This book constitutes the proceedings of the The S eventh International Workshop on AI for Education, AI4EDU 2025, Held in Conjunction with AAAI 2026, Singapore, during January 26, 2026.

The 11 full papers included in this book were carefully reviewed and selected from 60 submissions.These papers covered areas such as Multi-Agent Educational Systems; Personalized Learning and Knowledge Tracing; Intelligent Tutoring and Learning Path Recommendation; AI Assessment and Data Generation.

Klappentext

This book constitutes the proceedings of the The Seventh International Workshop on AI for Education, AI4EDU 2025, Held in Conjunction with AAAI 2026, Singapore, during January 26, 2026.

The 11 full papers included in this book were carefully reviewed and selected from 60 submissions.These papers covered areas such as Multi-Agent Educational Systems; Personalized Learning and Knowledge Tracing; Intelligent Tutoring and Learning Path Recommendation; AI Assessment and Data Generation.

Inhalt

.- Multi-Agent Educational Systems.

.- CoLearn: A Multi-Agent System for Personalized Blended Learning in Higher
Education.

.- Addressing Situated Teaching Needs: A Multi-Agent Framework for Automated
Slide Adaptation.

.- AI-Driven Adaptive Tutoring: A Multi-Agent System for Structured, Multimedia
Enhanced Education.

.- MASA:Multi-Agent Guided Interview for Scenario-Based Assessment of Critical
Thinking.

.- Personalized Learning and Knowledge Tracing.

.- Teaching According to Students' Aptitude: Personalized Mathematics Tutoring
via Persona-, Memory-, and Forgetting-Aware LLMs.

.- Reconstruction Attention Positional Encoding for Knowledge Tracing: Integrat
ing Cognitive Forgetting into Transformer-Based Models.

.- RouteKT: A Knowledge Tracing Framework for Modeling Students' Problem
Solving Routes with Large Language Model.

.- Intelligent Tutoring and Learning Path Recommendation.

.- SpeakerTrainer: Multimodal Coaching of Presentation Skills on Mobile Devices.

.- Context-Driven Learning Path Recommendation: From Static Records to Dy
namic Contexts.

.- AI Assessment and Data Generation.

.- Evaluating LLMs as Self-Assessing Educational Agents: Dual-Role Modeling for
Reliable AI-Generated Exams.

.- Synthetic Data in Education: Empirical Insights from Traditional Resampling
and Deep Generative Models.