Mitigating Cybersickness in VR for Educational Purposes: Group Diagnostics and a Fuzzy Model for Technology Recommendations

As virtual reality (VR) becomes increasingly common in education, a significant share of students experience cybersickness during immersive sessions. This paper presents a framework for assessing cybersickness and pre-configuring VR applications for specific educational cohorts before deployment. A two-level experimental methodology with twelve control points was developed to evaluate discomfort across different locomotion and rotation methods. Psychometric analysis confirmed the reliability of the measurement scale and revealed a multidimensional factor structure. A hybrid Mamdani fuzzy model with four linguistic terms was constructed from fourteen discomfort indicators and compared with alternative fuzzy inference approaches and classical machine learning methods; the fuzzy model offered the best balance between interpretability and inferential consistency. Analysis of two student cohorts (IT majors and pedagogy majors) revealed substantial group differences in discomfort perception, with pedagogy students showing markedly higher sensitivity. Based on the model outputs, concrete recommendations were formulated for configuring VR applications to reduce cybersickness, including frame rate stabilisation, limiting jump height, simplifying visual effects, and introducing adaptive onboarding modes. The developed methodology provides a practical diagnostic tool for adapting educational VR software to target user groups.