Grants and Awards

Immersive Virtual Reality has the potential to revolutionize education by providing student-centered, engaging learning experiences. VR takes various forms in schools, such as desktop virtual reality (DVR), immersive classrooms (IR), and head-mounted display virtual reality (HMD-VR), each with different levels of immersion. While educational use of VR enhances engagement and knowledge retention, it also poses challenges such as cognitive overload and privacy concerns. This research innovatively explores established educational frameworks in VR learning settings, specifically the e-CSAMR framework, combining the SAMR model with an e-collaboration classification, and a Teacher Prototypes typology. The study aims to explore professional development for VR teaching, changes in teaching and learning processes. Using a mixed-methods approach with multiple case studies, the research involves 36 teachers, 36 students, 12 ICT training instructors, and 12 ICT school coordinators. The study will observe actual learning in all three VR modalities- DVR, IR and HMD-VR and interview the participants. Ultimately, this research enhances teaching, learning, assessment and teacher training, in VR-based education, contributing to the e-CSAMR and Teacher Prototypes frameworks.

The rise of large language models (LLMs) sparked the question of how to communicate optimally with them. Since large language models learn through self-supervision and are based on billions of parameters, they possess communication skills and understand interpersonal communication. However, while they are capable of generating responses in communication with users, there is still a challenge in predicting their responses accurately, as these models, though highly advanced, are not always able to handle real-life communication scenarios as efficiently as humans. The challenge lies in how to effectively use the models for communication, especially when interacting with them through interfaces like text-based communication. The research explores this challenge, focusing on soft skills improvement by using an avatar-based task in virtual or augmented reality environments. In the proposed study, the task involves role-playing a job interview with an avatar that is based on a soft skills training model. The participant trains with ChatGPT through this avatar, which plays the role of an interviewer, asking questions related to the job. The avatar is designed to provide feedback on the trainee's performance. A key question in this research is how similar the avatar should be to a human. On one hand, the more the avatar resembles a human, the more realistic and familiar the interaction will feel, increasing the likelihood of the trainee treating the interaction seriously. On the other hand, if the avatar resembles a human too much, the trainee might mistakenly apply interpersonal communication knowledge to the avatar interaction, assuming it follows the same patterns as human interaction. The research will use the EON-XR commercial system to facilitate this training, measuring trainee performance during the job interview through the system, and examining subjective feedback on how the trainee perceives their improvement in soft skills.