Welcome to GAIE 2025!
Generative AI (GAI) technologies, including generative adversarial networks (GANs), generative pre-trained transformer (GPT) and large language models (LLMs), are revolutionizing education by providing learners and educators a more innovative, flexible and personalized education environment to enhance teaching and learning experiences. With the exponential growth of online learning platforms and digital content, educators encounter the challenges of utilizing such extensive education resource and recommending tailored materials to individual learners. Demonstrating effectiveness in natural language generation and creative design, GAI models are garnering significant interest and demand, particularly for assisting in personalized tutoring, content creation, and developing virtual learning environments. The incorporation of GAI technologies enhances education by making it more engaging, accessible, and tailored to the specific needs of individual learners, as well as dismantling language barriers. As such, this workshop aims to group researchers interested in GAI applications across all aspects of technology-enhanced education, facilitating communication among educators, linguistic experts, data specialists and AI researchers. We hope that applying GAI in education can enhance the learning experience, leading towards greater effectiveness, efficiency and personalized in learning. The International Workshop on Generative Artificial Intelligence in Education (GAIE) aims to provide a platform for researchers in exchanging the most recent achievements of theories, datasets, methods, metrics, applications, etc. for the development of the interdisciplinary area.
This workshop is affiliated to the International Symposium on Emerging Technologies for Education 2025 (SETE 2025), in conjunction with the International Conference on Web-Based Learning 2025 (ICWL 2025).
Workshop topics of interest include, but are not limited to, the following:
- Large Language Models (LLMs) and its Applications in Education
- Large Models / Foundation Models for Education
- Development and Implementation of Multimodal Models in Education
- AI Generated Content (AIGC) for Education
- Real-World Impact and Effectiveness of GAI Models in Education
- Quality Assessment of Content Generation in Education
- Non-Reference/Reference-based Metrics for GAI Models in Education
- Language Translation and Natural Language Understanding in Education
- Natural Language Processing in Education
- Big Data in Technology-Enhanced Education
- Content Recommendation for Education Applications
- Text Simplification and Summarization for Digital Education Contents
- Data Mining on Digital Education Resources
- Automatic Question Answering for Assisting Education
- Computational Linguistics for Education Data Processing and Evaluation
- Interpretation of GAI Model Outputs for Education
- Digital Libraries and Corpora for Education
General Co-Chairs
Tianyong Hao, South China Normal University, China
Fu Lee Wang, Hong Kong Metropolitan University, Hong Kong SAR
Lan Shuai, Educational Testing Service, USA
Program Committee Co-Chairs
Wenxiu Xie, Guangdong Polytechnic Normal University, China
Xiaoyong Hu, South China Normal University, China
Tao Gong, Google, USA
Organizing Committee Co-Chairs
Yong Li, Guangdong University of Education, China
Zili Chen, The Hong Kong Polytechnic University, Hong Kong SAR
Publication Co-Chairs
Shengyi Jiang, Guangdong University of Foreign Studies, China
Nana Jin, Shenzhen University, Shenzhen, China
Publicity Co-Chairs
Haijun Zhang, Harbin Institute of Technology (Shenzhen), China
Lap-Kei Lee, Hong Kong Metropolitan University, Hong Kong SAR
Program Committee
Haijun Zhang, Harbin Institute of Technology (Shenzhen), China
Zhenguo Yang, Guangdong University of Technology, China
Haitao Wang, China National Institute of Standardization, China
Jun Yan, Yidu Cloud Private Equity Management (Beijing), China
Ruoyao Ding, Guangdong University of Foreign Studies, China
Lap-Kei Lee, Hong Kong Metropolitan University, Hong Kong SAR
Yingshan Shen, South China Normal University, China
Hai Liu, South China Normal University, China
Zili Chen, The Hong Kong Polytechnic University, Hong Kong SAR
Nana Jin, Shenzhen University, Shenzhen, China
Enliang Yan, South China Normal University, China
Choujun Zhan, South China Normal University, China
Likeng Liang, Guangzhou Nanfang University, China
Heng Weng, Guangzhou University of Chinese Medicine, China
Kun Zeng, Sun Yat-Sen University, China
Kiyong Lee, Korea University, South Korea
Xinyu Cao, China National Institute of Standardization, China
Jie Wei, China National Institute of Standardization, China
Yuanyuan Mu, Chaohu University, China
Tao Gong, Google, USA
Shengyi Jiang, Guangdong University of Foreign Studies, China
Fu Lee Wang, Hong Kong Metropolitan University, Hong Kong SAR
Wenxiu Xie, Guangdong Polytechnic Normal University, China
Yong Li, Guangdong University of Education, China
Xiaoyong Hu, South China Normal University, China
Chunxia Zhang, Beijing Institute of Technology, China
Yingyi Zhuang, The Chinese University of Hong Kong (Shenzhen), China
Tianyong Hao, South China Normal University, China
Tiezheng Mao, Waseda University, Japan
Dabin Lin, Guangzhou Mumu Information Technology Co., Ltd., China
Junjie Chen, The University of Tokyo, Japan
Ru Peng, Zhejiang University, China
Ke Hu, The Chinese University of Hong Kong (Shenzhen), China
Paper Submission
All submissions must be in PDF format. Authors should avoid the use of non-English fonts to avoid problems with printing and viewing the submissions. All accepted papers MUST follow strictly the instructions for LNCS Authors. Springer's LNCS site offers style files and information. Formats for the submissions are either Long Paper (12-15 pages) or Short Paper (6-11 pages), including main content and references. Extra pages will have a cost of 100 AUD to Springer during publication.
Submissions MUST not have been published previously and must not be under review for publication while being considered for GAIE. This applies also to papers with significantly overlapping contributions. Authors are advised to interpret these limitations strictly and to contact the workshop chairs in case of doubt.
Submissions MUST follow LNCS (Lecture Notes in Computer Science) format. We encourage authors to cite related work comprehensively, and when citing conference papers please also consider to cite their extended journal versions if applicable.
GAIE 2025 will employ double-blind reviewing process, every research paper submitted to GAIE 2025 will undergo a “double-blind” reviewing process: the PC members and referees who review the paper will not know the identity of the authors. To ensure anonymity of authorship, authors must prepare their manuscript as follows:
- Author’s names and affiliations must not appear on the title page or elsewhere in the paper.
- Funding sources must not be acknowledged on the title page or elsewhere in the paper.
- Research group members, or other colleagues or collaborators, must not be acknowledged anywhere in the paper.
- Source file naming must also be done with care, to avoid identifying the author’s names in the paper’s associated metadata. For example, if your name is Jane Smith and you submit a PDF file generated from a .dvi file called Jane-Smith.dvi, your authorship could be inferred by looking into the PDF file.
It is the responsibility of authors to do their very best to preserve anonymity. Papers that do not follow the guidelines here, or otherwise potentially reveal the identity of the authors, are subject to immediate rejection. Because of the double blind review policy, the submission of an extended version of a short paper which has published elsewhere is strongly discouraged in GAIE 2025.
Paper Submission System is Available at：
https://easychair.org/conferences/?conf=gaie2025
Registration Requirements
Each accepted paper must be accompanied by at least one full registration regardless of the status of the registering author. Otherwise, the paper will be removed from the proceedings and LNCS digital library. For more information regarding registration see the ICWL-SETE 2025 registration page.
Contact Us
All inquiries about the workshop, including submissions, can be emailed to wxxie@gpnu.edu.cn.