Think about the last time you opened Netflix.

Have you noticed that you don’t need to keep using the search bar or scroll endlessly just to find something to watch?  Or have you ever wondered how that platform had already curated shows to recommend, like this newly released anime that matched your past favourites, a tv series aligned with your recent interests, or a trending Korean drama that others like you couldn’t stop watching? And within seconds, you’re barely aware you’ve even clicked play? 

If yes, then Netflix provided you with more than just content. It delivered exactly what you wanted, often before you even realised it.

That’s the power of AI-driven personalisation and it’s quietly changing how we interact with everything from entertainment to e-commerce. In particular, it’s beginning to gain momentum on one of its most pertinent applications: online learning.

Education Meets Personalisation

Traditional education often treats all learners the same. Same pace, same content, and same teaching methods. But just like entertainment preferences, people’s learning needs are deeply personal. Some learners can absorb information quickly, while others need more time. Some like visuals, but others learn better by doing. Some people enjoy challenges, while others require confidence-building steps.

That’s where AI personalised online learning comes in.

Modern learning platforms that feature artificial intelligence can adapt to any learner’s pace, interests, and progress. They can recommend the next lesson, offer targeted courses, and even change the way information is presented based on how a learner learns best.

It’s like Netflix, but instead of recommending your next binge, it guides you to your next learning journey. But, while the results appear to be seamless, the processes that drive this transition are far from simple. So, how does AI personalisation in online learning usually work?

Step #1: Data Collection

Data collection plays a crucial role in providing a personalised training experience to each learner. With an AI-powered learning platform, the system gathers various types of information, including:

• Behavioural Data: refers to the learner’s interactions and activities in the online learning environment. This includes time spent on specific lectures or activities, doing quizzes, and participating in discussions or forums.
• Performance Data: refers to the learner’s actual learning outcomes and achievements. It comprises test scores for assessments, module completion rates, and analysis of student work and projects. 
  
• Demographic Data: covers the learner’s background details, which includes age, gender, location, educational background, and other related experiences. 
  
• Engagement Indicators: represent the learner’s level of participation and interest in the courses and the overall learning process. They include the frequency with which learners check in and use the platform, their engagement in discussions and interactions with other learners, and the comments and feedback they provide on it.

This information is gathered both passively through learner interactions, and actively by questionnaires or self-assessments. 

Step #2: Learner Profiling

Once enough data is collected, AI creates a learner profile, which is a thorough representation of learners that reflects their:

• Learning Style: involves various techniques learners use to understand and retain information. For example, by measuring a student’s engagement indicators, the system can determine whether the learner prefers visual, auditory, or kinesthetic learning modes.
• Knowledge Graph: data that shows what learners know and where their gaps are, allowing them to be aligned with the appropriate curriculum or skill frameworks. 
• Behavioural Traits: data that represents how learners engage with the system and the courses they are enrolled in. AI can detect behavioural patterns, such as cramming and inconsistent login times, to help discover anomalies or trends that may signal a need for intervention.
• Emotional State: a new feature in a more advanced learning platform in which AI technology captures facial expressions to track and monitor how learners interact with learning materials, allowing instructors to make timely interventions and effectively support struggling learners.   

Step #3: Content Recommendation

Based on the learner profile, the system will recommend training content that are ideally suited to the learner’s needs. For example, if a student is struggling with sentence structure in English grammar, the system can identify this weakness and make targeted lessons on parts of speech or subject-verb agreement before focusing more on advanced writing lessons. If the system detects a visual learner, the platform will recommend infographics, videos, or simulations. Ultimately, AI ensures that content is delivered at the right moment, preventing learners from moving ahead too quickly without first understanding prerequisites, and eliminating redundancy, which could lead to boredom or disengagement. 

Step #4: Real-time Feedback and Support

With the recommended content, the AI technology tracks how learners engage with their customised learning materials. When the system detects a learner is struggling, it can offer assistance to help the learner better understand the concept by simplifying content, providing hints, or suggesting remedial materials. 

Instead of waiting for days for an instructor to provide an assessment or guidance, some learning systems provide AI chatbots to assist student queries anytime, guide learners through course materials, and even give immediate feedback to their progress. 

Step #5: Continuous Adaptation

As learners advance through the platform, the system constantly adjusts their profiles and their learning paths to keep training relevant and effective. Based on every click and interaction, the system also adjusts in real time, recalibrating content delivery. Such responsiveness ensures that learners operate within their most optimal learning zone, where they are sufficiently challenged to remain engaged but not so much that they feel frustrated. It also aids in recognising and addressing misunderstandings as they arise, preventing knowledge gaps from growing. Ultimately, this continuous adaptation results in better retention, enhanced learning outcomes, and a more engaging, tailored learning path.

AI personalisation is reshaping e-Learning by making education smarter, more high-grade, and more applicable. For learners, it entails customised learning content, adaptive paths, and a learning pace that is appropriate for their needs, thereby enhancing motivation and retention. For educators and organisations, it exposes significant insights, enhances learning outcomes, and accurately scales learning and training. Therefore, whether you’re creating a course or planning to invest in a learning platform, incorporating AI personalisation is more than just an improvement, it’s a strategic advantage for delivering compelling, future-ready education. 

Are you ready to take the first step towards transforming your e-Learning initiatives with AI-driven personalisation that adapts to every learner’s unique journey? At SSA Innovations, we help fix performance gaps by providing relevant learning solutions tailored-fit to meet the needs of your business. From designing extensive curricula and interactive courses to providing a flexible learning platform to effectively implement your training initiatives, we can assist in transforming your company and enabling your employees to reach their fullest human potential. Get a quote today! Email us at innovations@ssagroup.com.