AI in Learning and Development: A Comprehensive Guide to Revolutionize Employee and Organizational Learning

How organizations are using AI to create superior learning experiences that empower employees to achieve organizational goals.

As artificial intelligence (AI) reshapes the learning and development landscape, it is sparking a wide range of responses. Some organizations are taking full advantage of its capabilities, while others are holding back due to concerns about investment costs, uncertainty around how their data will be used, and the quality of AI output.

Despite these fears, AI is already revolutionizing how employees learn, and how learning is designed and delivered. Organizations that adopt this technology early have a competitive advantage over those that wait. But while AI offers many advantages in the learning and development space, unlocking its full potential requires a thorough understanding of how to implement AI into your organization and where to apply it to gain the most value.

What is AI in Learning and Development?

Acquiring AI for Learning and Development Effectively with the Buy, Borrow, Build Strategy

When considering how to incorporate AI into your organization, focus on the Buy, Borrow, Build Strategy. These approaches help organizations adopt AI solutions that align with their unique needs.

Buy

In this approach, you’re investing in proven AI tools from external providers. These systems are ready to deploy and can instantly streamline automation, accelerate content creation, and generate robust analytical insights. This is a great option for organizations looking for quick implementation with minimal development.

Borrow

This strategy involves leveraging external expertise—through partnerships, consultants, or outsourced talent—to adapt an existing AI tool to your needs. By borrowing the right AI tools or expertise, you can supercharge your automation, content creation, and analysis efforts without a long-term commitment to in-house development.

Build

For more tailored solutions, consider building your AI tools in-house. While more resource-intensive, this approach gives you full control over how AI automates tasks, generates content, and provides real-time analysis.

Use AI For Automation, Content Generation, and Prediction

Now that you’ve given some thought to implementation strategies, your next consideration should be when, where, and how you should use AI to deliver the most value. While AI can be applied to many different applications, large learning models (LLMs) excel at doing three things: automating tasks, generating content, and analyzing large datasets to make predictions.

Using AI to Automate Tasks

Automating basic tasks will not only help save you time but also free up your employees to spend more time developing and improving your content. Here are a few areas where AI offers real value.

Streamlining Administrative Tasks

AI can automate repetitive administrative tasks such as scheduling, tracking attendance, and managing course enrollments, freeing up time for L&D professionals to focus on more strategic activities.

Creating Personalized Learning Paths

AI algorithms can automatically create personalized learning paths for employees based on their skills, job roles, and learning preferences, ensuring that each learner receives the most relevant content.

Automating Assessments and Feedback

AI can automate the creation and grading of assessments, providing instant feedback to learners and identifying areas for improvement.

Using AI to Generate Content

There are several AI tools that can help expedite the content generation process by making it faster, better, and richer. When using these resources, humans should always remain an integral part of the process, evaluating and refining AI outputs and ensuring that AI-generated content is safe, accurate, and relevant.

Developing Adaptive Learning Content

AI can generate adaptive learning content that adjusts to the learner’s progress and performance in real time, ensuring a tailored learning experience.

Curating Content

AI can curate and recommend the most relevant learning materials, helping learners access valuable content quickly.

Enhancing Interactivity

AI can create interactive simulations and other engaging content that enhances the learning experience and keeps learners motivated.

Using AI for Data Analysis and Prediction

AI empowers L&D professionals to make informed decisions, enhance learning outcomes, and proactively address learners’ needs through actionable insights.

Identifying Learners’ Behaviors

AI provides insights into training effectiveness by analyzing trends and patterns in learner data.

Anticipating Future Needs

AI can predict future learning needs and outcomes based on historical data. This enables organizations to proactively address skills gaps and plan for future training requirements.

Measuring Content Effectiveness

Organizations use AI to evaluate learner engagement and retention, enabling data-driven improvements.

Examples of AI in Learning and Development

We have put together four examples of AI in learning and development to help illustrate some of the capabilities of AI and the benefits they can help teams achieve.

AI-Driven Content Creation

AI tools can significantly enhance the speed and quality of learning content creation. However, achieving this requires a program specifically trained to understand what constitutes effective learning content. Publicly available large language models (LLMs) might fall short, producing low-quality or inaccurate content because they primarily function as extensive databases with access to vast publicly available information.

Content creation and management tools, like our own Learning Content AIQ, helps you create higher quality learning content with a centralized environment to structure your organization’s knowledge. It is designed to excel in creating high-quality learning materials. It has been pre-trained by experts with decades of experience in the field and is engineered to assist learning creators. The program offers AI-automated processes for content authoring and instructional design, which can reduce development time by 50% or more. Users can quickly generate course outlines based on proven best practices, create assessments with built-in instructional design principles, and cut the development time for video and podcast scripts by 70%.

AI Avatars and Digital Assistants

AI tools such as Inworld enable developers to create chatbot avatars that possess unique personalities, knowledge, and cognition. These avatars are tied to LLMs and can use that information to answer questions and engage learners through realistic conversations.

Developers define each avatar’s knowledge base by adding specific information to the knowledge field, or by linking it to knowledge and cognition documents. They can also import personalities and backstories for their avatars, outline their moods, and customize their responses. Avatars can even role-play specific scenarios (imagine chatting with a simulated employee who has recently experienced a difficult performance conversation). This provides a valuable support system that enables learners to engage with your content on a deeper and more meaningful level.

Translation

AI makes it possible to translate learning content quickly and easily, even when it contains several different modalities. For instance, Google Translate enables you to translate entire pages of content. This includes everything on the page, such as menu options, accordions, pop-up windows, closed captioning, and video subtitles. AI will even translate user posts and questions.

Many AI tools include some form of translation. While the translations provided by these free tools may not be perfect—they tend to be roughly 80% accurate—they provide organizations with a fast, easy, and cost-effective way to translate content into dozens of different languages. This can be a game changer for organizations that normally lack the resources to reach their entire audience at scale.

This, however, is not the only option. Organizations can increase the accuracy of translations through trainable AI tools. Programs such as Smartcat use translation memories and glossaries to become more accurate over time. Users can input phrases and words into these memories to teach the program how to improve, creating a Human+AI synthesis in which AI provides the translations, humans review them, and the program learns from this feedback.

Personalized Learning

Personalized learning focuses on tailoring learning experiences to meet the individual learner’s needs, preferences, and goals. AI tools and technologies make it possible to personalize learning at scale and to drill down to the individual learner’s requirements. AI can help analyze large amounts of data, adapt content, and provide real-time support and insights to learners. Personalized learning paths and adaptive learning content are two of the use cases discussed earlier in this guide.

Tutoring is another great use case for personalized learning. Learners can engage with an online tutor powered by a tool like Mobile Coach that provides personalized instruction and feedback. Let’s say the area of focus is customer service. The online tutor will simulate the behavior of a human tutor and help the learner identify areas for improvement. If, for example, the learner has identified empathy as a key area for improvement, the tutor will then offer personalized learning that demonstrates how to apply empathy while servicing clients. This includes answering questions and providing feedback and support pulled from the content on which it has been trained.

When developing an AI tutor, you will need one or more humans to create a solid database for the AI system to pull from and to orchestrate the interactions between the learner and the tutor.

The Role of Learning in Skilling, Upskilling, and Reskilling

According to the Organisation for Economic Co-operation and Development (OECD), AI and machine learning will transform 1.1 billion jobs over the next decade. Organizations will rely on the continuous skilling, upskilling, and reskilling of their employees to keep up with the pace of change and thrive in this new environment. AI can quickly identify gaps in the current workforce and even predict emerging skill requirements based on industry trends. Once these gaps have been identified, L&D professionals can then use AI to automate the creation and delivery of new training programs tailored specifically to these needs.

The use of AI in this area offers broad advantages for the individual learner as well, through personalized learning paths, adaptive learning, and real-time feedback that provide learners with learning programs customized to their specific roles, skill levels, learning preferences, and learning goals. Even better, AI can also help employees advance in their careers by suggesting potential roles that align with their interests and abilities and by facilitating mentorship relationships with other employees based on their career aspirations and learning needs.

3 AI Technologies to Save Time in Learning and Development

ChatGPT, Copilot, Gemini and DeepSeek are the most popular AI platforms, helping people conduct research, transcribe, summarize, and analyze meeting notes and audio, write and ideate, and much more. Let’s explore three additional AI technologies that are tailored to enhance efficiency for learning and development professionals.

#1 Colossyan for Generating Video

AI video generation has advanced dramatically in the past year, evolving from clunky, basic outputs to lifelike, seamless videos—like those on platforms such as Sora. Tools like Colossyan empower teams to create engaging, realistic videos for applications like sales simulations, complete with customizable avatars, interactive features, and predesigned templates.

#2 ElevenLabs for Creating Audio and Voice-Over

AI is transforming voice-over narration with tools like ElevenLabs, which offer text-to-speech capabilities using a global library of digital voices, voice cloning, and translation features that maintain speaker delivery across 29 languages. This is ideal for creating engaging, accessible L&D materials for diverse audiences.

Listen to this quick example we created for our podcast.

#3 Quizgecko for Creating Assessments

AI tools like Quizgecko streamline learner assessments by generating customizable quizzes from text, web pages, or videos, offering various question formats, LMS integration, analytics, and the option to enhance quizzes with AI-generated imagery for a more engaging experience.

Implementing AI in Your Organization

Now that you understand what AI can bring to learning and development, it is time to apply these ideas to your organization. To get started with AI:

Resources for AI in Learning and Development

Human+AI Quick Start Guide for L&D

AI is a game changer, but its true power lies in how you use it. The Human+AI Quick Start Guide is your resource for maximizing AI’s full potential, from identifying key problems to mastering the art of prompt engineering for optimal results.

AI for Everyone: Breaking Down the Jargon and Prompt Engineering Tips for Generative AI

How can you maximize the value of AI? Learn more about how to get started with AI, including strategies for overcoming common challenges, power user tips for prompt engineering, and some surprising use cases from AI for Everyone course experts Donna Clendaniel and Theodora Michaelidou.

Using ChatGPT to Create Professional PowerPoint Presentations

Follow Michael Thiel as he walks through the process and demonstrates the pros and cons of using ChatGPT to help create your next slide deck.

About the Authors

Theodora Michaelidou

Theodora is an Innovation Learning Consultant based in Cyprus with global multifaceted experience in the Learning & Development space. She has served as an Instructional Designer, a Training Manager, a University Lecturer, and a Schoolteacher. She is a Fulbright scholar with a bachelor’s degree in Elementary Education from the University of Cyprus and an MSc in Instructional Systems Technology from Indiana University Bloomington. Theodora is passionate about turning innovative ideas into tangible outcomes that address business challenges and meet the client’s learning needs. She has a strong focus on designing human-centric learning experiences that are harmonious and relevant for the learner.

Matt Donovan

Chief Learning & Innovation Officer

Early in life, I found that I had a natural curiosity that not only led to a passion for learning and sharing with others, but it also got me into trouble. Although not a bad kid, I often found overly structured classrooms a challenge. I could be a bit disruptive as I would explore the content and activities in a manner that made sense to me. I found that classes and teachers that nurtured a personalized approach really resonated with me, while those that did not were demotivating and affected my relationship with the content. Too often, the conversation would come to a head where the teacher would ask, “Why can’t you learn it this way?” I would push back with, “Why can’t you teach it in a variety of ways?” The only path for success was when I would deconstruct and reconstruct the lessons in a meaningful way for myself. I would say that this early experience has shaped my career. I have been blessed with a range of opportunities to work with innovative organizations that advocate for the learner, endeavor to deliver relevance, and look to bend technology to further these goals. For example, while working at Unext.com, I had the opportunity to experience over 3,000 hours of “learnability” testing on my blended learning designs. I could see for my own eyes how learners would react to my designs and how they made meaning of it. Learners asked two common questions: Is it relevant to me? Is it authentic? Through observations of and conversations with learners, I began to sharpen my skills and designed for inclusion and relevance rather than control. This lesson has served me well. In our industry, we have become overly focused on the volume and arrangement of content, instead of its value. Not surprising—content is static and easier to define. Value (relevance), on the other hand, is fluid and much harder to describe. The real insight is that you can’t really design relevance; you can only design the environment or systems that promote it. Relevance ultimately is in the eye of the learner—not the designer. So, this is why, when asked for an elevator pitch, I share my passion of being an advocate for the learner and a warrior for relevance.