Lesson 3 - Using affective computing to adapt and personalise learning

SUMMARY

In the previous lesson, you explored how affective states can be measured, and why doing so requires careful design, interpretation, and ethical awareness. But measuring affect is only the first step. The real question is: what should we do with this information?

This lesson focuses on how affective data can be used to adapt and personalise learning experiences. You will examine how indicators such as engagement, frustration, or flow can inform decisions about task difficulty, feedback, pacing, and support in digital learning systems. Rather than treating affect as an isolated signal, the lesson shows how it becomes part of a broader decision-making process that shapes how learners experience training.

Through examples drawn from educational technologies, serious games, and simulation-based learning, you will see how affect-aware adaptation can enhance learning when applied thoughtfully, and how it can fail when applied without care. The lesson also addresses key risks, such as over-adaptation, loss of learner control, and trust issues, preparing you to reason critically about when and how personalisation is beneficial.

By the end of this lesson, you will be able to connect affect measurement to concrete adaptation strategies, setting the stage for the final lesson, where these ideas are brought together in the context of XR-based learning ecosystems.

About The Author

Dr Enrique Hortal is an Assistant Professor in the Department of Advanced Computing Sciences at Maastricht University. His academic and research work focuses on affective computing, brain–computer interfaces, and machine learning for the analysis of physiological and brain signals. He brings extensive experience in computational intelligence and human-centred AI, combining theoretical foundations with practical applications in emotion recognition and intelligent systems.