The research group aims to develop an integrated, psychometric and technology-based research framework that serves the understanding, diagnosis, and development of learning processes.
Our main research directions include the design of psychometric models for both diagnostic and developmental purposes, with particular attention to modeling permanent and transient learning disorders and disabilities. In the field of personalized cognitive training, we develop parameter-level, CHC-compatible adaptive mechanisms that integrate measurement and development functions — enabling the system to simultaneously perform diagnostic profiling and targeted cognitive enhancement.
A key outcome of our work is the synthetic data generator, which produces detailed, high-resolution datasets suitable for training neural networks, allowing the joint validation of psychometric and machine learning models.
Our research also includes the development of personalized learning environments based on wearable physiological sensors (ECG, EEG, pupillometry) and augmented (AR) and virtual reality (VR) technologies.
Diagnostic measurement system • Mobile EEG (eMotive) • Hololens and other VR/XR devices •
Stanford University
Okosdoboz • Zenitech • Audi • Zeneakadémia • MTA-AVKF Tanulási Környezet munkacsoport