Christine Saeedi-Givi

PhD thesis: Personalised Learning using advanced technologies

Research interests:

1. Human-Computer Interaction
2. Affective Computing
3. Eye Tracking
4. Cognitive Load Theory

This research explores how AI integrated XR technologies can enable personalized learning in training environments. Grounded in Cognitive Load Theory, my PhD aims to design adaptive XR systems that manage intrinsic, extraneous, and germane cognitive loads to improve learning efficiency. By integrating eye-tracking and other relevant metrics, a unified cognitive load index will guide real-time adaptations. The research addresses critical questions about what, when, and how personalization should occur in learning, as well as which user characteristics and adaptive technologies are most effective. Low-cost, scalable XR solutions, particularly those using webcam-based ocular data and non-immersive features, will be explored for broader application.

Who or what inspired you to pursue your research interests?

Research shows that individuals learn more effectively when the learning environment is tailored to their specific needs. However, real-world educational practices still largely follow a one-size-fits-all approach. As a result, personalized learning, such as private tutoring, is often accessible only to those from privileged backgrounds, while others must rely on standardized methods. My goal is to develop low-cost, scalable solutions that make personalized learning accessible to all. By adapting educational strategies to individual needs, I aim to promote equitable education and fair learning opportunities.