In the era of “AI in Everywhere”, integrating advanced AI techniques into the educational environment has become a central issue for the continuous development of modern education systems. The main objective of this workshop is to discuss and share the latest academic efforts in applying optimization and machine learning to these crucial scenarios. Recent advances reveal that an AI-based approach can significantly enhance manual analysis and decision-making processes. For instance, machine learning can unravel the complex influences on student motivation, while combinatorial optimization techniques are proven to be highly effective in assembling personalized and adaptive tests. The main objective of this workshop is to discuss and share the latest academic efforts in applying optimization and machine learning to automate and improve these crucial educational scenarios. We aim to bring together researchers from optimization, machine learning, and educational technology to explore cutting-edge solutions and forge a path toward truly optimized, personalized learning environments.

Submission Guidelines

Authors should follow Springer LNCS Authors Guidelines: Information for Authors to prepare their papers. Formats for the submissions are limited within a maximal length of 8 pages for main content and 4 pages for references. All accepted papers by a registered author will be published as a volume in the Springer series Lecture Notes in Computer Science (LNCS). Authors should consult Springer's authors' guidelines and use the proceedings templates there linked. There are both LaTeX and Word formats: the authors make their choice. Also, authors should comply with Springer's authors' code of-conduct.

List of Topics

• Automated test assembly and parallel form construction (e.g., using evolutionary algorithms, integer programming)

• Machine learning models for understanding academic motivation, attribution, and engagement

• Affective computing, student emotion detection, and their roles in feedback

• Cognitive diagnosis models and their practical applications

• Algorithms and systems for adaptive testing and intelligent item selection

• Automated scoring and feedback generation for complex tasks

• Optimization of learning pathways and curricula for individuals or groups

• Reinforcement learning for optimizing pedagogical policies and adaptive interventions

• Resource allocation and scheduling in educational systems

• Educational data mining and learning analytics for personalization

• Generative AI for creating educational content and interactive learning scenarios

• Explainable AI for educational decision-making and recommendations

• Development and case studies of AI-powered tutoring systems

• Ethical considerations, fairness, and bias in educational AI systems

Committees

Program Committee

• Weijie Yu (Sun Yat-sen University)

• Ying LIN (Sun Yat-sen University)

• Liman Man Wai Li (The Education University of Hong Kong)

• Xiao－Min Hu (Guangdong University of Technology)

• Qiang Yang (Nanjing University of Information Science and Technology)

• Xiao-Fang Liu (Nankai University)

• Yong-Feng Ge (Victoria University)

• Xinyuan Zhang (Jinan University)

• Dan-Ting Duan (Communication University of China)

• Yuhui Zhang (Dongguan University of Technology)

• Xiaolin Xiao (South China Normal University)

• Yu Bai (Hanyang University )

Organizing committee

• Jun Zhang, Professor, Nankai University, IEEE Fellow. (Workshop Co-Chair)
• Yue-Jiao Gong, Professor, South China University of Technology. (Workshop Co-Chair)
• Zeyuan Ma, PhD Student, South China University of Technology. (Publication Co-Chair)
• Hongshu Guo, PhD Student, South China University of Technology. (Publication Co-Chair)

Invited Speakers

• Dr. Ying LIN, Associate Professor, Department of Psychology, Sun Yat-sen University.
• Dr. Liman Man Wai Li, Associate Arofessor, The Education University of Hong Kong.

Publication

All accepted papers by a registered author will be published as a volume in the Springer series Lecture Notes in Computer Science (LNCS). Among the papers accepted at the conference, some will be selected to be extended and submitted to a planned Special Issue of the Journal IEEE Transactions on Learning Technologies. 

Venue

The Hong Kong Polytechnic University, Hong Kong

Contact

Prof. Yue-Jiao Gong, gongyuejiao@gmail.com