Lesson 4 - XR2Learn: Adaptive XR learning ecosystem

SUMMARY

In the previous lessons, you explored how affective states influence learning, how they can be measured responsibly, and how they can inform adaptation and personalisation decisions. This final lesson brings those ideas together in the context of a real European innovation initiative: XR2Learn.

XR-based learning environments offer powerful opportunities for industrial training, vocational education, and lifelong learning, but they also introduce new challenges. Immersive experiences can increase engagement and realism while at the same time amplifying cognitive load, stress, and variability between learners. XR2Learn addresses these challenges by combining XR technologies, affective computing, and adaptive learning principles within a human-centred and open ecosystem.

In this lesson, you will explore XR2Learn at a conceptual level: its vision and objectives, how it fits within broader frameworks such as Industry 5.0 and skills development, and how affective and behavioural information can support adaptation in immersive learning scenarios. You will also be introduced to key XR2Learn building blocks, including reusable enablers, beacon applications used for validation, and the open resources that enable reuse and extension beyond the project itself.

Rather than focusing on technical implementation details, this lesson aims to help you understand how the concepts learned throughout the course are realised in practice, and how you can apply similar principles in your own educational or industrial contexts. By the end of the lesson, you will see XR2Learn not just as a project, but as an example of how affect-aware, adaptive learning ecosystems can be designed responsibly at scale.

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.