Generative AI has brought unprecedented levels of access to new technology in practically all fields of work—L&D being no exception. Learning designers today have quick and easy access to the powers of artificial intelligence (AI). Over the past year, we have not only witnessed people using AI to improve their workflows, but also a growing number of smart applications using AI to enhance learning experiences.

Today’s learners are experimenting with AI, just as we are. They want immediate, personalized, and relevant answers, ChatGPT style! The time from question to answer has been dramatically shortened, and a “super user” has access to this information on demand. How can we replicate this in learning to help create a “super learner”?

AI can be integrated in our current learning ecosystem to create new learning mechanisms by generating real-time content for learners. Learning designers have access to tools today to create a new Level 4 of interactivity to support learning goals. Before we dive into that, let us understand what makes e-learning interactive, engaging, and effective.

E-Learning interactions are usually divided into four wide categories. Traditionally, these levels can be described as follows:

• Level 1 (Passive Interaction): This level of interactivity requires only passive involvement of the learner. The main characteristic is the transfer of knowledge via text and images. The learner can read or watch new information but does not perform any functions or interact with the training material.

• Level 2 (Limited Interaction): At this level, e-learning elements such as branching menus, click-and-reveal, or drag-and-drop activities help to make the e-learning modules slightly more interactive. The learner can now engage with the training material when performing these simple activities. This level also incorporates the use of audio, video, and basic animations.

• Level 3 (Complex Interaction): Interactivity is further increased at this level, via use of scenarios, narratives, and role-plays. Compared to previous levels, assessments can also be more complex. The learner may be asked to provide complete answers, and not just pick options from readily provided answers.

• Level 4 (Adaptive, Mixed Reality, or Game-Based Interaction): Introducing simulations and gamification elements provides the learner the highest level of interactivity. The learning environment closely resembles real-life situations, allowing the learner to fully practice the newly learned skill. In this level, we see the use of AR/VR elements, gamification products, and branched scenarios all adding up to a multi-dimensional learning experience.

So, where do AI-enhanced learning interactions fit here? They expand the scope of Level 4 interactions. The most appealing thing about using AI-powered interactions is that unlike some other L4 interactions, these don’t have to be resource intensive. With a proper tech stack in place, building AI interactions is much easier and faster than other L4 activities, while simultaneously providing a great new level of personalization and adaptive learning content to our learners.

We can now offer our learners personalized, dynamic, and responsive learning assets without breaking the bank. LLM integrations in a current learning ecosystem allow you to build Level 4 interactions. Let’s consider four examples of such interactions:

• Digital Coach: Using AI, a digital coach feature can be added to the e-learning module. The learner can directly interact by asking this coach questions related to the training topic. With AI, the coach can be programmed as an expert to answer the learner’s questions based on company policies and guidelines. This interaction can also feature as a “help” or “ask me anything” button in the training.

• Role-Play Simulations: A custom role-play interaction can be built between the learner and the AI agent. The traditional role-play simulation can be powered using AI, wherein AI can be programmed to take on any persona (an interviewer, an irate customer, an upset employee). Relevant feedback on the role-play can also be provided by the AI, and finally, the simulation can be addressed as a one-on-one coaching session with the learner.

• Auto Assessments: Open text responses written by users can be assessed and responded to automatically. This allows learning designers to build valid and authentic assessments of tasks that cannot be adequately assessed via, say, a multiple-choice question. A good example would be writing an effective business email. For assessments to be valid, designers can consider providing scoring criteria such as a rubric based on which AI can assess the user’s responses. In fact, AI could also provide detailed and helpful feedback in addition to scoring the answers and could be superior to a human instructor in some ways.

• Infinite Question Generator: Practice interactions for learners can be easily built as the AI can create several different questions based on a certain topic. The topic and level of complexity can be predetermined. We can then use AI to assess the answers and provide feedback. This is ideal for exam preparations where guided practice can significantly improve learner skill and outcomes.

These interactions are just some examples of how AI can truly help us build engaging and effective learning interactions.

These interactions, when used wisely and strategically, can help us answer learners’ questions to the best possible degree. L&D can now offer support, practice, solutions, and infinite coaching in a scalable, self-paced manner. And these can be built using the tools available to us today! As the technology available to us develops, so too will the possibilities in our offerings.

How would you supercharge your learning interactions with AI?

For more insights, join me at TK 2024 for the session: Putting AI to Work: Integrating ChatGPT and Storyline.