Getting Started with Generative AI in Learning and Development

Summary
Generative AI is transforming how organizations approach learning and development by increasing learner engagement, streamlining content creation, and personalizing training experiences. This blog explores how L&D professionals can responsibly integrate generative AI in learning and development—from defining clear goals and establishing guardrails to building AI literacy and starting with small pilot projects. The future of AI in L&D depends on how effectively we use AI for training and development to support people, not replace them.

Introduction

Learning and development professionals often have mixed views about generative AI. About 59% of learners are already using these tools to deepen understanding and practice skills.

However, AI outputs can also introduce errors, bias, or misleading explanations if left unchecked. This blog post shows L&D practitioners, who design, review, and deliver training, how to get started with generative AI.

Understanding Generative AI 

Generative AI, or a large language model (LLM), is a super-smart pattern recognizer that processes a ton of new media. Then, it creates new content based on its interpretations and learnings.

Foundational LLMs are pre-trained on large datasets and available on the web. Some organizations leverage these LLMS and develop closed generative AI models, fine-tuned on private company data.

Most deployed generative AI doesn’t learn from each user chat. They change when developers update a generative AI model or from lots of user interactions in the field.

How all this happens is a bit of a black box, but with good design and usage, generative AI models provide coherent and appropriate responses, leading to better applications of material learned.

Define L&D Goals and Desired Results

Before introducing any generative AI, define your L&D goals, the acceptance criteria, and who will verify outputs.

Specify the following:

• What you want to achieve (e.g., faster quiz writing, more scenario variety).
• Exactly what the AI will do (e.g., draft quiz items, draft a meeting summary, create scenario variants).
• The approval process and organizational L&D personnel to verify generative AI are ready for regular use in the learning process. How to do so varies by the organization and its culture.

For example, if you’re looking for certification preparation, generative AI could be used to recommend to learners which training to enroll. This outcome creates a more personalized learning experience. Or you’re looking for a one-hour self-paced training course. Have generative AI create a practice simulated scenario that pertains to a quiz question. With the learner’s engagement, they are more prepared to take the quiz.

Follow Guardrails

L&D teams must ensure that learners understand the limitations of AI tools. Encouraging basic AI literacy can help learners recognize when something might be incorrect or misleading. Here are some guardrails to keep in mind:

• Generative AI requires specific instruction.
• Unless you know differently, do not assume previous instructions will persist in the next session.
• Revalidate generative AI usage when vendors release a new version.
• Models produce confident but incorrect or biased answers (hallucinations). Treat AI output as a draft that needs human verification.
• Generative AI amplifies a learner’s or trainer’s conception event when the information is false. Ensure the learner has basic AI literacy to respond.

Ensure a Positive Generative AI Experience

Successful learning with generative AI in learning and development hinges on the organizational learning environment, including buy-in before its use. Foundational support includes:

• Executive and upper management need to lead responsible, ethical, and innovative use of AI in L&D.
• Trainers and L&D teams need to be transparent on how generative AI is embedded in learning.
• The trainer and organizations need to trust the data quality used by the model interacting with the learner.
• Learners and L&D teams need to assure that information gathered by the AI tool meets L&D intentions and goals.
• The organization needs governance and processes to handle compliance with AI regulations and ethical risks in training.

Understanding all these aspects of generative AI as used by L&D requires basic AI literacy.

Become Generative AI Literate Before L&D Use

Leaders, trainers, and learners must have foundational AI literacy. This includes understanding how AI works, recognizing and responding to hallucinations (false or biased information), evaluating results critically, and crafting effective prompts. AI literacy ensures that both leaders and learners can engage responsibly and intelligently with AI tools, keeping human expertise at the center of the process. That way, companies are best prepared to implement generative AI to augment learning and meet training goals.

Start Small

Generative AI, while holding promise, has many demands for support. So, start small when implementing generative AI into the L&D space.

• Select a small group of learners who have some foundational AI literacy when introducing generative AI in their training.
• Choose a specific project or a module to introduce generative AI, such as a question or a customer-related scenario.
• Set specific parameters. For example, instead of having generative AI create content for an entire module, have it provide a brief summary or key points on existing material.
• If learners are already using a generative AI tool to explore a topic to answer common questions about the training topics, provide specific instructions on how people can apply those outputs in the training environment, and when they can use that tool.
• Limit generative AI usage in training to one tool to provide the same type of experience.
• Develop and collect metrics on using generative AI vs. no tool to validate if a tool is needed.
• Plan to iteratively improve and scale up your generative AI usage over time.

A methodical and cautious approach mitigates potential risks when introducing AI into L&D activities. Consider other instances where LLMs have occasionally given bad advice and leaked data. Taking baby steps is a good approach.

Conclusion

Generative AI can transform how L&D teams deliver training. It creates personalized content, generates relevant examples, and helps trainers focus on high-value interactions. But remember, the human trainer must remain in charge.

These AI tools do not understand your specific business context on their own. If you’re training bank tellers on customer service, the AI does not automatically know the specific procedures your institution follows. You need to provide that context. Start small with clearly defined use cases, provide clear instructions, review the outputs before sharing, and most importantly, use AI to complement, not replace, your expertise.

Stay connected with your L&D teams to understand new capabilities as they emerge. By thoughtfully combining AI tools with human expertise, you will deliver more effective, engaging training that meets the actual needs of your organization and learners.

The future of L&D is powered by both innovation and expertise. AI drives efficiency, but it’s great talent that brings learning to life. Find your next L&D expert on TTA Connect.