between NLP, ML, GANs, and LMS, it’s enough to make your head spin. But here’s the thing: once you crack the code, these concepts aren’t just understandable, they’re genuinely exciting. Especially for educators, designers, and learning pros looking to future-proof their work.
This glossary breaks down the essentials, no fluff and no hype. Just clear explanations, practical relevance, and a few real-world tie-ins for good measure.
Illustration of person with VR glasses looking at a computer with a chat bot on the screen (Midjourney, 2025).
1. Artificial Intelligence (AI)
The umbrella term. AI refers to computer systems designed to perform tasks that typically require human intelligence like decision-making, learning from data, or recognizing patterns. It’s the tech behind everything from voice assistants to personalized learning paths.
2. Machine Learning (ML)
Machine Learning is a subset of AI that enables systems to improve automatically through experience. Think of it as teaching a system to learn from data so it can make predictions or decisions without being manually programmed for every scenario.
3. Neural Networks
Inspired by the human brain, neural networks are what power many of today’s AI superpowers. They’re made up of layers of interconnected nodes (think digital neurons) that process information and learn patterns in data. These networks are the engine behind tools that recognize images, translate languages, and even write stories.
4. Deep Learning
This is where machine learning gets, well, deeper. Deep Learning uses neural networks (inspired by the human brain) to process massive amounts of data. It’s behind some of AI’s most impressive feats like self-driving cars, facial recognition, and language translation.
5. Reinforcement Learning
Imagine teaching a system the way you’d train a dog, with rewards and feedback. Reinforcement learning allows an AI agent to learn through trial and error, improving its performance based on outcomes over time.
6. Natural Language Processing (NLP)
NLP is the branch of AI that enables computers to understand, interpret, and generate human language. It powers things like chatbots, virtual assistants, translation tools, and sentiment analysis.
7. Generative AI
This is the creative branch of AI. Generative models don’t just analyze data—they make things: text, images, music, code, and more. By learning patterns in existing data, they can generate new content that’s often surprisingly original and human-like.
8. Prompt
A prompt is what you give the AI to get things started. It could be a question, a request, or even just a phrase. The more specific your prompt, the better the result.
Example: “Summarize this article in three bullet points.”
9. Prompt Engineering
This is the behind-the-scenes magic that makes generative AI work well. Prompt engineering is the practice of tweaking your instructions to guide the AI toward the best possible output. It’s part creativity, part strategy.
10. Overfitting
When a model knows the training data a little too well, it may struggle with new, unseen data. Overfitting is like memorizing the answers to a practice test instead of understanding the subject.
11. Underfitting
On the flip side, underfitting happens when a model is too simple to capture the underlying patterns in the data. It performs poorly across the board on both training and new data.
12. Fine-Tuning
Fine-tuning is like customizing a pre-trained model for a specific job. You feed it extra examples so it learns to speak your language—whether that’s a brand’s tone, a niche industry, or a classroom setting.
13. Guardrails
Guardrails are built-in limits that help AI stay in its lane. They’re safety checks that keep responses appropriate, ethical, and on-topic—especially important when AI is used in sensitive areas like education or healthcare.
14. Hallucination
In AI-speak, a hallucination is when the model confidently makes something up. It might sound convincing—but it’s wrong. These slip-ups happen when the model doesn’t have the right info, or misinterprets the prompt.
15. Bias
Bias occurs when a model makes systematic errors due to flawed assumptions or data. It’s not just a technical issue, it’s an ethical one. Biased models can reinforce inequalities and produce unfair outcomes if not properly addressed.
16. Ethics in AI
Ethics in AI is about designing systems that are fair, responsible, and aligned with human values. This includes transparency, accountability, and making sure AI supports, not harms, society.
17. Bias in AI (Yes, again)
This one deserves a spotlight. AI bias can creep in through unbalanced training data or flawed algorithm design, leading to discriminatory outcomes in high-stakes areas like hiring, healthcare, and education.
18. Human-Centered Perspective
This approach puts people at the heart of AI design. It’s not about replacing humans, it’s about using AI to enhance what we do best. In education, it means keeping empathy, connection, and human insight front and center, even as we bring powerful new tools into the mix.
19. Privacy in AI
AI systems often rely on large datasets, some of which include sensitive personal information. Privacy in AI means protecting that data through encryption, user consent, and secure handling practices.
20. Transparency in AI
Also known as explainability, this is about making AI systems understandable. When users can see how decisions are made, it builds trust and allows for proper oversight and accountability.
21. AI Safety
This field focuses on making AI systems reliable, predictable, and aligned with human intentions. It involves stress-testing models, minimizing unintended consequences, and ensuring systems perform safely in real-world scenarios.
22. Validation
Validation is the human-in-the-loop step like fact-checking, reviewing, and moderating what the AI produces. It helps catch hallucinations, correct bias, and ensure the output is useful, accurate, and appropriate.
23. AI Literacy
AI literacy is all about giving people the know-how to use AI safely, ethically, and effectively. It includes understanding how these tools work, what they can (and can’t) do, and how to spot red flags like bias or misinformation. It’s an essential skill in our increasingly digital world.
24. AI for Social Good
When used intentionally, AI can support humanitarian efforts, improve healthcare, combat climate change, and increase access to education. AI for Social Good is about solving big problems, not just automating tasks.
25. Virtual Learning Environments (VLEs)
Think digital classrooms, but smarter. AI-enhanced VLEs offer personalized learning, real-time feedback, and data-driven insights that adapt to each learner’s needs.
26. Sentiment Analysis for Education
This technique uses NLP to detect the emotional tone of student feedback, discussion posts, or course evaluations. It helps educators understand how learners are feeling and where improvements might be needed.
27. Gamification + AI
AI-powered gamification tailors the experience to the learner, offering personalized challenges, adaptive difficulty, and meaningful rewards. It turns engagement into momentum.
28. AI-Powered LMS
A next-gen Learning Management System that doesn’t just track progress, it guides it. With AI, LMS platforms can recommend resources, flag at-risk students, automate feedback, and support instructors more efficiently.
29. AI-Powered Content Creation
Need help generating lessons, quizzes, or multimedia assets? AI can draft, adapt, and personalize content at scale, saving you hours and making learning more relevant to your audience.
30. Social Learning
AI can facilitate and moderate learning communities, connecting learners with similar goals, recommending peer discussion threads, or even identifying helpful contributors in forums.
31. Microlearning
Bite-sized learning, powered by AI. These short, focused bursts of content are ideal for busy learners, and AI can recommend the right “micro” at the right time, based on performance and preferences.
32. Gamification and Badging
Beyond points and leaderboards, AI-driven badging can track skill mastery, recognize achievements, and deliver targeted encouragement, keeping learners motivated through tangible milestones.
Curious how AI can level up your learning design, or just want to geek out over prompts, models, and smart tools? Let’s connect.