It’s Monday, and you’re a leader who wants to learn how to give better feedback. Since your company recently brought on an artificial intelligence chatbot to help you improve your skills as a manager, you open a new window and begin typing. The AI gives you a few general tips—remind the employee what they’re doing well, listen actively, frame challenges as opportunities for growth—and you figure it’s enough to get your week started.
But then suddenly it’s Friday, and closing your laptop for the week you realize you applied none of the tips in any of your check-in or performance conversations. Your full schedule swallowed up every good intention you had of working on your feedback skills, even if you were fully motivated on Monday to try something new. What you learned in isolation stayed between you and the AI.
What happened?
Neuroscience knows why the learning never stuck: because it never happened in the first place. AI’s instantaneous replies seemed to offer a shortcut to learning, but then autopilot took over. Organizations everywhere are experiencing a version of this scenario. And unless they incorporate the science of learning into their rollouts of such AI platforms, they should always expect a low to negligible return on their investment, just as so many companies have already experienced.
Human skills are becoming more valuable in an AI era, which means they will need to be so deeply embedded in employees’ long-term memory that critical skills can easily be deployed under pressure. Here are four brain-friendly steps teams can take to start learning better with AI.
1. Engage with AI
The science of learning begins with paying attention: What are you focusing on? Humans evolved to be easily distractible, so we could stay vigilant to nearby threats. These days, the things that best capture our attention are social, insightful, and meaningful. And yet, most learning programs never make it out of the gates because people are already thinking about lunch. Or they’re thinking about the work they need to get back to, so their mind drifts off.
In the new era of AI, capturing attention means making the learning highly compelling. People should want to work with AI to better understand the given material. An AI that spits out answers certainly may keep people’s attention, because it seems to solve their problems for them. But science knows this is a cheap form of attention. The user isn’t actually paying close attention or engaging with the material—they’ve outsourced their focused thinking to the AI, trusting whatever it supplies.
Instead, learners must be told to prioritize their (human) discernment, rather than passively accepting what AI gives them. Staying attentive within an AI conversation means asking thoughtful questions, challenging the AI’s assumptions, and suggesting new possibilities to deepen one’s understanding of the material.
The upshot here is that using AI well requires active collaboration, including during the learning process, which brings us to the next step in the process: generation.
2. Generate new insights
Decades of research show that learning takes place when we get the chance to play an active role in the learning process, such as when we apply what we’ve learned in a creative way. We need to make something, put the idea into our own words, or share our interpretations of the lesson with a peer.
On a neurological level, the act of generation more deeply encodes the information in our brain, so that it can move from short-term to long-term memory. With generation, the material has a chance to transform from an abstract set of facts into a robust web of knowledge that can connect to other mental maps, which deepens our overall understanding.
One of the most powerful forms of generation is the moment of insight. This is the mind-expanding experience of an “Aha!” moment, where we see the world in a brand-new way. Generation is the act that sparks moments of insight, which has been shown to increase the intrinsic motivation that keeps us asking questions and making new connections.
AI can help us arrive at moments of insight by asking us open-ended questions that get our gears turning. AI can act as the wise coach who leads us to new conclusions and discoveries that we feel ownership over. When we own our new ideas, we pursue them with greater drive and curiosity. This is a highly rewarding experience, and it sets the foundation for the next step: emotions.
3. Make the experience emotional
If we want learning to stick, it can’t be a purely intellectual exercise. Learners must feel something during the learning process—both positive and even slightly negative—to create richer memories, which promote long-term retention.
Pre-AI, this could often take the form of social learning events, since humans strongly encode memories that have a social component. It could also mean learning through play, group activities, and tying the learning to one’s personal life to make it more meaningful.
In the new AI era, it can also mean personalizing the AI agent’s style of coaching and engagement. For instance, if you are working to integrate AI into your decision-making processes, you might personalize the AI agent to match your results-driven focus, framing each new skill as one that provides a strategic advantage over your peers.
To help these insights stick, however, we need to incorporate the fourth and final element: spacing.
4. Give learning space to breathe
Left to its own devices, the brain is a forgetting machine. Sure, you might remember that mean thing a boy in class said to you in second grade, or that time you hit the game-winning home run. But most of what we learn and experience is quickly classified as unimportant, so it’s soon forgotten.
In fact, the Ebbinghaus Forgetting Curve, named for Hermann Ebbinghaus, the nineteenth-century psychologist who discovered the quirk of memory, suggests that humans typically forget about 50% of what they learn within the first hour, and 70% within the first 24 hours.
But there’s a clever way to sidestep the forgetting curve: reinforcement. By spacing out learning and revisiting old lessons shortly thereafter, the brain begins to encode the information more deeply into long-term memory. The very act of recalling the information cements its place within our minds.
In a learning context, that means digesting a batch of info in Week 1, letting it sit for a week, then recalling it in Week 2, Week 3, and so on. The more times learners get to recall the info, the easier it’ll be to store the information as long-term knowledge. AI can help here if we prompt it to check in on prior material and prompt us to recall what we learned in the past. It can become a helpful ally in systematizing our spacing efforts.
Applying the lessons
With these four steps in mind, think about how much more effective your feedback lessons would have been if they followed the science of learning.
Throughout the week, you would have paid attention and deepened your engagement with the material; you would have practiced giving better feedback with the AI (perhaps in a role-play scenario) that sparked insights for better real-world conversations; you would have felt both negative and positive emotions that made the material feel more meaningful; and you would have had a chance to revisit material you’d already learned over time, deepening your understanding and motivation to keep learning.
While AI’s powers may seem to offer an escape from the effort of learning new skills, the science of learning shows how valuable AI can be to help us excel in our roles. Organizations that want to get the most out of their investment and truly transform their culture should take note. Through a science-based approach, AI can help teams not only learn but develop performance-boosting behaviors across the whole organization.