The world of e-learning has changed dramatically in just a few years. We’ve entered an era where generative artificial intelligence and data-driven systems are reshaping the learning experience. Whether you’re delivering internal training to your team or serving a broader learning community, it’s worth rethinking how you use data in education. In this Journal entry, we explore how the use of vast amounts of data is transforming e-learning—what opportunities it offers, and what challenges it brings with it.
Data in E-Learning
The digitalisation of learning platforms has made an unprecedented volume of data available – ranging from learner interactions and assessments to patterns of engagement with course materials. Today’s modern e-learning systems can track over 200 different data points per learner session, building multi-dimensional profiles of each participant.
This wealth of information opens the door to powerful opportunities – but also brings significant challenges.
What Is Generative AI – And What Does It Have to Do with Data?
Generative artificial intelligence refers to AI systems that can create new content—whether text, images, music, video, or even code. These systems are powered by machine learning models trained on vast amounts of data. Based on what they’ve learned, they can produce new patterns, suggestions, responses, or even entire chunks of educational material. Among the most well-known of these technologies are transformer-based models, such as GPT (Generative Pre-trained Transformer), which are capable of generating context-aware, natural-sounding text.
What makes generative AI especially powerful is its ability to learn patterns from previously seen data and use that knowledge to generate relevant new content. The real breakthrough lies in its capacity to respond to individual user behaviour or input, delivering personalised solutions in real time. In education, this means learning materials, feedback, or even practice exercises can be dynamically tailored to a learner’s current skill level and learning style. This has the potential to fundamentally reshape the learning experience—making it more flexible, adaptive, and effective.
The 21st-Century Learner Profile – Needs to Consider
Today’s learners are often overburdened, impatient, and eager for knowledge. Without opportunities for professional development, they may leave their organisation. We explore the characteristics of the “modern learner profile” and offer tips on how to motivate such learners in your courses.
Read more!What Can Data Do in Education?
In the past, data brought slow, incremental changes to e-learning. You might have looked at who completed a course, what sections they skipped, how many failed a test, or how learners rated the experience – then used those insights to make occasional adjustments to the content or learning process.
Generative AI has changed that. Today, data from learner activity – such as video viewing habits, quiz results, interactions, and the frequency of revisiting materials – feeds into advanced systems that can analyse behaviour in real time.
As a result, modern platforms can now dynamically adapt learning paths, assessments, and even content itself based on how each learner engages. This shift makes education far more responsive, personalised, and effective than traditional methods ever could.
The biggest breakthrough lies in the ability to offer each learner a truly individual learning experience. Instead of one-size-fits-all content, learners receive materials tailored to their prior knowledge, interests, and learning pace.
For example, if one of your team members struggles with a specific module, the system can offer alternative explanations – perhaps through visuals, storytelling, or hands-on examples. Meanwhile, those who perform well can be challenged with more advanced tasks.
This approach not only boosts learner motivation but also significantly improves the overall effectiveness of training programmes.
Traditional tests often operate in a binary way: right or wrong. Generative AI, however, can interpret open-ended responses and go beyond simply marking an answer as correct or incorrect. It can explain why something is wrong and suggest how it could be improved.
This is especially valuable in training programmes where the goal isn’t just factual recall, but the development of critical thinking and deeper understanding.
One of the key advantages of data-driven systems is their ability to make predictions. For instance, they can forecast whether a learner is likely to drop out of a course based on their current behaviour.
This isn’t magic – it’s data. The system monitors signs like declining activity, missed assignments, or reduced participation in forums. When patterns like these emerge, you can step in early with targeted support, guidance, or mentoring – before it’s too late.
Creating learning materials is a significant investment of time and money for many organisations. Generative AI can offer substantial support in this area – it can help produce texts, quizzes, examples, and even complete microlearning modules.
While human oversight remains essential to ensure quality and accuracy, development time can be significantly reduced without compromising professional standards.
This is especially valuable when content needs to be updated frequently, such as in response to regulatory changes, technological advancements, or internal policy updates.
One of the biggest challenges of data-driven education is ensuring data security and learner anonymity. Today, generative AI can work with synthetic data – artificially created datasets that mimic real behavioural patterns without coming from actual individuals. This allows you to develop, test, or simulate learning behaviours without compromising anyone’s personal information.
That said, it’s likely you’ll still need to use real learner data to some extent. In these cases, transparency is key. It’s essential to comply with data protection regulations, obtain proper consent, and clearly explain what data is being collected and how it will be used.
Helping learners understand the purpose and benefits of data use can go a long way in building trust and fostering acceptance of new technologies.
E-learning is increasingly becoming a social experience. Generative AI can map out interactions within learning communities and suggest optimal group formations – for example, identifying who might work well together on a project.
With the help of network analysis, it can even detect where misunderstandings or misinformation are spreading in forums or chat spaces, allowing educators to step in and correct issues early.
This is especially valuable in organisational settings that rely on multi-level, collaboration-based training programmes, where peer interaction plays a central role in the learning process.
What to Keep in Mind When Relying on Data and Generative AI in Education
Generative AI-powered e-learning offers a wealth of opportunities – but it also comes with its own set of challenges. Here are a few key considerations to ensure responsible and effective use:
- Don’t rely solely on technology: personal feedback and the presence of mentors remain crucial to a meaningful learning experience. AI should support – not replace – human interaction.
- Watch out for algorithmic bias: AI can learn from flawed data just as easily as from good examples. Human oversight is essential to catch errors and ensure fairness.
- Ensure equal access: advanced learning experiences shouldn’t be available only to those who are more tech-savvy or have better devices. Inclusivity must be a priority.
By using generative AI thoughtfully, you can enhance learning without compromising quality, fairness, or the human touch that makes education truly impactful.
Need Help Getting Started?
If you’re looking to make your organisational training more effective by leveraging the power of generative AI – but you’re unsure where to begin – get in touch with us. We’ll help you identify the best solutions for your needs.
Even if your resources are limited, it’s worth reaching out. With expert guidance, you can save significant time and costs while still achieving high-quality results.
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