Tag: AI for learning Platforms

Welcome to Part 2 of the AI Glossary for L&D! In Part 1, we laid the foundations by defining what artificial intelligence is and exploring some of the fundamental concepts, such as machine learning and neural networks. In this second installment, we build on those foundations by examining the following key technologies that put AI into action:

• Natural language processing (NLP)
• Large language models (LLMs)
• Generative AI (GenAI)
• Retrieval-augmented generation (RAG) 

If you’re enjoying this exploration of AI, stay tuned for Part 3, where we’ll explore advanced AI technologies and applications, and Part 4, where we’ll take a peek behind the AI curtain at the architecture and algorithms that make everything work

 

 

The Key Technologies Putting AI into Action 

What is natural language processing (NLP)?

Imagine teaching a computer to understand human language, not just as symbols or even individual words, but as meaningful sentences and stories filled with emotions and intentions. This is essentially what NLP is. It’s like giving computers the ability to read between the lines, grasp the nuances of language, and even respond in a way that makes sense to us humans. 

How and where is NLP used? 

NLP is the technology that enables you to have a natural conversation with your smartphone’s voice assistant. You can ask it questions, give it commands, or even just chat about your day, and it will understand what you mean, while taking into account slang, accents, or incomplete sentences.

If you’ve ever used an email spam filter, had your text automatically corrected by your phone, engaged with a chatbot, or used a language translation app, you’ve experienced NLP in action.

How can L&D leverage or apply NLP?

NLP technology could be applied to analyze learner feedback from surveys and comments to identify areas where training programs can be improved. 

 

What are large language models (LLMs)? 

Now imagine a vast library containing every book, article, and website ever written. Large language models (LLMs) are like superpowered librarians who have read and memorized everything in the library. They can understand the patterns and relationships between words, ideas, and even emotions expressed in the text. This enables them to not only answer questions accurately but also write different types of creative text formats and translate languages.

How and where are LLMs used? 

With a remarkable ability to understand and generate human-like language, LLMs are incredibly versatile. LLMs are the driving force behind chatbots, language translation tools, and content-generation platforms. 

If you’ve ever used a chatbot on a website to ask a question and received a helpful response, you’ve likely interacted with an LLM.

How can L&D leverage or apply LLMs? 

LLMs can be used to analyze and summarize content, create personalized learning content, and develop realistic simulations for performance-based training. 

 

What is generative AI (genAI)?

Generative AI takes things a step further. Instead of simply understanding and processing language, it can create something entirely new. Think of it like a musician who can play any instrument and compose entirely original symphonies based on their deep understanding of music theory and different musical styles, or a chef who’s sampled every ingredient and explored every cooking technique and can create new and exciting dishes. GenAI can create original text, images, audio, and more.

How and where is genAI used? 

GenAI is behind tools like ChatGPT for text generation, DALL-E for image creation, and numerous music and code generation platforms.

If you’ve ever used an app to generate art from a text prompt, or seen AI-generated images in your social media feed, you’ve witnessed the power of genAI.

How can L&D leverage or apply genAI? 

GenAI can be used to design engaging eLearning scenarios and simulations, creating immersive learning experiences for employees. (Download a free copy of our GenAI Playbook for more L&D use cases, tips, and inspiration!)

 

What is retrieval-augmented generation (RAG)? 

Finally, imagine combining the vast knowledge of LLMs with the ability to instantly access and retrieve specific information. That’s the power of RAG. It’s a bit like having a detective who uses their knowledge and experience (LLM) to investigate a case, but also relies on databases and records (information retrieval) to gather evidence and verify facts. 

How and where is RAG used?

RAG is used to improve the performance of LLM-powered applications, especially in domains where specific knowledge is crucial. If you’ve ever used a search engine like Google to research a topic and received a concise summary with links to relevant sources, you’ve benefited from RAG technology.

How can L&D leverage or apply RAG?

RAG technology can be used to develop AI-powered learning assistants that can answer employee questions about company policies, procedures, or training materials by pulling information from internal data sources.

 

 

As L&D professionals, we’re no strangers to jargon. Remember when “LIFOW” was just a bunch of letters? Now, “learning in the flow of work” is a cornerstone of our learning experience design (LXD) strategies. But AI has thrown us a curveball. Suddenly, we need to be fluent in a whole new language, complete with its own set of jargon and acronyms. 

So, if you’re unsure what “NLP” and “LLMs” are—and how they differ—or are curious about “Deep Learning” and “Backpropagation,” help is at hand! Our AI Glossary for L&D is your go-to guide for simple, straightforward explanations of the concepts, tools, technologies, and methodologies that fall under the umbrella of “AI.”

To make things even easier, we’ve broken the glossary into four parts. In this first part, we introduce the foundational concepts of AI. Over the coming weeks, we’ll continue adding items to the glossary as we expand on the concepts and dive deeper into the “AI-verse.”

 

The Building Blocks of AI

Let’s begin by laying a solid foundation with the core concepts that underpin the field of AI. In this foundational section, we’ll look at the building blocks of AI, specifically:

• AI
• Machine Learning
• Neural Networks

For each glossary term, you can find a high-level description as well as examples of its common uses and how and where it can be leveraged by L&D teams.

 

What is AI?

AI is the umbrella term for a wide range of technologies and approaches that are designed to mimic human reasoning and problem-solving abilities.

How and where is AI used? 

AI is all around us, powering applications like social media, facial recognition, recommendation systems, and chatbots. 

How can L&D leverage and apply AI? 

Here are two ways L&D can leverage AI:

• To enhance the learner experience via AI-powered learning solutions that provide opportunities for personalized and adaptive learning. 
• To enhance learning design practices and processes. Learning experience designers can leverage AI throughout their learning solution design workflow. From needs analysis to design, development to implementation and evaluation, there are dozens of tools and technologies to support this work. 

Where applicable, we’ll share specific examples of these applications.

 

What is machine learning? 

Machine learning is a subset of AI where systems learn from data and improve their performance on a task without being explicitly programmed. Systems are fed vast amounts of data and use it to recognize patterns, make predictions, and take actions using the data, all without human intervention. There are three main categories of machine learning: supervised learning, unsupervised learning, and reinforcement learning. We’ll explain these advanced concepts in a later addition to the AI Glossary for L&D. 

How and where is machine learning used? 

Machine learning is the engine behind dozens of everyday applications such as fraud detection, image recognition, and recommendation systems. If you’ve ever used streaming platforms like Netflix or Spotify, you’ve experienced machine learning in action. These platforms analyze your viewing and listening habits to identify patterns and make recommendations tailored to your preferences. 

How can L&D leverage and apply machine learning? 

Machine learning can be used to analyze learners’ behavior and create personalized learning pathways. Machine learning might also be used to analyze and predict learner performance, recommend relevant content, and even automate the analysis of learner data.

 

What are neural networks? 

Neural networks are computational models inspired by the human brain. Neural networks used in machine learning recognize patterns in data. Think of them as interconnected networks of artificial neurons that process information and learn from it, much like our own brains do.

How and where are neural networks used? 

Neural networks are the backbone of technologies like image recognition, natural language processing, and speech recognition. 

How can L&D leverage and apply neural networks? 

Neural networks can be configured to power personalized learning experiences, generate adaptive assessments, and provide intelligent feedback.