Special Session 9

GAI-Enhanced Learning Analytics

Description
This special session explores the emerging role of Generative Artificial Intelligence (GAI) in learning analytics and educational innovation. It focuses on how technologies such as large language models, multimodal AI, and intelligent learning systems can support data-informed teaching, personalized learning, adaptive assessment, and educational decision-making. The session also examines challenges related to ethics, transparency, privacy, and human-AI collaboration in educational contexts. By bringing together researchers and practitioners, the session aims to promote innovative and inclusive approaches to AI-enhanced learning analytics in the era of generative AI.

Session organizer
Assoc. Prof. Xieling Chen, Guangzhou University, China

The topics of interest include, but are not limited to:
▪ GAI-Supported Learning Analytics and Educational Data Mining
▪ Personalized Learning and Adaptive Feedback with Generative AI
▪ Human-AI Collaboration in Teaching and Learning Analytics
▪ Multimodal Learning Analytics and Learner Behavior Analysis
▪ Explainable, Ethical, and Trustworthy AI in Education
▪ AI-Driven Assessment and Academic Integrity
▪ Learning Analytics for Smart and Hybrid Learning Environments
▪ Data Privacy, Transparency, and Governance in AI-Enhanced Education

Submission method
Submit your Full Paper (no less than 5 pages with two colums) or your paper abstract-without publication (200-400 words) via Online Submission System, then choose Special Session 9 (GAI-Enhanced Learning Analytics)
Template Download

Introduction of session organizer

Assoc. Prof. Xieling Chen
Guangzhou University, China

Chen Xieling is an Associate Professor at Guangzhou University, and a distinguished young talent under the “Hundred Talents Program.” She has served as the principal investigator for several research projects, including those funded by the National Natural Science Foundation of China and the Guangdong Provincial Philosophy and Social Sciences Planning Program. Her research interests include “AI + Education,” educational large language models, AI-empowered teaching, and natural language processing. She has published nearly 100 academic papers in international and domestic journals and conference proceedings, with more than 50 papers published in SCI/SSCI-indexed journals such as Computers & Education. Three of her papers have been listed as ESI Highly Cited Papers. She serves as an Editorial Board Member of Elsevier journals, including Computers and Education: Artificial Intelligence. She has been ranked among the World’s Top 2% Scientists for four consecutive years (2022–2025).