Bringing Generative Artificial Intelligence (GenAI) to Education

A Tutorial at the Educational Data Mining Conference (EDM 2025)
July 20–23, 2025, Palermo, Italy

Time and Location

Time: Jul 20 (full-day), 2025

Location: Room F190

Abstract

Generative Artificial Intelligence (GenAI) has rapidly emerged as a transformative technology across multiple research communities. With its exceptional capabilities in generating images, audio, video, and natural language, GenAI has achieved unprecedented success in various domains. In this tutorial, we aim to provide a comprehensive overview of integrating GenAI into education, presenting recent advancements in the field, exploring both its opportunities and challenges, and inspiring further research in this direction. We begin by introducing fundamental concepts of GenAI to equip attendees with the necessary background knowledge for the discussions that follow. Next, we examine the impact of GenAI on education, summarizing key transformations, opportunities, and challenges to provide a broad perspective on its applications in educational settings. Building on this foundation, we present detailed case studies of representative works that implement GenAI in education. By showcasing pioneering research and applications, we illustrate the benefits, challenges, and limitations of GenAI-driven educational tools. Additionally, we have prepared a live demonstration session where participants will experience two LLM-powered educational systems: an interactive learning platform and an automated grading tool. Through hands-on interaction, attendees will gain firsthand insight into how GenAI is shaping education. Finally, we conclude with a discussion session, encouraging participants to share their thoughts, insights, and ideas on the future of GenAI in education. By the end of this tutorial, we hope attendees will gain a solid understanding of current trends in GenAI-driven education, develop deeper insights into this evolving field, and find inspiration for future research and applications.

Tutorial Syllabus

  1. Introduction About GenAI

  2. Overview of GenAI in Education

  3. Application Cases in Education

    1. Grading and Feedback

    2. Content Creation

    3. Education Assistant

    4. Role-Play Simulation

    5. Career Development

  4. Demo Systems Experience

  5. Future Directions and Q&A

Slides

Available upon Request

Presenters

Image of Hang 

Hang Li is a Ph.D. student at Michigan State University. He holds an M.S. in Statistics from the University of Illinois at Urbana-Champaign and a B.S. in Information and Computing Science from Beijing Jiaotong University. His research interests include Graph Neural Networks, Generative AI, and AI for Education. He has received several accolades, including 2nd Place in the OGB-LSC @ NeurIPS Node Classification Competition and 1st Place in the ACM Ubicomp STABILO Time Series Classification Challenge 2020. His prior research has been published in top-tier AI and education conferences, including AAAI, KDD, EMNLP, AIED, and EDM, among others. He regularly serves as an external reviewer for various data mining, natural language processing and machine learning conferences including ACL, AAAI, WWW, KDD, TKDE, IJCAI, etc.

Image of Korbin 

Kaiqi Yang is a Ph.D. student of Computer Science and Engineering at Michigan State University. He received a Master’s degree in Applied Statistics and a Bachelor’s degree in Sociology from Fudan University. His research interests include Social Computing, AI for Social Science, and Social Networks. His work has been accepted at leading conferences on data mining and natural language processing, such as CIKM, EMNLP, and AIED. He serves as a reviewer for ACL, TKDD, TKDE, CIKM, KDD, RecSys, etc.

Image of Yucheng 

Yucheng Chu is a Ph.D. student at Michigan State University. She holds a B.S. in Computer Science from Columbia University. Her research interests include Generative AI and AI for Education. Her prior research has been published in top-tier AI and education conferences such as AIED.

Image of Jiliang 

Jiliang Tang is University Foundation Professor in the computer science and engineering department at Michigan State University. He got one early promotion to Associate Professor in 2021 and then a promotion to Full Professor (designated as MSU Foundation Professor) in 2022. Before that, he was a research scientist in Yahoo Research. He got his Ph.D. from Arizona State University in 2015 and MS and BE from Beijing Institute of Technology in 2010 and 2008, respectively. His research interests include graph machine learning, trustworthy AI, and their applications in Education. He authored the first comprehensive book “Deep Learning on Graphs” with Cambridge University Press and developed various well-received open-sourced tools including scikit-feature for feature selection, DeepRobust for trustworthy AI, and DANCE for single-cell analysis. He was the recipient of various career awards (2022 AI’s 10 to Watch, 2022 IAPR J. K. Aggarwal, 2022 SIAM SDM, 2021 IEEE ICDM, 2021 IEEE Big Data Security, 2020 ACM SIGKDD, 2019 NSF), numerous industrial faculty awards (Meta, JP Morgan, Amazon, Cisco, Johnson & Johnson, Criteo Labs and SNAP), and 8 best paper awards (or runner-ups) including WSDM 2018 and KDD 2016. He serves as conference organizer (e.g., KDD, SIGIR, WSDM, and SDM) and journal editor (e.g., TKDD, TKDE, and TOIS). He has published his research in highly ranked journals and top conference proceedings, which have 43,000+ citations with an h-index of 98 and extensive media coverage.