AI Coaching

AI Glossary for L&D, Part 2: Key Technologies

October 11, 2024February 26, 2025 by Emma Klosson

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.

Continue expanding your AI vocabulary

Click on the links to view the rest of the AI Glossary for L&D series.

Part 1: Foundational Concepts explores the basics of artificial intelligence including machine learning and neural networks.
Part 3: Advanced Techniques and Applications examines the specialized AI techniques and applications that are pushing the boundaries of what’s possible.
Part 4: Algorithms and Architecture offers a peek behind the AI curtain at the algorithms and architecture that enable AI technologies and applications to perform.

AI Glossary for L&D, Part 1: Foundational Concepts

September 27, 2024February 26, 2025 by Emma Klosson

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.

Ready to dive deeper? Continue exploring the AI Glossary for L&D

Now that you have some of the basic concepts of AI down, why not take a deeper dive into the technologies and applications that power AI.

Click on the links to view the rest of the AI Glossary for L&D series.

Part 2: Key Technologies explores the technologies that put AI into action including natural language processing (NLP), large language models (LLMs), generative AI (GenAI), and retrieval-augmented generation (RAG)
Part 3: Advanced Techniques and Applications examines the specialized AI techniques and applications that are pushing the boundaries of what’s possible.
Part 4: Algorithms and Architecture offers a peek behind the AI curtain at the algorithms and architecture that enable AI technologies and applications to perform.

AI in Action: Your Questions about AI Coaching, Skills Training, and Workflow Enhancements…Answered!

August 19, 2024September 6, 2024 by Tiffany Vojnovski

L&D innovators are making extraordinary strides in adding AI to their learning strategies and solutions, sparking questions about AI coaching, and they’re eager to show their work.

We helped a few of our own client-partners do just that at a recent Training Industry Tech Talk. Our whirlwind tour showcased seven projects that leverage generative AI (genAI) in three different ways:

AI-Driven Learning Experiences: Using genAI to create highly personalized, adaptive learning solutions tailored to each learner

AI Workflows and Research: Leveraging AI to streamline an L&D team’s internal processes to improve productivity, while also conducting research to continually evaluate the accuracy and effectiveness of AI-powered learning experiences (above)

AI Training Programs: Creating training programs about AI that help to equip an enterprise workforce with the skills and knowledge they need to thrive in the rapidly changing age of AI

Below are the questions that came up during this rapid-fire review (now with answers!):

What programs do you use to create AI-driven learning experiences?
Is genAI coaching technology best for individual or group training sessions?
How do you train AI coaches like Hilton’s?
Do you have any experience or use cases in using AI to assist with ensuring we are meeting accessibility guidelines?
How engaged do stakeholders need to be in an AI-powered experience like Hilton’s?
My company has banned ChatGPT for employee use. How prevalent is that stance, and how have you worked around it?
When an AI learning solution is delivered to the customer, are you using a closed AI system?

What programs do you use to create AI-driven learning experiences?

We’re technology-agnostic and are able to adapt our solutions based on the unique needs and organizational contexts of our clients. We’ve successfully integrated both Claude by Anthropic and ChatGPT by OpenAI into our solutions. Using a more varied toolbox helps us recommend the most effective solutions for their organization’s needs and existing infrastructure. We’ll work—and evolve—with the AI infrastructure, tools, and policies already in place.

Is genAI coaching technology best for individual or group training sessions?

First, a quick recap of Hilton’s genAI-powered, immersive Delivering on Our Customer Promise guest service skills coaching experience. It’s created with WebXR, a browser-based virtual reality (VR) technology that can be accessed via headset, computer, tablet, or smartphone.

Learners—hotel team members—land in a digital twin of a Hilton property where they meet a concerned 3D-animated “guest” who expresses an issue with their stay.

Learners must resolve the guest’s issue using Hilton’s five-step problem resolution model, HEART, and speaking their response into their device’s microphone. (Experience a scenario in this video excerpt from the Training Industry Tech Talk.)

On the back end, a large language model (LLM) transcribes the learner’s speech into text and compares the content against a rubric. Learners then receive detailed feedback and a pass/fail “grade” on each step of the HEART model (See Q3 below for details on how we “trained” this LLM.) All feedback is delivered by VIC, Hilton’s knowledgeable, endearing robot emcee and coach.

Delivering on Our Customer Promise makes for great individual practice because it gives learners a safe space to put nuanced conversational skills to the test. With its in-depth analysis of each learner’s responses and very personalized feedback based on what they said, this solution was designed expressly as an individual experience.

As custom content creators, we can also help a client-partner create a group-based immersive genAI coaching experience. For example, one learner’s interactions within the scenario might be screencast to the larger group, with a facilitator encouraging dialogue and reflection on each learner’s experience. We can create materials like a facilitator and/or participant guide to ensure a great discussion every time—with no prep needed!

How do you train AI coaches like Hilton’s?

We’ve already touched on the LLM behind Hilton’s Delivering on Our Customer Promise immersive coaching experience (Q2 above). Here’s how we crafted the prompt that powers VIC, the robot coach and emcee of the experience:

1. Creating a Knowledge Database: We considered the vast stores of knowledge and context an expert brings to a coaching interaction: a thorough knowledge of how to apply the five-step HEART model of problem resolution and coach team members to do the same, along with a wealth of examples of what good, great, and not-so-great look like.

We then added this expert knowledge to a database that helps to increase the context for every prompt and also helps prompts to generate “relevant, accurate, and useful” results. This process, known as retrieval-augmented generation (RAG), extends the LLM’s capabilities in specific domains, such as an organization’s internal knowledge base.”

2. Role and Goal: We then told the LLM who it was and how it should behave. This LLM is a manager of a Hilton hotel, and its goal is to ensure that hotel team members are resolving each guest’s problem by correctly following the HEART model. This step gives the LLM a personality, backstory, and communication style that feels authentic, not mechanical—and contributes to the “story” that unfolds in each immersive scenario. We also fed this Role and Goal information back into the Knowledge Database (above) to provide further context for the prompt.
3. Step-by-Step Instructions: Here, we provided additional context to the LLM by breaking down each step of the HEART model with very specific written descriptions. We then began to feed it with examples of desired responses to help clarify how learners should perform.

This step is essential for an experience focused on nuanced skills like showing empathy: To respond accurately, the LLM needs numerous examples of what “good” sounds like. (As we hone the LLM’s understanding of a good response, we feed new iterations back into the Knowledge Base.)

4. Constraints: To prevent the LLM from acting in unexpected ways, we worked with Hilton SMEs to define nonexamples. That is, responses that are inappropriate—for example, offering a free night’s stay. You guessed it: We feed these back into the Knowledge Base to provide additional context.
5. Pedagogy: Here, we conditioned the LLM to give feedback on the learners’ performance to help them reflect on their successes and opportunities—and correct their missteps during their next attempt. As we refine this part of the prompt, it, too, is fed into the Knowledge Base.
6. Testing: In this vital step, we engage Hilton’s SMEs to create further examples (and nonexamples) of potential HEART model applications and increase the quality of the feedback learners receive. On a continuous basis, SMEs test the scenarios and provide the development team with additional knowledge and context…which, in turn, is fed back into the Knowledge Base for further refinement.

In terms of digital accessibility, do you have any experience or use cases in using AI to assist with ensuring we are meeting accessibility (WCAG 2.2) guidelines?

Yes! Our Accessibility team created a chatbot to use as a source of quick information about WCAG compliance. We “trained” the LLM via a similar process to that described above in Question 3; however, it worked less as a coach and more as an information-retrieval tool. Our team began by adding a Knowledge Base composed of detailed accessibility checklists, documents, and websites containing WCAG guidelines. The chatbot’s Role and Goal was to serve as an expert member of a learning team who had deep knowledge of accessibility. Because its function was to search existing information to provide answers to team members’ questions, it didn’t need to act as a coach or provide feedback on our team’s performance—though it certainly could be trained to do so!

How engaged do stakeholders need to be in an AI-powered experience like Hilton’s, versus a more traditional instructor-led training (ILT) or video instructor–led training (VILT)?

It takes a very collaborative process to create an experience like Hilton’s Delivering on Our Customer Promise. We needed Hilton stakeholders to go through the experience multiple times to help us vet the accuracy of the AI coach’s responses and refine the prompt accordingly. In more traditional modalities, such as ILTs, VILTs, videos, or eLearning modules, stakeholders only need to review milestone deliverables like presentation materials, storyboards, prototypes, and the final build. With AI simulations like these, though, more robust stakeholder involvement is required to ensure accuracy.

My company has banned ChatGPT for employee use. How prevalent is that stance, and how have you worked around it?

Quite prevalent, in fact! Cisco’s 2024 Data Privacy Benchmark Report finds that 27% of companies have banned GenAI applications altogether, at least for the time being. And with so many folks entering sensitive data into these applications—including confidential employee information and intellectual property—it’s not surprising that they’re feeling cautious.

We don’t recommend working “around” a ban! If you’re curious about an AI tool, check it out—on a personal device, with non-work-related data. Meanwhile, we recommend that you ask your organization’s leaders about their security and ethical concerns and what’s at stake. What, if any, measures would need to be in place for them to consider an AI tool? Where could an AI tool help you shave budgets or timelines?

Knowing where your leaders are coming from and sharing your team’s AI aspirations empowers you to play an active role in your organization’s conversation. You’ll need an expert (or two) at the table to help you work through the many considerations and concerns every organization should address before leveraging any AI tool. We’re happy to help guide that conversation and even offer a customizable workshop that can help you and your stakeholders shake out their needs, concerns, and wishlists. (Wondering about this workshop? Check out this video excerpt.)

When an AI learning solution is delivered to the customer, are you using a closed AI system?

Let’s start with a quick level-set on the distinction between open and closed AI systems in the eLearning landscape:

Open AI Systems: These platforms openly share their underlying code and training methodologies. This transparency allows the broader community to contribute improvements, customize the system, or even build entirely new applications upon it.
Closed AI Systems: These systems keep their code and training processes confidential, typically restricting access to a select group. In the eLearning context, this could mean limiting access within an organization to protect proprietary data or maintain control over the learning experience.

All of our AI-powered learning solutions are built upon closed AI systems. Doing so ensures the highest level of security for your data and allows us to tailor the solution precisely to your organization’s unique needs.