Research Vision
Education That Learns Alongside the Student
Traditional digital learning platforms present identical content to
every learner. Our goal is to develop intelligent educational systems
that recognize individual learning differences and personalize content,
feedback, pacing, and instructional strategies in real time.
Core Technologies
Understanding the Learning Process
We combine physiological sensing, learning analytics, artificial
intelligence, and immersive technologies to estimate learner states
and support adaptive educational experiences.
Learning Analytics
Cognitive Load
Attention Modeling
Engagement Detection
Adaptive AI
Educational Data Mining
Attention
Monitor Learning Focus
Detect fluctuations in attention using behavioral, physiological,
and interaction data to identify moments when learners become
distracted or highly engaged.
Cognitive Load
Balance Mental Effort
Estimate cognitive workload during learning activities to optimize
task difficulty and reduce unnecessary mental overload.
Engagement
Promote Active Learning
Measure learner engagement and motivation to create more interactive,
personalized, and meaningful educational experiences.
Digital Learning
Adaptive Online Learning Platforms
Personalize instructional materials, pacing, quizzes, and feedback
according to each learner's progress and cognitive state.
Immersive Education
Virtual and Mixed Reality Classrooms
Create immersive learning experiences that dynamically adapt scenarios,
guidance, and challenges based on learner performance and attention.
Learning Analytics
Support Teachers with AI
Provide educators with meaningful insights into engagement, classroom
participation, learning difficulties, and overall student progress.
Personalized Education
Learning Designed for Every Student
Develop intelligent tutoring systems that continuously adapt to each
learner's abilities, preferences, and educational objectives.
Future Directions
From Intelligent Tutors to Lifelong Learning Companions
We envision educational technologies that not only evaluate learning
outcomes but also understand how knowledge is acquired, retained, and
applied over time. Future systems will combine multimodal AI, cognitive
digital twins, and neuroadaptive technologies to support personalized
lifelong learning.
Adaptive Learning
Learning Analytics
Human-AI Collaboration
Digital Twins
XR Education
Responsible AI
Student Opportunities
Design the Next Generation of Intelligent Learning Systems
Students in this research area work on educational AI, physiological
signal analysis, machine learning, user modeling, VR-based education,
human-centered design, and educational data analytics. Projects range
from adaptive tutoring systems to immersive learning environments and
intelligent classroom technologies.
Educational AI
Learning Analytics
Machine Learning
VR Education
Human Factors
User Studies