Today\u2019s approach to workplace learning was designed for a different era. The traditional model for learning (even e-learning) reflects the constraints of the age it was built in. At the time, developing learning programs required costly and time-intensive content curation, fixed delivery channels, and no way to know exactly what an individual needed to learn at any given moment for next-level growth.<\/p>\n\n\n\n
But now, AI is in the picture.<\/p>\n\n\n\n
AI removes most of those barriers, which means the learning model itself is due for a rethink. Learning technology can simply do more now than it could before. There are different constraints to work to, and greater demands for speed and personalization. Learning trends are evolving fast, and it\u2019s becoming increasingly business critical to keep up with them as workforces need to upskill in emerging capabilities like AI.<\/p>\n\n\n\n
You can see it in market projections already: The corporate e-learning market is expected to reach $1 trillion by 2030, according to Global Market Insights<\/a>, and a World Economic Forum report<\/a> finds that 85% of employers are planning to prioritize upskilling by then too.<\/p>\n\n\n\n But this massive spending doesn\u2019t just represent more money invested in that same, traditional learning model. Learning and development is quickly morphing into something more dynamic and AI-driven than that model can sustain. Something industry experts could barely imagine before. And because skill development might look so different than it does today, I want to paint you a picture of what to expect.<\/p>\n\n\n\n I’ve been building and testing AI learning prototypes in Degreed AI Labs long enough now to have some real conviction about the direction of the market. So here’s my take on where things are headed for 2027 and beyond.<\/p>\n\n\n\n Today, you can learn by watching a video, reading an article, or attending a seminar. But often, the content doesn\u2019t necessarily correlate to the format in which you\u2019re learning. It\u2019s based on whatever presentation of the content is available. Usually, the formats of most learning content didn\u2019t involve hands-on experience either, even though that\u2019s critical to developing most skills.<\/p>\n\n\n\n Think about what it means to get better at something like presenting. A course can give you frameworks. But a course can’t give you repetitions. It can’t watch you present your actual slides and tell you your energy dropped on slide four or the message you delivered in the data points on slide seven wasn\u2019t clear. You can\u2019t practice the Q&A with a skeptical audience over and over until you get it right.<\/p>\n\n\n\n When the format of the content matches the task, and the experience is built around what you’re actually trying to do, something changes. The feedback is more specific. The practice gets more useful. Suddenly, experiential learning can be done on demand and in the moment of need for the exact situation you\u2019re trying to prepare for.<\/p>\n\n\n\n We’ve been designing courses for an imaginary average person for decades. First, it was one-size-fits-all\u2014the same course for everyone. But even the tailored or adaptive learning that\u2019s emerged since then takes the form of pre-built paths that were created in advance and based on certain assumptions about the direction or speed of someone\u2019s learning. Better than the first model, but not truly \u201cpersonalized.\u201d<\/p>\n\n\n\n The future looks more like a conversation. One that starts by understanding what you already know, adapts the content when you’re confused, goes deeper when you push back, and checks in on what you need next, rather than continuing on a predetermined track.<\/p>\n\n\n\n I tested this kind of dynamic course experience in AI Labs to see exactly what it might look like in practice. I created an experiment for a new kind of development course that is designed for easy daily learning. The content wasn\u2019t created before I started the conversation. Instead, the AI persona was just ready for me, primed with the context of my upskilling history and proficiency. <\/p>\n\n\n\n In the experiment, the first screen was a selection of topics based on my previous learnings and interests. I selected a fairly technical topic in the demo, one where I had some existing context and skill history in place. The difference was immediate. It started with mid-level content that matched my proficiency level. When I asked a question that took things in a different direction, it followed without friction.<\/p>\n\n\n\nThe Formats for Development Content Will Be More Specialized<\/h2>\n\n\n\n
Personalization Will Mean Dynamic, Responsive Learning<\/h2>\n\n\n\n