Learning Science in Theory, in Practice, and in Business

Emily Gerson

Thursday, January 8th, 2026

Our brains can usually only take in three pieces of new information at a time before going into cognitive overload. Even emotions can change how people learn or whether they learn at all. They upskill best when they can practice a new skill in context. These are examples of learning science.

There are so many factors that can affect workforce development and how well it works (or doesn’t) inside your organization. But at a moment when one of the biggest business challenges is helping workforces upskill in AI, the importance of consistent and effective learning can’t be overstated.

If employees can learn quickly and effectively, your business can adapt faster. And there’s a science behind making that happen.

Understanding how the science of learning works in theory, in practice, and in business will empower you to use it to your advantage, creating a high-powered engine for workforce transformation.

In Theory: What is Learning Science?

Learning science is the study of how people acquire and apply knowledge. It combines findings from behavioral science, cognitive neuroscience, sociology, and more.

As with any science, there are subsets and offshoots, but let’s focus on what’s most applicable to the workforce. Adult Learning Theory, also called andragogy, highlights six key components:

Relevancy: This is a common topic among learning and development (L&D) professionals. People have to know why they are learning something. They have to be able to see the connecting thread between what they are learning and their work or challenges.
Practicality: In addition to wanting to understand why and how it applies, adults prefer practical, concrete examples and hands-on application of what they are learning. When they see how to execute on learning topics in a real-world situation, they are able to practice accordingly and apply more effectively.
Intrinsic motivation and independence: Often, mandatory trainings can make people less motivated. To learn best, adults need the freedom and independence to guide their own development in ways that matter to them.
Extrinsic goal setting: Despite being highly internally motivated and independent, adults also need to see progress as a result of their investment in learning. Establishing clear goals, milestones, and achievements along the way demonstrates progress and success, and will also help boost motivation.
Referenceable and shareable experiences: Unlike children, adults have a lot of pre-existing knowledge and experiences. That previous knowledge needs to be respected and accounted for, but it also means that adults can learn a lot from one another. Collaboration can be a very effective tool among adult learners.
Ownership and respect: Because adults have their own sets of experiences and knowledge to work off of, they need to feel as if their expertise is both heard and accounted for. It’s important to treat adults as equals to any instructors. Don’t talk down to them, even when they’re in the early stages of learning a new skill.

But understanding theories and putting those theories into practice are very different (we’ll talk more about experiential learning later, too). Knowing how to practically apply these theories in a modern and innovative work environment is what will make the difference between light learning at work and transforming an entire workforce to effectively acquire new skills to take on the future.

In Practice: Apply the Science of Learning to Workforce Transformation

There are many ways to apply learning science principles to workforce development programs, and as technology evolves, more opportunities will arise. Many of the practical applications we’re going to focus on here are innovative approaches that will help employees grow capabilities better than traditional methods.

Personalize learning with AI and contextual skill data.

What we define as “personalization” has changed dramatically in the last few years. Since the internet kickstarted a content and e-learning explosion years ago, people have been able to find information on almost anything they want to learn. Early on, that felt like a type of personalization. It probably even satisfied the relevancy principle of adult learning, to an extent, because it was the best way to date to access tailored content.

Now, so much more is possible, and just being able to find content on a specific topic isn’t enough. When every other channel from social media to video streaming is automatically curated to people’s preferences and past content consumption, employees know they are wasting time sorting through heaps of information just to find one article or podcast that’s applicable to their desired skill and proficiency need.

AI has enabled a new level of access to content that’s tailored based on skill level, role, business goals, and personal focus areas. This ties back to the principle of referenceable experiences; in this scenario, the learner’s prior knowledge and experience is being directly taken into consideration when content is curated and provided to them.

It takes more than a generic LLM to offer this level of learning personalization. The AI that is enabling tailored learning support has to have the right context. It needs accurate skill data, learning content, and learning science principles built in. Otherwise, it’s a “generic-in, generic-out” scenario, which is what many content recommendations already are. For example, content could be recommended based on a specified skill focus, but could be too novice or too advanced for the learner, resulting in an ongoing search for the right content.

This level of tailored learning, combined with other AI capabilities, can also offer unique, on-demand development experiences by making content interactive—think conversations with AI coaches, automatically generated quizzes, and multimedia learning pathways curated in a matter of minutes. This gives employees an engaging and easy way to own their learning journey. They have experiences, not just static content, available at their fingertips.

These innovations double down on adult learning science principles by making relevant content instantly available to employees, then allowing them to easily lead their own development journey with AI-native experiences, all while giving them content that takes their previous knowledge and experience into consideration.

Applied adult learning science principles: Relevancy, Ownership and Respect, Referenceable Experiences

Build upskilling momentum with goals, milestones, and recognition.

Certificates and badges are a straightforward way to acknowledge accomplishment when someone develops a new skill. But progress is a pre-requisite for completion, and you can create opportunities to reward progress as well. Learning science tells us that intrinsic and extrinsic motivation are important. If every goal achieved provides positive reinforcement that boosts learning productivity and engagement, then it makes sense to include more milestones along the way.

Managers are in a unique position to celebrate these small wins. Recognition in meetings or public channels can be as meaningful as any concrete award for adult learners, who prioritize respect but also appreciate acknowledgement. In an episode of Degreed’s Learning Algorithm podcast, How Managers Can Use AI to Develop Their Teams, Casey Adams, Vice President of Solutions Consulting and Enablement at Degreed, recommends spotlighting and celebrating in-progress learning successes to his broader team as a form of recognition.

Adams says it can be as simple as saying to an employee, “Hey, you learned this new thing, show the team what you’re doing,” and giving them the platform to share their new skills. That acknowledgement recognizes modest achievements, creates an opportunity for collaboration, and builds a stronger team learning culture.

Applied adult learning science principles: Extrinsic Goal Setting, Intrinsic motivation and independence

Map skills to roles to guide mobility and transformation.

Empowering your employees to develop their skills means giving them a clear map from Point A to Point B. Whether upskilling is intended to support a large-scale workforce transformation or an individual career move, it helps if your employees know what skills they need at what skill level to reach their goals.

Start with what’s current. Map which skills and corresponding proficiencies are needed for each role within the organization. This sets clear expectations for employees, and allows leaders to easily suss out skill gaps.

From there, you can set clearer goals for future skill development and identify which skills are needed at which level of proficiency. For example, to move from a contributor to a manager level, an employee might need to move from a level 3 in leadership to a level 5. Alternatively, all employees may need to go up one skill proficiency level in AI by the end of the year to keep pace with change and organizational transformation objectives. Putting concrete numbers to these goals help bring the relevancy and practicality principles to life for your employees.

Though this concept isn’t new, it is suddenly readily achievable and scalable. New AI technology ensures this is not a manual mapping and linking process. It’s automatic.

Once this information is outlined and available to leaders, managers, and individual employees, it becomes easier for leaders to make data-driven talent decisions and for employees to meet expectations and even take on stretch assignments and responsibilities. Suddenly, employees are empowered to own their own journey, again because they actually have the map to get where they want to go, which links to the intrinsic motivation and independence principle of learning science.

Applied adult learning science principles: Relevance, Practicality, Intrinsic Motivation and Independence

Use AI and experiential learning to build real capability.

It’s time to move beyond passively consuming learning content. Can you learn from articles, videos, and podcasts? Of course. But, to use an analogy from Degreed Founder and CEO, David Blake, reading about running doesn’t make you a skilled marathon runner.

As people, we need practice to learn. We need real experience. We need trial and error. The more we do something, the more confident we become. Our How the Workforce Learns Gen AI report found that the most confident Gen AI users were:

Nearly twice as likely to use Gen AI daily
4x more likely to apply it to real problems
32% more likely to learn on the job
77x more likely to engage with and become proficient using Gen AI

Essentially, the most confident users were the ones actually using it, not just passively consuming learning content on what Gen AI is and how to use it. This is an example of experiential learning in action. Those who are taking an active step in the process of developing a skill are becoming more competent at applying it in their day-to-day lives. This is the transformative difference between understanding AI and using AI. Knowing this, L&D teams can spend time weaving more of those experiences into workplace upskilling.

Traditionally, experiential learning had to happen outside of the e-learning workflow. Often, it required time from a manager or L&D employee to create, administer, supervise, and offer feedback. If it took the form of a quiz, someone had to spend time creating that quiz. If it was a practice pitch or conversation, someone else had to take time to play along, respond, and react, then provide feedback.

Instead, employees can now use an AI agent to:

Create one-on-one coaching moments
Practice for presentations
Role play important conversations
Assess learner knowledge

This agent would be as effective or more than a human coach because it would have access to that person’s skill level, their background, their goals, and foundational organizational knowledge. Suddenly, e-learning can go from passive content consumption to an interactive learning experience.

Experiential learning delivered through AI gives employees the chance to apply learning in a low-risk environment, prepare for the high-impact moments, reflect, and get instant feedback on their work and progress. This directly leverages the learning science principles of practicality and intrinsic motivation. Teams can see the clear applicability of this skill in their daily work, leading to a higher level of internal drive to improve, while the on-demand aspect gives them desired independence in the learning process.

This strategy has an added business productivity bonus: If employees are allowed to practice and apply learnings in real-world scenarios, they might also be completing real work while they do it. Combining learning with work output is a win-win.

Applied adult learning science principles: Practicality, Intrinsic Motivation and Independence

In Business: Use Learning Science to Create a Competitive Advantage.

The half-life of workforce skills, or the time it takes for a learned skill to become outdated, is about four years, whereas it used to be 10, according to Forbes. AI skills have an even shorter shelf-life, at about two years.

Your business runs on those skills. Learning helps employees keep them up to date, and using learning science to optimize learning ensures they can do that as quickly and effectively as possible.

In other words, learning and development is critical because it’s the backbone of any forward-looking business’ goal of adaptability and long-term success. If your employees can acquire essential, emerging skills and capabilities as they emerge, they can lead in those areas. And if they can’t (or don’t) learn the new skills shaping the market, your business could rapidly fall behind.

It’s not just a question of being prepared for AI. AI happens to be the most pressing area for upskilling currently, but if you embed learning into the way teams work in your organization, it will help them be ready for and adapt to whatever comes next. Employees today need to be lifelong learners to keep up with continual change, and learning science can empower them with the right tools, habits, and opportunities for growth and innovation.

Learning science applied at scale helps make development more effective, impactful, and long-lasting. As a result, businesses can:

Accelerate upskilling
Make data-driven decisions about talent and development
Lead in moments of key opportunity (like AI transformation)
Build a resilient and adaptable workforce

Using the Science of Learning and Cognitive Neuroscience to Your Advantage.

Ultimately, if your workforce can learn and develop faster, your business will be set up to thrive, no matter what the next big change or challenge is. An essential ingredient for success is understanding the principles of learning science and how they can accelerate upskilling. If your people are enabled to adapt and motivated to do so, they’ll be ready for anything.