Recent research:
Across multiple studies, the researcher investigates educational technology with a strong emphasis on AR/VR, learning analytics, and inclusive pedagogy, integrating AI methods to understand emotions, engagement, and learning outcomes. Key themes include AR/VR for algorithm and UML learning, emotion-aware teaching with CNN-based facial analysis, and LMS-like architectures that combine big data with aquaculture and IoT for smarter education-adjacent applications. Methodologies range from mixed methods (surveys, questionnaires, ANOVA, SUS/NASA-TLX) to AI-driven data analysis (LLaMA2 for bullying detection; CNN emotion recognition; AHP-based tool benchmarking). The impact centers on improving emotional engagement, participation, and learning performance while addressing privacy, accessibility, and ethical considerations. The work also explores practical classroom innovations (COMPUAR, UML-3Dfi, AR games) and systemic approaches (AI-assisted assessment, AR-based rule learning, and inclusive design for at-risk students). Overall, the profile presents a cohesive trajectory toward intelligent, emotion-aware, and inclusive educational technologies that support teaching quality, student well-being, and data-informed decision-making in diverse settings.

Key Topics:

• Educational Technology
• Gamification & XR in Learning
• Emotion & Learning Analytics
• AR/VR for Programming & UML
• AI in Education (NLP, ML, Data Science)
• Inclusion, Teacher Training & Accessibility
• Privacy, Ethics & Governance
• Pattern Recognition in Education (bullying detection, emotion states)