The Go-To AI Terminology Glossary For L&D Pros
Artificial Intelligence (AI) has entered almost every industry, including Learning and Development (L&D), and, consequently, training programs. In fact, AI is becoming popular in L&D, offering possibilities for personalized learning, content creation, automation, and much more that would have seemed impossible 10 years ago. Whether you’re already exploring AI-powered tools or still figuring out how to use AI as an L&D pro, you must understand its terminology.
Although AI terminology like “neural networks” and “Machine Learning” may sound overwhelming, they’re used daily, especially when choosing between AI software, exploring new platforms, or enhancing your training programs. Therefore, the better you understand the vocabulary, the more confidently you can make decisions, ask the right questions, and communicate with both your team and other experts.
That’s why this glossary is here: to make AI more accessible to L&D professionals. This is your proof that you don’t need to be an expert to adopt AI. You need basic knowledge of key AI terms, especially those that directly impact your role as an L&D professional. With this glossary, everything becomes simpler and clearer, so you can understand the terms next time you see them in a learning context. Let’s explore all about AI.
What’s In This Glossary:
AI Basic Terms That Every L&D Pro Should Know
As we mentioned above, you don’t need to be a tech expert to understand how AI works. You just need the right foundation. Below, we’re going to break down the core terms behind AI in a way that makes sense for L&D professionals. Let’s dive in.
Artificial Intelligence (AI)
Artificial Intelligence refers to computer systems that are designed to perform tasks that typically require human intelligence. For example, understanding language, recognizing patterns, making decisions, or even creating content. In L&D, AI can be found in personalized learning paths or smart content recommendations, to name a few. When your LMS suggests a course based on learner progress, that’s AI in use.
Machine Learning (ML)
Machine Learning (ML) is a part of AI that’s all about systems that can “learn” from data. Instead of being programmed to do a specific task, an ML model learns through examples. Over time, it gets better at spotting patterns and making predictions. In L&D, ML can track how people interact with learning materials and suggest what they should focus on next. It can figure out which training materials help people remember things better or even spot the learners who might need a little extra support. The more data it collects, the smarter it gets.
Natural Language Processing (NLP)
You’ve probably seen the term Natural Language Processing, or NLP, often. This is the part of AI that deals with understanding and working with human language, written or spoken. Thanks to NLP, AI can now read emails, answer questions, translate languages, and even generate responses that sound human. As an L&D pro, you’ll find NLP in AI-powered chatbots in LMSs that answer learner questions, help analyze survey responses, and allow learners to interact with content using voice or text commands.
Large Language Models (LLMs)
Large Language Models (LLMs) are trained on massive amounts of text data, such as books, websites, and forums, so they can understand and generate human-like responses. ChatGPT is one of the most famous examples. These models can write emails, explain topics, create training content, and even simulate human conversations. For L&D professionals, LLMs can help them summarize long texts, create personalized quizzes, or simply brainstorm ideas.
Neural Networks
A neural network is like a brain made of code. Inspired by how our own brains work, neural networks are systems of interconnected “nodes,” like neurons, that process information in layers. They’re great at recognizing patterns, especially in data like text, images, or audio. In learning, neural networks might be behind tools that grade assignments, transcribe voice to text, or even generate summaries of long videos.
Generative AI
Generative AI focuses on creating new content, such as text, images, audio, video, and even code, based on patterns it’s learned. You can use it as a creative help to design course outlines, localize training content, shape courses based on different roles, etc. Generative AI tools can also help scale content creation, so you won’t have to worry if your audience is large. Of course, there’s still a human touch needed, especially for quality, but these tools can save you time.
Common AI Terminology Used In L&D
AI in L&D is already transforming the way professionals design, deliver, and personalize learning experiences. So, knowing how it’s used in L&D will help you understand things better and make smarter decisions for your learners. Let’s break down some of the most practical ways AI is used in L&D and the key terms that come with each one.
Personalized Learning
AI helps you tailor the learning journey to each individual’s pace, preferences, and skill gaps. This includes smart recommendations, where AI-powered learning tools suggest content based on what the learner has already done, their interests, and even their job role. Similarly, it uses adaptive learning paths that adjust in real time based on learner behavior to better support them. Why does it matter? Personalization can boost both engagement and retention.
Chatbots And Virtual Assistants
Some LMSs have a chatbot or virtual assistant that’s available 24/7 to guide learners, answer questions, or even quiz them. AI is behind this. How does it work? The system uses natural language to interact with users, whether it’s text-based or voice-enabled. Next, through “intent recognition,” the AI figures out what a learner really means when they ask something and then performs that specific action. For example, if a learner asks, “Where can I find my assignments?” the system will direct them there in the platform. These tools create a more interactive, engaging learning experience and support learners at all times.
Content Generation
As we’ve already discussed, AI can create quizzes, generate images and videos, and even write course outlines. While it still needs work from humans, it can save you lots of time. Specifically, you can use AI for text generation by giving the tool a prompt. Prompts are like instructions, and how you phrase them determines the quality and relevance of the AI’s response. For example, “Write a 5-question quiz about Ancient Egypt for junior high students” is a good and clear prompt. Any content created by AI, including text, video, voice, or images, is called synthetic content. This is a game changer in L&D because it gives more time to IDs to focus on important tasks like learning outcomes.
Learning Analytics
AI takes large amounts of learning data and turns it into insights you can actually use. Let’s start with predictive analytics. AI tools analyze past learner data to predict things like course completion, likelihood of success, or even future learning needs. Next, we have learner profiling, which allows you to see each learner’s strengths, challenges, preferences, and engagement levels. There’s also data about sentiment, and it’s called sentiment analysis. It uses AI to scan feedback, surveys, or discussion forums and tell you if your audience is feeling positive, negative, or neutral about the content. Lastly, engagement metrics use AI to interpret engagement data like time spent in a module, how deeply learners interact with content, or even patterns of disengagement.
Automation
AI can really make life easier for L&D teams. It helps automate repetitive tasks and make operations more efficient. For instance, through process automation, you can use AI to handle routine tasks, like sorting emails, tagging learning content, or assigning modules based on job roles or assessment results. You can also leverage intelligent tutoring systems (ITS), which are advanced learning platforms that mimic one-on-one tutoring. This means less time spent on manual admin tasks, which, in turn, leads to focusing more on strategy, learner experience, and innovation.
Technical AI Terminology For L&D
Now, let’s see some of the most common technical AI terminology you’ll encounter when working with AI in L&D.
Training Data
AI learns through data, and this is called training data. Training data refers to information fed to an AI system so it can learn to recognize patterns, answer questions, or make predictions. This data could be emails, test scores, video transcripts, learner feedback, quiz results, etc. The more diverse and organized the data, the better the AI becomes at performing its task.
Data Labeling
Data labeling means tagging data so the AI knows what it’s looking at. This is crucial because without the labeling, AI can’t be accurate. In learning environments, labeled data might include tagging learner messages as “positive,” “confused,” or “frustrated,” or emails as “informative” or “announcements.”
Model Training
Once you have labeled data, you can begin training your model. Model training is the process of teaching an AI system how to perform a specific task based on the data it’s given. Over time, AI starts recognizing patterns, like what kind of content helps learners succeed or when someone is likely to drop out of a course.
Inference
If training is how the AI learns, inference is how it uses what it learned. Once your AI model is trained, inference is where it applies that knowledge to your prompts. In L&D, this could mean analyzing a learner’s recent behavior and recommending the next course or detecting confusion in a learner’s feedback to offer support.
Prompt
Speaking of prompts, let’s define them. A prompt is simply the input or instruction you give to an AI model to get a specific response. The better your prompt, the more useful the AI’s result. So, make sure you’re clear in what you’re asking so you can get accurate and satisfactory responses.
Fine-Tuning
While general AI models are trained on data from the internet, fine-tuning lets you change those models using your own data. This helps the AI understand your specific tone, context, or content. So if you’re working with a generic AI tool but want it to sound like you or your brand, you might fine-tune it using your course materials, learner interactions, and company profile.
Tokenization
Tokenization means breaking text into smaller pieces called tokens so the AI can understand and process it. For instance, if you want to input a long text or sentence, you might want to split it into tokens. Why does this matter? Because AI doesn’t read the way we do. It processes patterns in tokens to figure out meaning, intent, and context. The number of tokens also affects cost and response length in some tools, so it’s helpful to know.
Bias In AI
AI can be biased because humans are biased, and AI learns from us. Bias in AI happens when the training data contains false assumptions about certain groups or perspectives. In an L&D context, this could mean a learning recommendation system favoring certain job roles or students, overlooking minorities, or offering content with gender stereotypes.
AI Hallucination
AI hallucination is when the AI gives you an answer that sounds right but is completely made up. This can be especially dangerous in learning content, where accuracy matters. If you ask your AI to create a training module on safety, for example, and it invents fake content, it could cause real harm. The solution? Always review and fact-check AI-generated content before giving it to learners.
Overfitting/Underfitting
These two terms often come up when training AI models, and they are about quality control. Overfitting happens when a model learns the training data too well. It performs great on known data, but not when given something new. Underfitting is the opposite. This happens when the AI hasn’t learned enough, so it performs poorly.
API (Application Programming Interface)
An API lets your learning platform connect with AI tools, such as integrating a chatbot into your LMS or adding real-time language translation into your eLearning videos.
Ethical AI Terminology
When we use AI in L&D, there’s something we can’t ignore, and that’s ethics. Whether you’re choosing an AI tool to recommend courses or exploring generative AI, you must know how to use these tools responsibly. That’s where ethics-related terms are useful. Let’s check them out below.
Explainability
Explainability refers to how clearly an AI system can show or “explain” the steps it took to reach a conclusion. In the L&D world, this could mean understanding why an AI tool recommended a certain training module to a learner or why it assessed someone’s project the way it did. Why does it matter? Learners want transparency, especially if it has to do with promotions, skill assessments, or career growth.
Data Privacy
L&D teams deal with a lot of learner data, such as course completions, feedback, or behavioral patterns. Data privacy refers to the responsible handling, storage, and use of that personal information. With AI tools, data is often used to train or personalize experiences. But it must be done ethically. That means collecting only what you truly need, letting learners know how their data is being used, getting their consent, and storing data securely.
Bias Mitigation
We covered AI biases above, so let’s see how to tackle them. Biases can enter AI models when the data they learn from is full of prejudices or outdated facts. Bias mitigation refers to the efforts made to recognize, reduce, and prevent this from happening. For L&D professionals, this means being mindful of how AI selects or recommends learning content, who it aims to help with upskilling, and whether it uses inclusive language.
Responsible AI
Responsible AI is all about creating and using AI systems that are ethical and fair while focusing on what matters to people. In L&D, this means putting learners’ well-being and growth first, being transparent about how AI makes decisions, reducing bias and misinformation, and keeping privacy a top priority.
Transparency
Transparency is all about being open. It’s not just about whether the system can be explained, but whether you’re actually being clear about how it works. For instance, do your learners know they’re interacting with an AI tool? Are they aware when the recommendations come from AI? Can they choose to opt out or share their thoughts? A transparent AI strategy makes sure no one feels misled.
Model Governance
Model governance means monitoring AI models to make sure they keep performing well and fairly over time. It helps prevent issues like bias or inaccuracies and ensures everything stays compliant with regulations. In L&D, this could mean regularly checking the AI’s recommendations, keeping an eye on how it’s used in different departments, setting up regular check-ins with tech teams or vendors, and making sure any updates are well documented.
Conclusion
As AI continues to change both the way we learn and work, knowing the terms around it helps L&D teams stay informed and able to collaborate with peers across all departments. The more we understand those terms, the easier it is to work with AI across the board. This glossary is a helpful resource, and you can always expand it with the new terms you’ll come across while working with AI in L&D.
