• 2026 International Conference on Smart Education and Data Mining

Call for paper

SEDM 2026   invites the submission of original research papers in Smart Education and Data Mining, covering theoretical, experimental, and applied perspectives, for publication in the conference proceedings. To ensure high standards and quality, each submission undergoes anonymous peer review by an average of three independent reviewers. Accepted papers will be presented as either oral presentations or posters at the conference. Topics of interest include (but are not limited to) the following areas: 

 

  • Track 1: Educational Data Mining and Learning Analytics

    Multimodal Analytics and Mining Algorithms for Learning Behaviors
    Content Generation and Interactive Optimization with Large Language Models
    Cognitive State Modeling and Learner Profile Construction
    Learning Outcome Prediction and Early Warning Systems
    Algorithms for Personalized Learning Path Recommendation
    Interpretable Analysis and Visualization of Educational Data
    Automated Assessment and Adaptive Feedback Mechanisms
    Knowledge State Diagnosis and Learning Progress Tracking
    Multidimensional Evaluation Framework for Academic Performance
    Prompt Engineering and Large Language Models in Education

  • Track 2: Intelligent Educational Systems and Architecture

    Metaverse-Enhanced Education and Immersive Learning Environments
    Microservices and Cloud-Native Architecture for Educational Platforms
    Optimization of Low-Latency Interactive Learning Systems
    Inference Optimization and Service Architecture for Educational LLMs
    Model Compression and Acceleration for Lightweight Educational Applications
    Integration and Application of Educational Robotics Systems
    Software Engineering and DevOps Practices in Education
    System Fault Tolerance and Quality of Service Assurance
    Innovative Applications of Edge Computing in Educational Contexts
    Design of AI-Enabled Intelligent Teaching Systems

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  • Track 3: Educational Knowledge Engineering and Data Governance

    Construction and Dynamic Evolution of Educational Knowledge Graphs
    Domain Ontologies and Semantic Interoperability Techniques
    Adaptive Knowledge Delivery and Personalized Service Frameworks
    Secure Computation and Privacy-Preserving Techniques for Educational Data
    Data Standardization and Cross-Platform Sharing Frameworks
    Compliance and Governance of Cross-Border Educational Data
    Ethical Frameworks and Algorithmic Fairness in Educational AI
    Bias Detection and Mitigation Techniques
    Explainable AI and Transparent Educational Systems
    Practices for Responsible AI in Education

 

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