Perhaps your tech stack is filled with tools of untold power. Perhaps you have a long wish list of such tools. There seems to be an AI tool for everything now… And yet, somehow the learning solutions we build still take the same shape. The same compliance video, but with a “human-ish” voice over. The same elearning module, but with a talking head this time.
Three years ago, this would have been revolutionary. But 2022 is a distant speck on the rearview mirror, and it’s time we use our limo to go places other than the ol’ grocery store.
This shift in perspective calls for an audit of our processes. And what process can warrant it more than ADDIE?
The ADDIE model, while great for project management, assumes that these stages are sequential for the development of your solution. We conduct analysis first to understand, and then design, and eventually we get to evaluate our training. And because our resources have been limited for so long, this kept us busy. But AI integration allows us to reconceptualize ADDIE beyond sequential stages and allows us to explore practical implementations.
Before adding anything else to that stack, it is worth auditing what is already in it. Auditing your L&D AI tech stack includes a scorecard for exactly that.
Analysis
Typically, when a performance problem is identified, analysis is the stage where you begin to uncover the gaps and environmental constraints. With AI, however, you can:
- Spot gaps from performance data before they happen. Yes, before a department in the company knocks on your door and says “we need training.” AI can anticipate training measures by using your sales reports, customer service metrics, project completion rates, or other data you have on hand. If a new update to the CRM is rolling out, AI can monitor CRM usage: which fields go ignored? Which elements are redundant? Which processes need more guidance?
- Analyze existing course content for gaps or redundancies by feeding in existing training content. For some time now, the AI buzz has pressured L&D departments to produce more, when our focus should always be to produce more intentionally. With AI, you can automate analysis of existing content, and should there be updates to policy or documentation, you can easily identify which materials need to be updated. You can identify which pieces are relevant to which roles, which can help build pathways for design and development.
- Identify barriers to learning by using open-ended feedback. This shouldn’t occur after the delivery of a new training piece. Rather, it becomes part of continuous training process. Training doubles up as diagnostic tool when open feedback can be taken and received at scale. AI can conduct deep review of existing data and surface with suggestions at a scale that would have been impossible otherwise.
Design
Let’s move past writing learning objectives and drafting content. Now, we can build learning experiences that truly understand and respond to each person.
- Assemble a true design partner who has been a part of all of your discussions: with the stakeholders, the SMEs, and the learners. By providing transcripts or meetings and supplementing it with other documentation, your design partner can help you better understand the performance problem and context. You can further connect them to employee surveys and process documentation to create a hyper-focused co-pilot for just one specific course or training.
- Critique your storyboard under the lens of your prospective learners. This is more than just asking AI to adopt your learner’s persona based on a few variables. By feeding in job descriptions, performance data, or transcripts of interviews with learners, you can provide AI deep insight into the needs of your learners, which then allows you to sharpen your course design and build something relevant. We have SME reviews built into our process, but learner reviews may not always be possible. That’s why having this data can be useful to create learner personas and have AI critique our builds. It’s not as good as the real thing, of course, but it does give you a place to start.
- A/B test learning path designs. When your analysis for gaps and redundancies has established learning pathways, an AI agent can not only adopt a persona, but stress test the pathway to see whether it truly serves the intended purpose. For further accuracy, have AI conduct its own review, while the actual learners conduct theirs. You can also always compare the results and fine-tune the model as needed for future use.
Develop
We’re not just talking about generating AI talking heads here; we’re talking about AI-powered simulations, adaptive environments, and immersive experiences that were once complex, costly, and impossible.
- Create pseudo-social learning experiences. Imagine learners practicing tough conversations or navigating tricky customer interactions without the pressure of a real human on the other end. Well, you don’t need to imagine! It’s already happening. By incorporating speech-to-text functionality and AI-generated video APIs, you can simulate realistic conversations, giving learners a safe space to hone their communication skills and build confidence. It’s like having a dedicated practice partner, available 24/7.
- Build adaptive quizzes with dynamic difficulty. If you’re tired of one-size-fits-all assessments, you’re not the only one. The good news is that with AI, you can analyze a learner’s real-time performance and adjust the next questions on the fly. This means no more bored experts breezing through basic questions or frustrated beginners drowning in advanced concepts. Instead, each learner gets a tailored challenge that keeps them engaged and optimizes their learning path.
- Develop intelligent tutors that provide personalized feedback. Forget generic “good job!” feedback. AI can process learner inputs and compare them against expert knowledge, providing detailed, constructive feedback that truly helps them understand where they went right or wrong. It’s like having a personal coach for every learner, guiding them through complex topics and helping them master new skills with precision.
Implement
Implementation isn’t just about launching a course anymore. With AI, it becomes a dynamic, responsive process that personalizes content delivery and offers support in real-time. Imagine a learning journey that actively adapts to each learner’s needs, providing exactly what they need, when they need it.
- Provide AI-powered peer matching for collaborative activities. With streamlined content in personalized paths, the next step would be to re-imagine the journey to incorporate elements of social learning. AI can analyze learner interests, skill sets, or project needs (pulled from your learner profiles or project briefs) to match individuals for collaborative activities. It’s about fostering meaningful connections and maximizing the power of peer learning without the headache of manual matchmaking. It’s also great to help build mentorship opportunities within your organization, creating a culture of learning and collaboration.
- Immediate Q&A support for learner queries. This isn’t a chatbot that gives you three options to choose from and if it can’t answer your question, it gives up. This is a chatbot that will stay with you to work through the issue, until an L&D team member can respond. It leverages a knowledge base of FAQs and troubleshooting guides, emails of support tickets, recordings of support calls, to provide guidance. Think of it like a vibe coding tool, except it’s for support.
Evaluate
Evaluation isn’t just about looking back anymore. With AI, it becomes a forward-looking, predictive exercise.
- Identify key success factors of highly effective learning programs. Now, you can know exactly what makes your best learning programs so good. AI can analyze data from your top-performing courses, instructors, or learner cohorts (using LMS analytics or instructor evaluations) to pinpoint the key elements that drive success. This insight is gold for replicating winning formulas and continuously improving your L&D offerings. But how do you measure effectiveness? This brings us to:
- Track the application of learned skills on the job. Beyond formal metrics, AI can help you track the actual application of learned skills through analyzing observations, peer feedback, or comments from performance review systems. The time savings that AI affords us can be used to go into the environment and record observations (remember, AI can transcribe!). This provides us with rich data on how training is being used in the real work environment, helping us build learning interventions that work.
These aren’t futuristic fantasies; they are capabilities being built and explored right now. The key isn’t to chase every new shiny object, but to understand the function AI can serve within your L&D strategy. It’s about asking: “What problem can AI help us solve? How can it make our learning experiences more meaningful?”
If you’re exploring the answers to these questions, our AI Accelerator program is designed precisely for you. We’ll delve into best practices for leveraging these advanced AI integrations, from connecting APIs to maximizing the power of tools like Storyline and video platforms. It’s an opportunity to move beyond the theoretical and into the practical, equipping you with the knowledge and confidence to truly innovate.
We explore these questions live in our AI masterclasses for L&D professionals every month.
Knowing what AI can do is only half of it; the other half is deciding what it should do. Our Human-AI Delegation Framework for L&D gives you a way to draw that line consistently.
