Workshop on

AI for Education (AI4EDU): Exploring LLMs and Adaptive Learning for Personalized Education


Recent advanced AI technologies, especially the large language models (LLMs) like GPTs, have significantly advanced the field of natural language processing (NLP) and led to the development of various LLM-based applications. One potential application is as communication interfaces in human-in-the-loop education systems, where the model serves as a mediator among the teacher, students and the machine capabilities including its own. This approach has several benefits, including the ability to personalize interactions, allow unprecedented flexibility and adaptivity for human-AI collaboration and improve the user experience. However, several challenges still exist in implementing this approach, including the need for more robust models, designing effective user interfaces, and ensuring ethical considerations are addressed.
This workshop aims to bring together researchers and practitioners from academia and industry to explore the cutting-edge AI technologies for personalized education, especially the potential of LLMs and adaptive learning technologies. The objectives of the workshop are to: 1. Review the current state-of-the-art in LLM-based systems and their applications in education. 2. Identify challenges and opportunities in using LLMs as both communication and collaboration interfaces in adaptive learning systems, educational games and intelligent educational assistant. 3. Discuss the potential of LLMs in improving human-computer interaction (HCI) and user experience in educational settings. 4. Discuss the state-of-the-art technologies of adaptive learning that tailor education to the individual needs, learning styles, proficiency levels, and problem areas of each student, for personalized learning experience. 5. Explore ethical considerations and standardization issues in the use of LLMs, especially with IEEE AI Standards Committee and IEEE Learning Technology Standards Committee. 6. Introduce and design new implementation approaches such as prompt engineering, local fine tuning, integrated reasoning, and delegation framework for dialog-based systems that use natural language to not only to generate content but also shape the behavior of the system.

This workshop encourages submissions of innovative solutions for a broad range of AI for Education problems. Topics of interest include but are not limited to the following:

  • LLM-based models and their applications in educational settings
  • Adaptive learning systems and their applications in educational settings
  • The potential of LLMs in education from both the theoretical and practical angles
  • Ethical considerations in the use of LLMs as communication interfaces in educational settings, especially with IEEE AI Standards Committee
  • Standardization for LLM-based communication interfaces in education
  • Designing effective user interfaces for LLM-based systems
  • LLM and related model training, testing, validation, and deployment
  • IEEE AISC and LTSC Standardization Efforts around these areas

Call for Papers

Contact with us: Contact Us

We invite high-quality paper submissions of theoretical and experimental nature on the broad AI4EDU topics. The workshop solicits 5-7 pages double-blind paper submissions following CAI 2024 template from participants. All submissions will be peer-reviewed. Submissions of the following flavors will be sought: (1) research ideas, (2) case studies (or deployed projects), (3) review papers, (4) best practice papers, and (5) lessons learned. An optional appendix of arbitrary length is allowed and should be put at the end of the paper (after references).

Accepted papers will be presented as posters during the workshop and list on the website (non-archival/without proceedings). Besides, a small number of accepted papers will be selected to be presented as contributed talks.

Submission link:

Any questions may be directed to the workshop e-mail address: Contact Us

Key Dates


Workshop Paper Submission Due Date: May 15, 2024(AoE)

Notification of Paper Acceptance: May 22, 2024

Camera-ready Papers Due: May 30, 2024

CAI-24 Workshops: June 25, 2024

Workshop Schedule:

Time and Date: 10:30 am – 2:30 pm, June 25, 2024

Location: Melati Junior 4011

Zoom Link: TBD

10:30 am - 10:40 amOpening Remarks (Richard Tong)
10:40 am - 11:10 amKeynote Talk (Xiangen Hu)
11:10 am - 11:40 amKeynote Talk (Richard Tong)
11:40 am – 12:10 pmPanel Discussion (Cheryl Wong Sze Yin, Xiangen Hu, Richard Tong)
12:30pm – 1:30 pmLunch Break
1:30pm – 2:00pmKeynote Talk (Cheryl Wong Sze Yin)
2:00pm – 2:20pm Contributed Talk
2:20 pm - 2:30 pmClosing Remark (Richard Tong)  

Keynote Speakers (TBD)

Cheryl Wong Sze Yin

Senior Scientist

Assessing learning in the era of AI

Abstract: Typical assessments of learning are through written assignments or through final written or oral examinations. The rise of AI, in particular generative AI, has changed the way people search for information and the way that people learn. Hence, the method of assessment should also reflect this change. In this talk, I will share potential areas that we can leverage AI to assess learning.

Short Bio:
Cheryl is currently a senior scientist at Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR) and a core member of the AI4EDU programme in A*STAR. Cheryl obtained her PhD from Nanyang Technological University, Singapore (NTU), studying various optimization algorithms and its application in airspace management. Cheryl has been developing artificial intelligence (AI) solutions over various domains and adapting these models over time with new incoming data. In the past 3 years, Cheryl has been investigating how artificial intelligence (AI) can be applied in the education domain. Her current research interests mainly focus on developing or adapting current AI technologies for use in education. This includes adaptation of knowledge tracing models over time, quantifying and measuring student engagement and the impact of AI on education. .

Speaker Name

University of X

Title of the Talk

Abstract: xxx.

Short Bio:
XXX is a Professor at XX .


Accepted Papers


Accepted Papers

Flipped Classroom Teaching with Large Language Models: Peer Instruction and Just-in-Time Teaching, Chee Wei Tan

A Web Application for Video Transcript Summarizer and Translator, Hima Harshitha Angadala, Shaik Ershathunnisa

Code Generation from Flowchart using Optical Character Recognition and Large Language Model, Aryaman Darda, Reetu Jain

Evaluating the Impact of Code Autocompletion using Natural Language Processing on Developer Productivity and Software Quality, Aryan Sharma, Meet Arora, Ankit Jha, Gunjan Bhalla, Ankita Sharma, Anand Kumar


Workshop Organizers

Qingsong Wen

Head of AI Research & Chief Scientist
Squirrel Ai Learning

Short Bio: Qingsong Wen is the Head of AI Research & Chief Scientist at Squirrel Ai Learning by Yixue Education Inc., working in EdTech area via AI technologies. Before that, he worked at Alibaba, Futurewei, Qualcomm, and Marvell, and received his M.S. and Ph.D. degrees in Electrical and Computer Engineering from Georgia Institute of Technology, USA. His research interests include machine learning and decision intelligence, especially AI for Time Series (AI4TS) & AI for Education (AI4EDU). He has published around 100 top-ranked AI conference and journal papers, had multiple Oral/Spotlight Papers at NeurIPS/ICLR, had multiple Most Influential Papers at IJCAI, received multiple IAAI Deployed Application Awards at AAAI, and won First Place of SP Grand Challenge at ICASSP. Currently, he serves as Organizer/Co-Chair of Workshop on AI for Time Series (AI4TS @ KDD, ICDM, SDM, AAAI, IJCAI) and Workshop on AI for Education (AI4EDU @ CAI). He also serves as Associate Editor for Neurocomputing, Guest Editor for IEEE Internet of Things Journal, and Guest Editor for Applied Energy. In addition, he has regularly served as Area Chair/(S)PC of the AI conferences including KDD, AAAI, IJCAI, ICDM, ICASSP, etc.

Joleen Liang

Squirrel Ai Learning

Short Bio: Joleen Liang is the Co-Founder of Squirrel Ai Learning. She is Visiting Professor at the Research Institute for Innovation and Technology in Education (UNIR iTED), the Secretary of the IEEE International K-12 Education Knowledge Graph Standards Working Group, the Vice-Dean of Intelligent Education Committee of China Automation Congress, the Deputy Head of the Technology and Standards Working Group, the Smart Education Working Committee of the Internet Society of China, and the Executive Director of the Artificial Intelligence and Robotics Education Committee of the China Education Development Strategy Society. She received her Ph.D in Intelligent Science and Systems at Macau University of Science and Technology. In 2020, she and Squirrel Ai was honored ‘AI Education Innovation Award’by UNESCO. She is also the founder/director of AI+Adaptive Education International Conference (AIAED) focusing on important trends emerging from AI-tech applied to next-generation education and how these advances can impact adaptive human learning at scale. The 1st-4th AIAED invited more 200 AI and AI education speakers (scientists and companies) internationally, and 10000+audiences, 500+ investors, 1000+ CEOs, 200+ media.

Richard Tong

Chief Architect
Squirrel Ai Learning

Short Bio: Richard Tong is currently the Chief Architect of Squirrel Ai Learning. Prior that, he was the Principal Architect of Carnegie Learning, the Head of Implementation at Greater China Region for Knewton, and the Director of Solution Architecture for Amplify Education. He also served as the CTO of Phoenix New Media. He serves as the Chair of IEEE Artificial Intelligence Standards Committee. He is an experienced technologist, executive, entrepreneur and one of the leading evangelists for standardization effort for global education technology and AI in education.

Yu Lu

Associate Professor
Beijing Normal University

Short Bio: Yu LU received the Ph.D. from National University of Singapore in computer engineering. He is currently an Associate Professor with the School of Educational Technology, Faculty of Education, Beijing Normal University (BNU), where he also serves as the director of AI Lab at the advanced innovation center for future education (AICFE). He has published more than 90 academic papers in the prestigious venues, and serves as the associate editor for multiple academic journals, such as IEEE Transactions on Learning Technologies. He also serves as PC member or track chair for multiple international conferences (e.g., AIED, EDM, AAAI, ACL, EMNLP, IJCAI, ICCE, GCCCE). Before joining BNU, he was a research scientist and principle investigator at the Institute for Infocomm Research (I2R), A*STAR, Singapore. His research interests mainly sit at the intersection field of artificial intelligence and education.

Liu Guimei

Principal Scientist

Short Bio: Liu Guimei is currently a Principal Scientist and Principal Investigator at Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR), Singapore. She received her PhD in Computer Science from Hong Kong University of Science and Technology (HKUST). She has published 40+ papers at data mining/AI/Bioinformatics conferences and journals. Her current research interests are mainly on learning analytics and adaptive learning.

Xiangen Hu

The University of Memphis

Short Bio: Xiangen Hu is a professor in the Department of Psychology, Department of Electrical and Computer Engineering and Computer Science Department at The University of Memphis (UofM) and senior researcher at the Institute for Intelligent Systems (IIS) at the UofM and is professor and Dean of the School of Psychology at Central China Normal University (CCNU). Dr. Hu received his MS in applied mathematics from Huazhong University of Science and Technology, MA in social sciences and Ph.D. in Cognitive Sciences from the University of California, Irvine. Dr. Hu is the Director of Advanced Distributed Learning (ADL) Partnership Laboratory at the UofM, and is a senior researcher in the Chinese Ministry of Education’s Key Laboratory of Adolescent Cyberpsychology and Behavior.

Robby Robson

Chief Science Officer & Co-Founder
Eduworks Corporation

Short Bio: Robby Robson is a researcher, entrepreneur, and standards professional known for creative and disruptive innovation in industry and academia. He is a co-founder of Eduworks Corporation where he was CEO for 20 years and where he currently serves as Chief Science Officer and Board Chair. His focus at Eduworks is on developing transformative tools for competency-based talent management and workforce development. As a volunteer, Robby serves on multiple IEEE boards and committees in the areas of Standards, Open Source, and Educational Activities.

Program Committee

  • Zhendong Chu, University of Virginia
  • Aoran Wang, University of Luxembourg
  • Hang Li, Michigan State University
  • Ronghuai Huang, Beijing Normal University
  • Fei-Yue Wang, Chinese Academy of Sciences
  • MingYu Lei, Smart Education Working Committee of the Internet Society of China

Workshop Sponsors

We are grateful to Squirrel Ai Learning for sponsoring our workshop and supporting the best paper awards.