Build Better Curricula and Scale Learning Programs Faster with Artificial Intelligence

AI/ML content tagging — which uses artificial intelligence and machine learning to analyze learning content for skills — can have a surprisingly powerful impact on role-based learning. Here's how.

Learning and development (L&D) leaders are key players in workforce transformation initiatives. When an organization needs to upgrade its workforce, it's up to L&D leaders to connect the right employees with the right learning opportunities to move the transformation forward.

In a previous article , we covered how role-based learning programs grounded in job architectures can efficiently upskill employees and meet transformation targets.

(Need a quick refresher? A job architecture is a systematic way of categorizing jobs in an organization. Role-based learning is an employee development approach where learning paths are organized by role, and employees are connected to learning opportunities based on their current roles and the roles they aspire to.)

L&D leaders could build role-based learning programs manually. But that would be a slog, and workforce transformation is a race against time. The longer it takes to upskill employees, the further a company may fall behind its competitors.

However, L&D leaders don't have to do it manually. Instead, they can leverage artificial intelligence (AI) and machine learning (ML) to streamline and automate much of the process.

At Skillsoft, we're introducing AI/ML content tagging to curate role-based learning paths in our skilling platform. Here's a look at how tagging works — and how it makes role-based learning even more scalable, accurate, and effective.

Skills are the core of any role-based learning program. Each role is associated with a specific set of skills, as defined in the job architecture. Learning content that covers these skill sets is organized in a comprehensive path. When employees need to upskill or reskill to reach a new role, they simply follow the relevant learning path to pick up the skills they're missing.

It's no small feat to curate an entire library of training content into role-based learning paths. You would need subject matter experts to comb through all your content — every course, every article, every video, every book, every exercise. They would need to analyze each asset to determine the skills it covers and the roles to which it would be relevant.

Aside from being rather expensive and incredibly time-consuming, this manual curation is bound to introduce some imprecision. Different experts might have different opinions on how to define certain skills and roles. This could result in learning paths that don't totally align with the organization's needs — like a software developer path that doesn't cover all the right Python content.

AI/ML content tagging can speed up content curation while removing subjective judgment from the equation. First, learning assets pass through a generative AI. The AI analyzes the content and identifies the skills it contains and the role(s) for which the content would be relevant. Then, the AI's conclusions pass through a machine learning (ML) algorithm. The ML compares the AI's analysis to reference standards and the results of previous analyses to ensure the AI is accurately identifying skills and grouping content appropriately.

Today's generative AIs are highly sophisticated programs. They can draw connections between disparate pieces of content with a fairly high degree of accuracy, but they're not perfect. AI's hallucination problem is well-documented, and some experts believe we may never be able to entirely stop AI from occasionally making things up.

We can still enjoy the benefits of AI. We just need to manage the risks. That's where the ML comes in. It acts as a guardrail, double-checking the AI's conclusions to ensure it doesn't run amok. Not only does the ML catch the (rare) outright falsehood, but it also corrects subtler mistakes.

For example, an AI may group a sales training course and a cloud engineering course together because they both talk about the skill of "networking." But the ML would determine that, actually, the content deals with two different kinds of networking: cloud networking on the one hand and the wining-and-dining social networking of sales on the other.

By running content through both a generative AI and an ML algorithm, we can take advantage of the AI's speed and overall accuracy while correcting any errors that may arise.

Role-based learning can drive workforce transformation, but it only works if the right people are connected with the right content. Without automation, L&D admins need to look at every piece of content, determine what it's good for, and then find the right audience for it.

With automation, L&D leaders can speed up curricula creation, scale learning programs, and improve the overall quality of learning paths.

1. Streamlining Curation and Delivering Content to the Right People

A job architecture gives you a role and a collection of skills that go with that role. AI/ML content tagging streamlines the process of turning this information into a useful curriculum. The AI takes care of finding content that covers the necessary skills, and the ML ensures the AI is accurate. Then, content can be grouped into the right learning paths and delivered straight to the employees who need it.

2. Scaling the Program Faster

Building curricula based on the skills contained in your learning content is a powerful way to train employees, but it's also pretty difficult. And it only becomes more complicated the more kinds of content you add and the more roles you target.

AI/ML content tagging makes it easier to scale role-based learning without sacrificing the breadth and depth of the learning opportunities offered. AI/ML content tagging can quickly turn even massive content libraries into curated, cohesive learning paths. And AI/ML can do all of this across different content types, formats, and learning providers.

3. Improving Program Quality

As mentioned earlier, before AI/ML hit the scene, L&D admins had to curate learning paths manually. This meant relying on a lot of subjective judgment, and it can be tough for even the most dedicated subject matter experts to parse so much content without missing a few things. As a result, organizations could end up with learning programs that missed important details or didn't include all the relevant content.

AI/ML, on the other hand, ensures much more relevant and reliable learning paths are created. With the AI and the ML checking each other's work, so to speak, you can be more confident that people are learning all the necessary skills and nothing has been left out.

In today's rapidly evolving workplace landscape, L&D leaders are at the forefront of workforce transformation. To quickly respond to the pressures of both the marketplace and internal organizational mandates, L&D leaders need role-based learning programs that connect the right people with the right training.

The addition of AI/ML content tagging makes role-based learning even more effective. Organizations can rapidly and efficiently curate comprehensive training paths, supercharging their learning programs and ensuring their organizations stay ahead of the curve.

Learn more about how the Skillsoft platform can help propel your workforce transformation strategy.