AIESC 2027: The International Conference on Artificial Intelligence in Education and Smart Campus (AIESC 2027) Musashino University Ariake Campus Tokyo, Japan, April 2-4, 2027

About AIESC 2027 (Hybrid Conference)

The International Conference on Artificial Intelligence in Education and Smart Campus (AIESC 2027) will be held at Musashino University Ariake Campus, Tokyo, Japan, from April 02-04, 2027, under the theme Human-Centric Intelligence for AI-Enabled Education and Smart Campus.

AIESC 2027 is organized by Musashino University (Japan) and The Chinese University of Hong Kong (China), and co-organized by Yamagata University (Japan). The conference receives support from Zhejiang University (China), De La Salle University Manila (Philippines), and College of Computer Science and Technology, Zhejiang University of Technology (China). It is co-sponsored by IEEE, IEEE Systems, Man, and Cybernetics Society, Beijing University of Posts and Telecommunications (China) and Qtairo Inc. (South Korea) with MPDI Digital, Computers and Conference Alert as the media partners.

AIESC 2027 focuses on Generative AI, Foundation Models and Intelligent Learning Systems; Human-Centered AI and Human-Machine Systems in Education; Intelligent Adaptive Learning, Cognitive Systems and Personalized Education, etc.. Through Keynote Speeches, Invited Sessions, Industrial Talks, AI workshops, High‑Level Roundtable, Teacher Education Forum, Editors' Dialogue, Technical Sessions, Poster Flash Sessions and Special Sessions, AIESC 2027 aims to foster interdisciplinary collaboration, promote knowledge exchange, and advance human-centered intelligent systems that empower the next generation of education and smart campuses.

Submission Guidelines

Accepted and registered full papers will be published in IEEE Xplore and submitted for indexing in Ei Compendex and Scopus.

AIESC 2027 has been included in the IEEE Conferences list: Click

Submission Link:https://easychair.org/conferences/?conf=aiesc2027

Papers submitted to the conference should be in English, and at least 5 pages. An extra fee will be charged for pages exceeding 5. The organizing committee will confirm your submission and provide an article number within 2 working days after receiving your manuscript. After the submission, we will forward your paper to relevant experts for review based on its theme.

Full Papers: Your manuscript accepted by AIESC 2027 will be published and you can deliver a paper presentation on AIESC 2027, orally or poster presentation. You must submit a Full-length Manuscript for review before the submission deadline.

Abstract Submission: You can deliver a presentation on AIESC 2027, but the presented manuscript will not be published. The abstract is necessary to submit.

For submissions intended for publication, please submit the full manuscript. Scholars are requested to prepare their manuscripts according to the provided template. All submitted articles should report original, previously unpublished research results, experimental or theoretical. Articles submitted to the conference should meet these criteria and must not be under consideration for publication elsewhere. We firmly believe that ethical conduct is the most essential virtue of any academic. Hence any act of plagiarism is a totally unacceptable academic misconduct and cannot be tolerated.

Full Paper Template | Abstract Template

Important DatesImportant Dates

Paper Submission Deadline: October 20, 2026

Notification of Acceptance/Rejection: November 20, 2026

Early Bird Registration Deadline: December 20, 2026

More details: https://www.aiesc.tech/index.html

List of Topics

Track 1: Generative AI, Foundation Models and Intelligent Learning Systems

Large Language Models (LLMs) and Foundation Models for Education
Generative AI-enabled Intelligent Learning Systems
Multimodal Foundation Models for Educational Applications
AI Agents and Autonomous Learning Assistants
Multi-Agent Systems for Education
Retrieval-Augmented Generation (RAG) for Educational Applications
Prompt Engineering and AI-assisted Knowledge Creation
Educational Natural Language Processing
Multimodal Learning and Reasoning
Knowledge-Augmented and Retrieval-Based AI Systems
Explainable, Trustworthy and Responsible Generative AI
Evaluation and Benchmarking of Generative AI Systems
Human-AI Collaborative Learning Systems

Track 2: Human-Centered AI and Human-Machine Systems in Education

Human-AI Interaction in Education
Human-Machine Interaction for Learning
Human-Centered Intelligent Educational Systems
Cognitive Computing for Education
Cognitive Modeling of Learning and Teaching
Human-AI Collaboration in Teaching and Learning
Intelligent Assistive and Companion Technologies
Affective Computing and Emotion-Aware Learning
Multimodal Human Behavior Understanding
User Modeling and Learner Modeling
Human-Machine System Modeling and Evaluation
Explainable and Interactive AI
Trustworthy Human-AI Collaboration
AI-assisted Teaching and Learning
Intelligent Learning Experience Design

Track 3: Intelligent Adaptive Learning, Cognitive Systems and Personalized Education

Intelligent Adaptive Learning Systems
Learner Modeling and Cognitive Modeling for Adaptive Education
Multi-dimensional Student Learning Profile Modeling
Personalized Learning Path Planning
Intelligent Recommendation for Learning Resources
Adaptive Difficulty Modeling and Optimization
Intelligent Question Generation and Adaptive Assessment
Intelligent Identification of Learning Weaknesses
Personalized Feedback Generation
Adaptive Learning in Online and Blended Education
AI-based Differentiated and Individualized Instruction
Cognitive AI for Personalized Learning
Reinforcement Learning for Adaptive Education
Continual Learning for Personalized Educational Systems
Intelligent Tutoring Systems

Track 4: Educational Data Intelligence, Machine Learning and Intelligent Decision-Making

Machine Learning for Educational Data Analytics
Deep Learning for Learning Analytics
Educational Data Mining
Multi-source Learning Behavior Modeling
Student Behavior Modeling and Prediction
Learning Pattern Recognition
Intelligent Identification and Prediction of Academic Risks
Early Warning Systems for Learning and Academic Performance
Intelligent Decision Support for Education
Explainable Machine Learning for Educational Applications
Causal Learning and Causal Inference in Education
Federated Learning for Educational Data
Privacy-Preserving Machine Learning
Intelligent Assessment and Evaluation
Dynamic Modeling of Learning Processes
Data-Driven Optimization of Teaching and Learning Systems

Track 5: Smart Campus Systems, IoT and Intelligent Infrastructure

Intelligent Smart Campus Systems
Internet of Things (IoT) for Smart Education
Intelligent Sensing and Context-Aware Computing
Cloud-Edge-Device Collaborative Intelligence
Edge AI for Smart Campus Applications
Intelligent Campus Data Infrastructure
Cyber-Physical Systems for Smart Campus
Intelligent Resource Allocation and Scheduling
Intelligent Computing Resource Management
Autonomous Campus Service Systems
Intelligent Campus Network Management
AI-enabled Infrastructure Monitoring
Intelligent Building and Energy Management
Context-Aware Smart Learning Environments
IoT-enabled Learning Environments
Intelligent Campus Operations and Services

Track 6: Digital Twins, Embodied AI and Cyber-Physical Learning Environments

Digital Twin for Smart Campus Systems
Digital Twin Modeling and Simulation
Real-Time Digital Twin Synchronization
Cyber-Physical Learning Systems
Embodied AI for Education
Intelligent Robots for Education
Human-Robot Interaction in Learning Environments
Autonomous Learning Agents and Robotic Assistants
Virtual and Augmented Reality for Intelligent Learning
Immersive Learning Environments
Multimodal Perception for Smart Learning
Intelligent Virtual Laboratories
Digital Twin-based Campus Management
Simulation and Optimization of Smart Campus Systems
Multi-Agent Collaboration in Virtual Learning Environments

Track 7: Campus Intelligent Management and Security Technology

AI-based Intelligent Recognition and Monitoring of Campus Personnel and Vehicles
Intelligent Early Warning and Detection of Abnormal Campus Behaviors
Intelligent Inspection, Operation and Maintenance of Campus Facilities
Intelligent Optimization of Educational Scheduling and Classroom Resource Allocation
Fine-grained Analysis of Student Campus Behaviors
Intelligent Campus Office Automation
Big Data Analytics and Risk Prediction for Campus Security Management

Track 8: Trustworthy AI, Cybernetics, Security and Emerging Intelligent Technologies

Trustworthy and Responsible AI
Explainable AI
Fairness and Bias Mitigation
AI Safety and Robustness
Adversarial Machine Learning
Security of AI-enabled Educational Systems
Secure and Trustworthy Smart Campus Systems
AI Model Vulnerability Detection
Cybersecurity for Intelligent Learning Systems
AI-based Anomaly Detection
Risk-Aware Intelligent Systems
Emerging Cybernetic Systems

Conference Speakers

Prof. Yasushi Asami, The University of Tokyo, Japan (Vice President)

Prof. Ryan Baker, The University of Pennsylvania, USA

Prof. Benjamin W. Wah, The Chinese University of Hong Kong, China

Prof. Maiga Chang, Athabasca University, Canada

Prof. Sangmin-Michelle Lee, Kyung Hee University, South Korea

More Invited Speakers:AIESC 2027| Invited Speakers

Publication

Accepted and registered full papers will be published in IEEE Xplore and submitted for indexing in Ei Compendex and Scopus.

AIESC 2027 has been included in the IEEE Conferences list: Click

Special Issues:
MDPI Digital
Indexing: Scopus, Ei Compendex, EBSCO, and other databases.
Journal Rank: CiteScore - Q1

MDPI Computers
Indexing: Scopus, ESCI (Web of Science), dblp, Inspec, Ei Compendex, and other databases
Journal Rank: JCR - Q2 CiteScore - Q1
Impact Factor: 5.2

Venue

Musashino University Ariake Campus

Address: 3-3-3 Ariake, Koto-ku, Tokyo 135-8181, Japan

Contact

Please feel free to contact us: aiesc.conference@gmail.com

Conference Secretary

Mia Sato

Organized by

Musashino University, Japan and The Chinese University of Hong Kong, China,

Co-Organized by

Yamagata University, Japan and Asia Pacific University of Technology & Innovation (APU), Malaysia

Supported by

Zhejiang University, China, De La Salle University Manila, Philippines, College of Computer Science and Technology, Zhejiang University of Technology, China

Sponsored by

Beijing University of Posts and Telecommunications, China, IEEE, IEEE Systems, Man, and Cybernetics Society, and Qtairo Inc., South Korea