Call for Papers

  • Home
  • Call for Papers

Call for Papers

CFP Download

The conference welcomes original research papers, practical application works, and review articles covering adaptive learning systems, generative AI and large language models in education, learning analytics, intelligent tutoring, multimodal AI, affective computing, educational assessment, explainable AI, learner modeling, human-AI collaboration, AI curriculum design and XR immersive learning. All submissions will undergo peer review. Accepted papers will be published in the AIEPL Conference Proceedings and indexed by authoritative databases.

Track 1. Adaptive & Personalized Learning Systems

  • Adaptive learning frameworks for diverse student groups
  • Knowledge graph-based personalized learning path recommendation
  • Self-adaptive learning resource pushing mechanisms
  • Individualized learning strategy optimization algorithms
  • Dynamic student ability modeling for adaptive education
  • Personalized difficulty adjustment in online courses
  • Adaptive intervention strategies for at-risk learners
  • User profile-driven personalized learning ecosystems

Track 3. Generative AI & Large Language Models in Education

  • LLM-based intelligent teaching assistant systems
  • Generative AI for automated course content generation
  • Prompt engineering for educational scenario adaptation
  • Generative AI-powered personalized tutoring dialogue
  • Large model fine-tuning for domain-specific education
  • Automated question generation and test paper generation
  • Generative AI for educational content rewriting and optimization
  • Multimodal generative learning assistance in classrooms

Track 5. Learning Analytics & Educational Data Mining

  • Educational big data collection and preprocessing methods
  • Student learning behavior mining and pattern recognition
  • Learning performance prediction based on educational data
  • Real-time learning state analysis and early warning
  • Educational feature engineering and data dimensionality reduction
  • Learning trajectory mining and knowledge mastery analysis
  • Educational data visualization and intelligent analysis
  • Massive online learning data mining and application

Track 7. Intelligent Tutoring Systems & AI Pedagogical Agents

  • AI pedagogical agent design and behavioral simulation
  • Intelligent tutoring system based on knowledge tracing
  • Multi-agent collaborative teaching interaction models
  • Student confusion detection and tutoring intervention
  • Personalized question recommendation in tutoring systems
  • Intelligent error analysis and targeted tutoring strategies
  • Long-term student growth modeling for tutoring systems
  • Human-agent interactive teaching optimization mechanisms

Track 9. Multimodal AI for Learning & Assessment

  • Multimodal learning behavior recognition (vision, audio, text)
  • Fusion learning analysis based on multi-source educational data
  • Multimodal emotion recognition in online learning scenarios
  • Cross-modal knowledge understanding for intelligent assessment
  • Multimodal student engagement quantification methods
  • Multimodal deep learning for educational evaluation
  • Real-time multimodal learning state perception
  • Multimodal AI-based comprehensive learning assessment

Track 11. Affective Computing & AI-Driven Learning Engagement

  • Student emotion recognition in online and offline classrooms
  • Learning engagement prediction based on affective computing
  • Fatigue and distraction detection in long-term learning
  • Emotional state adaptive teaching adjustment strategies
  • Positive learning motivation stimulation via AI
  • Affective feature mining for learning performance analysis
  • Emotion-aware personalized learning intervention
  • Student psychological state analysis in intelligent education

Track 2. AI-Powered Educational Assessment & Intelligent Feedback

  • Automated scoring and evaluation algorithms for subjective questions
  • AI-based learning effect comprehensive assessment models
  • Real-time intelligent feedback in online learning
  • Fine-grained knowledge point evaluation and diagnosis
  • Automatic homework correction and error analysis
  • Formative assessment systems based on artificial intelligence
  • Summative evaluation optimization with deep learning
  • Personalized improvement suggestion generation

Track 4. Explainable AI & Trustworthy Educational Systems

  • Explainable AI (XAI) in educational decision-making
  • Trustworthy algorithm design for intelligent education
  • Bias detection and fairness optimization in educational AI
  • Interpretability improvement of learning prediction models
  • Privacy protection in educational intelligent systems
  • Security and reliability of AI teaching platforms
  • Transparent student evaluation algorithms
  • Trustworthy personalized learning recommendation systems

Track 6. AI-Driven Learner Modeling & Cognitive Diagnosis

  • Fine-grained student cognitive diagnosis modeling
  • Multi-dimensional learner profile construction methods
  • Knowledge state tracking and mastery evaluation
  • Cognitive defect mining and learning gap analysis
  • Student learning preference and habit modeling
  • Longitudinal learner growth state modeling
  • Multi-dimensional ability evaluation of students
  • Cognitive level classification and intelligent grouping

Track 8. Human–AI Collaboration in Teaching & Learning

  • Human-AI collaborative classroom teaching modes
  • Teacher-AI cooperative educational decision-making
  • Student-AI interactive learning mechanism optimization
  • Hybrid intelligent teaching assisted by artificial intelligence
  • Human-AI co-creation of educational resources
  • Collaborative intelligent learning in group education
  • Teacher workload optimization via AI collaboration
  • Future classroom human-machine collaborative education models

Track 10. AI Curriculum Design & Personalized Learning Paths

  • AI-driven curriculum framework optimization
  • Knowledge-based personalized learning path generation
  • Adaptive course sequence arrangement algorithms
  • Intelligent curriculum arrangement for different learners
  • Customized learning progression recommendation
  • Curriculum difficulty gradient design with AI
  • Interdisciplinary curriculum intelligent integration
  • Learning path dynamic adjustment based on ability changes

Track 12. XR/VR/AR Immersive Learning with AI

  • AI-enhanced VR immersive teaching scenarios
  • AR intelligent interactive learning environment construction
  • XR-based experiential education and skill training
  • AI-driven immersive learning behavior analysis
  • Intelligent interaction design in virtual classrooms
  • Immersive simulation education based on XR technology
  • AI scene generation for virtual teaching environments
  • Multimodal immersive learning experience optimization