AI-Powered Adaptive Learning: How Personalization Really Works

Why AI adaptive learning matters

Gathering employees in a classroom for several days of training takes excessive time and money, and lecture-style training doesn’t provide the personalized learning that 80% of employees agree is important. E-learning offers flexibility to personalize learning experiences with branching scenarios, skip-ahead quizzes, and variables. But AI offers new personalization capabilities that further enhance online learning’s value.

AI adaptive learning is a way to offer personalized training content, sequence, and difficulty for every learner, increasing engagement and improving training effectiveness.

In this post, we’ll discuss how AI adaptive learning works in practice and how you can leverage adaptive systems to build tailored training experiences.

What is AI-powered adaptive learning?

AI adaptive learning is training that uses machine learning algorithms and data analytics to adjust content, difficulty, and pacing in real time, and based on the learner’s performance and preferences. AI-powered adaptive learning makes employee training more relevant, engaging, and effective by providing personalized learning paths and real-time feedback.

Compared to traditional adaptive learning, which adjusts e-learning content and assessments based on predefined rules, AI adaptive learning is more sophisticated and dynamic.

Rather than following static if/then rules, adaptive AI learning serves learners with more personalized content based on their real-time performance and engagement data. For example, a beginner might be redirected to remedial lessons that focus on the areas the learner struggled with most, with lessons sequenced according to which concepts the learner needs to understand first. This ensures employees actually master what they’re learning and helps them reach competency faster.

Adaptive learning vs. traditional e-learning

Traditional e-learning follows the same course, path, and pace for every learner. It’s a one-size-fits-all approach. Adaptive learning delivers content dynamically based on learner performance. The course author might build in branching scenarios, skip-ahead quizzes, or gamification to practice concepts based on how learners respond to the material. In short, a more traditional e-learning path is a straight line, while adaptive learning is a multi-forked journey. E-learning platforms may include adaptive features with or without AI capability.

Adaptive vs. personalized learning

Adaptive learning and personalized learning both aim to tailor learning according to an individual’s needs and preferences. However, adaptive learning relies on systems and skills that make high-level personalization possible.

For example, an AI-adaptive learning system collects learner data and analyzes patterns to identify strengths and weaknesses, and tailors e-learning content accordingly.

Course designers can create personalized learning with or without AI adaptive features, but adaptive systems enable more personalization than non-adaptive systems.

For instance, you can tailor onboarding training by creating separate onboarding courses for customer service agents, sales representatives, and marketers. However, designers can create adaptive courses with responsive learning paths, assessments, and simulations to further personalize learning experiences.

How to build AI-powered adaptive learning experiences

Designing an adaptive learning experience is more intricate than simply investing in adaptive AI features. These six steps will help you build learning experiences that respond and adapt to learners’ needs and preferences.

1. Start with clear competency frameworks

Your AI adaptive learning platform needs a clear competency framework before it can personalize learning paths and help you bridge employee skill gaps. Define the skills and your learners need to master, and further segment by role and job descriptions. For example, customer service agents need strong interpersonal skills, as well as CRM and product knowledge.

Consider the broad outcomes or deliverables required for success in a specific job role. Then, build a hierarchy that breaks these outcomes into smaller sub-skills.

For instance, break down the skill “troubleshoot customer issues” into discrete sub-skills like active listening, root cause analysis, and problem-solving.

You can then create adaptive assessments and simulations designed to measure employees’ proficiency in these skills.

2. Design granular, reusable learning objects

Instead of creating a single 60-minute course, break the content into smaller chunks. This helps you reuse content for different courses and makes it easier for adaptive learning systems to build personalized learning pathways.

For example, you could break a sales rep’s onboarding course into smaller lessons on your company’s sales process, selling techniques, and objection handling.

That way, if your adaptive learning platform identifies an employee’s low proficiency in value-based selling, it can recommend the selling techniques module for the learner.

3. Tag content with rich metadata

AI engines use metadata to find and recommend tailored e-learning content. Use rich metadata to tag course and lesson content. Include information on the skills covered, difficulty, prerequisites, and modality.

For example, an introductory digital marketing course for marketing specialists at your company might have the following metadata:

• Skills covered: SEO fundamentals, keyword research, content marketing
• Difficulty: Intermediate
• Prerequisites: Givvy’s Marketing 101 course
• Modality: Video, text, quizzes, interactive simulations

So, if a marketing specialist struggles with keyword research and prefers video-based learning, the adaptive learning system might recommend video lessons on keyword research and using keyword research tools.

4. Choose the right authoring and delivery platform

To build effective AI-powered adaptive learning experiences, you need a robust course authoring and delivery platform. The platform should include features that support AI adaptive learning systems and promote personalized learning.

For example, features such as AI-friendly content structures (modular course design), various content formats (text, video, audio, quizzes), customizable interactive simulations, and accessibility across multiple devices. Articulate 360 AI includes capabilities to create manual, lightweight adaptation, as well as sophisticated features like AI Tutor, which draws on course content to support learner needs as they progress—without bypassing critical skill building and assessments.

5. Build robust assessments

Adaptive assessments help tailor e-learning experiences by identifying where learners are struggling or excelling and recommending personalized learning resources.

Use pre-assessments to determine a learner’s current skill level and design a personalized learning pathway based on identified skill gaps. For example, Articulate’s skip-ahead-quiz feature allows learners to take a pre-test and, if they pass, move past the material they’ve mastered to a lesson the course author designates.

Incorporate ongoing knowledge checks to keep the learner profile up to date with the latest information on employees’ skills, strengths, weaknesses, and learning preferences.

6. Iterate with learner data

Learner engagement, course completion, and performance data help you understand which learning paths and content pieces actually drive mastery. Think of them as a window into learner engagement and training effectiveness.

If scenario-based videos and interactive simulations on handling difficult customers have high engagement and correlate with higher customer satisfaction (CSAT) ratings for service agents, then you’ll know to double down on those formats.

On the other hand, if text-only lessons with generic customer service scripts have poor engagement and CSAT ratings lower across that learner cohort, then you’ll know to retire those content types.

The benefits of AI-driven adaptive learning

AI adaptive learning benefits learners and training leaders alike. Here are the main advantages of training that responds to learners’ needs in real time:

• Improved learning outcomes: AI-adaptive learning helps learners master concepts faster by tailoring content to their unique strengths, weaknesses, and preferred learning pace. Advanced learners can progress to more challenging topics instead of being limited to beginner material, while learners who are struggling with certain topics receive additional resources to help them master those skills.
• Supports diverse learning preferences: Some learners prefer videos and images for processing information, while others prefer hands-on practice. Adaptive learning supports each of these learning preferences by offering a variety of content types (text, video, images, audio, interactive simulations, and more).
• Efficient use of learning resources: Adaptive learning tools identify who’s struggling and where, so L&D and training leaders can provide targeted support instead of reteaching concepts to everyone. This saves them time and helps learners focus on their weak areas, speeding up time-to-competency.
• Increased learner engagement and motivation: Assessments and simulations that adapt to learners’ choices make training more engaging by getting learners thinking and making decisions instead of simply staring at a screen. Gamification also rewards progress, increasing learner motivation.
• Data-driven insights for leaders: AI data analytics provide real-time insights on quiz and assessment scores, completion rates, and skill mastery trends, helping you see what content types are working and what concepts are giving learners a hard time.

Personalization at scale is within reach

AI adaptive learning offers tools and data to deliver more personalization, on a larger scale. Use them to build training experiences that engage learners and help them build the skills they need to succeed.

AI adapted learning doesn’t have to mean a huge program overhaul. Start small with one adaptive course. Experiment with personalized learning pathways, adaptive assessments, and interactive simulations and learn as you go.

No need to build everything from scratch, either. AI tools can help you design publishable e-learning in half the time. Learn how to leverage them effectively by checking out our blog post on creating online training with AI.