When we talk about learning with ChatGPT or other large language models (LLMs), we're really talking about a spectrum of instructional design possibilities. It's not a one-size fits all approach. Instead, we can think of it as distinct tiers, each bearing its unique challenges, risks, and opportunities.

Tier One: Self-Directed Exploration & Learning with AI

The first tier of AI integration - self-directed exploration - opens up a world of knowledge that learners can dip into based on their curiosity, needs, and pace of learning. Unlike traditional search engines that might offer an overwhelming amount of data, AI-powered exploration is like a guided tour through a jungle of information, pointing out key landmarks and helping learners find the most relevant answers.

The beauty of this self-directed approach lies in its inherent flexibility.

Whether a learner is trying to understand the foundations of eLearning, the intricacies of project management, or human centered design concepts, they can steer the conversation in the direction they need. It's the equivalent of having an interactive textbook that not only explains concepts but also engages in a back-and-forth to clear doubts or provide additional context. The learner could ask, "What are the key principles of instructional design?" and follow it up with, "Could you give me an example of how the 'chunking' principle can be applied in eLearning?"

This type of independent exploration can take on different forms based on the learner's comfort with the subject matter. Someone new to a concept can use AI to gain a broad overview, while a more knowledgeable learner can engage in a more nuanced discussion. The potential for follow-up questions, probes, and deep-dives transforms the learning experience from a passive consumption of information to an active learning dialogue.

However, there are some inherent challenges with this.

Just 14% of U.S. adults have tried ChatGPT to learn something new so it may require some guidance. From setting up accounts to being mindful of potential hallucinations, basic AI literacy can help minimize risks and get your learners off on the right foot. As they build confidence and become more comfortable, they can try out plugins to find current information and tailor their prompts to meet their needs.

Tier Two: Architected AI Learning Experience

Moving up to the second tier - architected experiences - we see AI transforming from a responsive tool to an active mentor. By integrating structured guidance and targeted feedback into our prompts, ChatGPT can simulate the one-on-one attention of personal tutoring, making it a powerful tool for L&D professionals and consultants.

With an architected experience, we can ensure the prompt gives ChatGPT the context and information it needs to provide relevant, accurate guidance. But this doesn't mean the learning path is rigid. The AI chatbot can still adjust based on the learner's prior knowledge, learning pace, and immediate needs. For instance, if the learner demonstrates proficiency, the AI could skip the basics and directly dive into more advanced applications or learning.

Chatbots for Learning

This could take the form of a chatbot developed through a platform like Chatbase where you can upload documents or add links to webpages or Poe where you can build the information directly into your prompt and customize.

Epic Prompts to Build Knowledge into ChatGPT

It could also be a prompt for learners to use in ChatGPT with a shared link or prompt they can copy and paste. Epic prompts like the leadership mentor or LTEM guide (shown above) give an easy way to customize the interaction and build in your own knowledge.

Tier Three: Strategic AI Integration

One application at this level is the creation of adaptive learning scenarios. These scenarios dynamically change in response to learner performance, providing a customized learning journey. This adaptivity ensures that learning is neither too easy nor too challenging, making it engaging and efficient.

Another potential application is with reflection and self-assessment. Often, we learn a concept, apply it once or twice, and then move on without deeply reflecting on our understanding and application of the concept. AI can fill this gap by facilitating post-activity reflections, prompting learners to think about what they've learned, how they've applied it, and how it can be relevant to their future tasks.

AI can also be used for authentic practice with feedback, especially when it comes to “soft skills” like communication, negotiation, and active listening. By strategically integrating AI in learning, we can create rich, engaging, and deeply personalized learning experiences. However, this type of AI integration often requires using multiple tools and/or using the API to develop. It’s a larger investment of time and resources, but it can pay off.

Using AI for Learning

As we navigate the multifaceted landscape of AI in L&D, it's clear that AI is not an all-or-nothing affair. From self-driven exploration to strategically integrated experiences, there are different levels and approaches for using AI in learning. As L&D professionals, it's up to us to take this opportunity, to experiment, explore, and create the most effective and engaging learning journeys for our learners.