Contributed papers are solicited describing original works in AI-Driven Educational Technologies and Digital Learning. Topics and technical areas of interest include but are not limited to the following:

Track 1: AI Foundations and Models for Education
Foundation models and large language models (LLMs) for educational applications
Generative AI for content creation, tutoring, and assessment
Multimodal AI for learning analytics and educational insights
Intelligent tutoring systems and pedagogical agents
AI-driven question generation and automated feedback systems
Reinforcement learning and adaptive decision-making in educational contexts
Explainable, trustworthy, and responsible AI for education
Edge AI and scalable AI systems for educational deployment

Track 2: Personalized and Adaptive Learning Systems
AI-driven personalized and adaptive learning pathways
Intelligent tutoring systems with dynamic cognitive diagnosis
Adaptive learning platforms and learner modeling
Large language model-powered personalized tutoring and scaffolding
AI-driven learning style detection and content recommendation
Automated curriculum sequencing and pacing optimization
Student proficiency modeling and knowledge tracing
Human-AI co-learning and pedagogical innovation

Track 3: Learning Analytics, Educational Data Mining, and Assessment
Learning analytics, educational data mining, and dashboard design
Predictive analytics for student performance, at-risk identification, and intervention
Automated assessment, evaluation, and intelligent feedback systems
Multimodal learning analytics (behavioral, affective, cognitive)
Educational big data analytics and intelligent learning behavior assessment
Data-driven methods for understanding student learning behaviors
Cognition-centered analytics and AI Twin-driven education systems
Learning Experience Platforms (LXP) and data-informed decision-making

Track 4: Immersive, Interactive, and Digital Learning Environments
Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR) in education
Metaverse platforms, social VR, and collaborative virtual spaces for education
Simulation, virtual labs, and digital twins for skill training
Educational game design, serious games, and gamification strategies
Next-generation Learning Management Systems (LMS) and digital learning platforms
Cloud computing, edge computing, and cybersecurity in educational systems
Haptic technology and multi-sensory immersive learning experiences
Digital curriculum resource construction and smart teaching platform development

Track 5: Educational Equity, Inclusion, and Ethical AI
Ethical AI, algorithmic bias, transparency, and fairness in educational tools
Assistive technologies and adaptive tools for learners with disabilities
Digital solutions for neurodiverse and special needs education
Bridging the digital divide: access, affordability, and digital literacy
Culturally responsive pedagogies and multilingual learning technology
AI for inclusive and global education
Privacy-preserving AI, data protection, and information security in education
Designing for global and cross-cultural educational contexts

Track 6: Educational Policy, Teacher Development, and Future Skills
Teacher–AI collaboration, augmentation, and professional development in the AI era
AI literacy, critical digital literacy, and future skills education
Leadership, policy, and managing change for digital transformation in education
Hybrid, blended, and flexible learning models
Design and impact of MOOCs, micro-credentials, and nano-degrees
Institutional data strategy, governance, and data-informed decision-making
Neuroscience, cognitive science, and technology-enhanced learning
Sustainability, green computing, and EdTech's environmental impact