Aspects of intelligent learning systems

Aim and Scope

With the rapid advancement of AI and digital technologies, intelligent and adaptive learning systems are increasingly being integrated into various educational contexts beyond traditional Learning Management Systems (LMS). These next-generation environments leverage artificial intelligence (AI) to personalize learning experiences, support educators, and enhance accessibility across time and distance.

This invited session aims to bring together researchers and practitioners to discuss innovative approaches, analytical frameworks, and applications of intelligent learning systems. We particularly welcome studies on how such systems capture, analyze, and utilize interaction data through cutting-edge analytical methods such as data mining, deep learning, and natural language processing (NLP) to improve learning processes and outcomes. The session also explores human-centered and affective aspects such as learner modeling, automatic feedback generation, ethical issues, and AI bias in education, contributing to the design of intelligent, trustworthy, and inclusive digital learning ecosystems. We invite contributions from researchers, educators, and practitioners working on intelligent, adaptive, and data-driven approaches to digital learning and educational innovation.

Topics of Interest

Specific topics of interest include but not limited to:

• AI and Adaptive Learning Systems
  • Intelligent and adaptive learning systems
  • AI in education and educational data mining
  • Applications of Generative AI (e.g., ChatGPT, Midjourney) in education
  • Adaptive support for learning, learner modeling, diagnosis, and feedback
  • Modeling of motivation, metacognition, and affective aspects of learning
• Technology-Enhanced and Evidence-Based Learning
  • Technology-enhanced learning and its theoretical foundations
  • Learning analytics and evidence-based education
  • Course management in online and virtual learning environments
  • Usage of Learning Management Systems (LMS) for teaching and learning practices
• Applications and Case Studies
  • Practices in specific educational contexts (e.g., language learning, STEAM, disaster prevention) using ICT
  • Curriculum and instructional design
  • Gamification and gamified learning environments
  • Embodied learning for wellbeing in education
• Ethics, Privacy, and Data Use
  • Use, security, ethics, and privacy of educational data
  • AI bias and fairness in learning systems
• Emerging Technologies and Interfaces
  • Complex system architecture and intelligent interfaces
  • Recommendation systems for education
  • Adaptation of AR/VR/XR in reshaping education
  • Development of innovative educational tools

Submission

KES2026 Invited Sessions Submission