Workshop on


AI for Education (AI4EDU): Advancing Personalized Education with LLM and Adaptive Learning

Introduction

Recent advanced AI technologies, especially large language models (LLMs) like GPTs, have significantly advanced the field of data mining and led to the development of various LLM-based applications. AI for education (AI4EDU) is a vibrant multi-disciplinary field of data mining, machine learning, and education, with increasing importance and extraordinary potential. In this field, LLM and adaptive learning-based models can be utilized as interfaces in human-in-the-loop education systems, where the model serves as a mediator among the teacher, students, and machine capabilities, including its own. This perspective 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, including the need for more robust and efficient algorithms, 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 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. Discuss the state-of-the-art technologies of adaptive learning and mining that tailor education to the individual needs, learning styles, proficiency levels, and problem areas of each student, for personalized learning experience. 3. Identify challenges and opportunities in using LLMs as both communication and collaboration interfaces in adaptive learning systems, educational games and intelligent educational assistants. 4. Explore ethical considerations and standardization issues in the use of LLMs. 5. Introduce and design new approaches such as prompt engineering, local fine tuning, integrated reasoning, and delegation framework for dialog-based systems that not only 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:

  • Mining multimodal data for comprehensive learning analytics in LLM-aided education.
  • Challenges and opportunities in integrating LLMs with existing adaptive learning systems.
  • Adaptive learning and mining systems and their applications in educational settings.
  • Predictive modeling in education using LLMs for student success and retention.
  • The potential of LLMs in education from both the theoretical and practical angles.
  • Ethical considerations in the use of LLMs as interfaces in educational settings, especially with AI standards committee.
  • Frameworks for standardization and benchmarking of LLMs in educational technology.
  • Data-driven approaches to curriculum development using LLM insights.
  • Data-driven approaches to curriculum development using LLM insights.
  • LLMs role in automated assessment and real-time feedback for students.
  • The future of LLMs in education: trends, potentials, and unforeseen consequences.

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 4-7 pages double-blind paper submissions from participants. 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. All submissions will be peer-reviewed. Some will be selected for spotlight talks, and some for the poster session.

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

Submission link: https://cmt3.research.microsoft.com/AI4EDUKDD2024

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

Key Dates

 

Workshop Paper Submission Due Date: May 28, 2024(AoE)

Notification of Paper Acceptance: June 28, 2024

Camera-ready Papers Due: July 05, 2024

KDD'24 AI4EDU Workshops: August 26, 2024

Workshop Schedule (Tentative):

Date: August 26, 2024


Location: TBD


Zoom Link: TBD

Time(GMT+8)TitleSpeaker
8:45 am - 9:00 amOpening Remarks
9:00 am - 9:30 amCoffee Break
9:30 am - 10:30 amKeynote Talk
10:30 am – 12:00 pmContributed oral talks
12:00pm – 1: 30 pmLunch Break
1:30 pm - 2:30 pmKeynote Talk  
2:30 pm – 3:30 pmPoster Session
3:30pm – 4:00pmCoffee Break 
4:15pm – 5:00pmPanel Discussion
5:00pm – 5:10pmClosing Remark
 

Keynote Speakers (TBD)




Speaker Name

Professor
University of X
Contact: xx@gmail.com

Title of the Talk

Abstract: xxx.

Short Bio:
XXX is a Professor at XX .

Accepted Papers

 

Accepted Papers (Oral)

Oral Paper Title Author list.

Accepted Papers (Poster)

Poster Paper Title Author list.

 

Workshop Organizers




Qingsong Wen

Head of AI Research & Chief Scientist
Squirrel Ai Learning
Contact: qingsongwen@squirrelai.com

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, Qualcomm, Marvell, etc., 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 over 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 @ KDD, 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

Co-Founder
Squirrel Ai Learning
Contact: joleenliang@squirrelai.com

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.

Carles Sierra

Professor & Director
IIIA of the Spanish National Research Council
Contact: sierra@iiia.csic.es

Short Bio: Carles Sierra is Research Professor and Director of the Artificial Intelligence Research Institute (IIIA) of the Spanish National Research Council (CSIC) located in the area of Barcelona. He is currently the President of EurAI, the European Association for Artificial Intelligence. He has previously worked at different universities in the UK and Australia. In the latter country he is an adjunct professor at Western Sydney University. He has taught at various universities, including the University of Technology Sydney, the Polit`ecnica de Val`encia, the Paris Descartes, or the Aut`onoma de Barcelona, among others. He has participated in more than 20 projects funded by the European Union and has secured more than 12M in funding throughout his career. He is or has been a member of various journal editorial boards, including those of AIJ and JAIR, two of the most prestigious generalist journals, and has been editor-in-chief of JAAMAS, which specializes in autonomous agents. He has published more than 300 articles that have received more than 20,000 citations. He organized IJCAI, the most important international artificial intelligence conference, in 2011 in Barcelona and was its Program Committee Chair in 2017 in Melbourne. He has been a member of the IFAAMAS committee that manages the reference congress on autonomous agents and multi-agent systems (AAMAS) of which he was Program Chair in 2004 and General Chair in 2009. He was the President of the Catalan artificial intelligence association for four years and its treasurer for eight. Since 2005 he is a Fellow of the European AI Association, EurAI. He received the ACM/SIGAI Autonomous Agents Research Award In 2019.

Rose Luckin

Professor
University College London
Contact: r.luckin@ucl.ac.uk

Short Bio: Rose Luckin is Professor of Learner Centred Design at the UCL Knowledge Lab in London. Her research involves the design and evaluation of educational technology using theories from the learning sciences and techniques from Artificial Intelligence (AI). She has a particular interest in using AI to open up the 'black box' of learning to show teachers and students the detail of their progress intellectually, emotionally and socially. Rose is also Director of EDUCATE, a London hub for Educational Technology StartUps, researchers and educators to work together on the development of evidence-informed Educational Technology. Rose was named on the Seldon List 2017 as one of the 20 most influential people in Education. She is a UFI charity trustee, a governor and trustee of St Paul's school in London and a governor of the Self-Managed Learning College in Brighton. She has taught in the state secondary, Further Education and Higher Education sectors, and she was previously Pro-Vice Chancellor for Teaching and Learning at the University of Sussex.

Richard Tong

Chief Architect
Squirrel Ai Learning
Contact: richard.tong@ieee.org

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.

Zitao Liu

Professor
Jinan University
Contact: liuzitao@jnu.edu.cn

Short Bio: Zitao Liu is Dean of Guangdong Institute of Smart Education, Jinan University, Guangzhou, China. His research is in the area of machine learning, and includes contributions in the areas of artificial intelligence in education, multimodal knowledge representation and user modeling. He has published his research in highly ranked conference proceedings, such as AAAI, WWW, AIED, Ubicomp, etc. and serves as the executive committee of the International AI in Education Society and top tier AI conference/workshop organizers/program committees. Before that, joining Zitao worked at TAL and Pinterest, and received his Ph.D degree in Computer Science from University of Pittsburgh.

Peng Cui

Associate Professor
Tsinghua University
Contact: cuip@tsinghua.edu.cn

Short Bio: Dr. Peng Cui is an Associate Professor with tenure in Tsinghua University. He got his PhD degree from Tsinghua University in 2010. His research interests include causally-regularized machine learning, network representation learning, and social dynamics modeling. He has published more than 100 papers in prestigious conferences and journals in data mining and multimedia. His recent research won the IEEE Multimedia Best Department Paper Award, SIGKDD 2016 Best Paper Finalist, ICDM 2015 Best Student Paper Award, SIGKDD 2014 Best Paper Finalist, IEEE ICME 2014 Best Paper Award, ACM MM12 Grand Challenge Multimodal Award, and MMM13 Best Paper Award. He is PC co-chair of CIKM2019 and MMM2020, SPC or area chair of ICML, KDD, WWW, IJCAI, AAAI, etc., and Associate Editors of IEEE TKDE, IEEE TBD, ACM TIST, and ACM TOMM etc. He received ACM China Rising Star Award in 2015, and CCF-IEEE CS Young Scientist Award in 2018. He is now a Distinguished Member of ACM and CCF, and a Senior Member of IEEE.

Jiliang Tang

University Foundation Professor
Michigan State University
Contact: tangjili@msu.edu

Short Bio: Jiliang Tang is a University Foundation Professor in the computer science and engineering department at Michigan State University. He got one early promotion to associate professor at 2021 and then a promotion to full professor (designated as MSU foundation professor) at 2022. Before that, he was a research scientist in Yahoo Research and got his PhD from Arizona State University in 2015 under Dr. Huan Liu. His research interests include graph machine learning, trustworthy AI and their applications in education and biology. He was the recipient of various awards including 2022 AI's 10 to Watch, 2022 IAPR J. K. AGGARWAL Award, 2022 SIAM/IBM Early Career Research Award, 2021 IEEE ICDM Tao Li Award, 2021 IEEE Big Data Security Junior Research Award, 2020 ACM SIGKDD Rising Star Award, 2020 Distinguished Withrow Research Award, 2019 NSF Career Award, and 8 best paper awards (or runner-ups). His dissertation won the 2015 KDD Best Dissertation runner up and Dean's Dissertation Award. He serves as conference organizers (e.g., KDD, SIGIR, WSDM and SDM) and journal editors (e.g., TKDD, TOIS and TKDE). He has published his research in highly ranked journals and top conference proceedings, which have received tens of thousands of citations with h-index 88 (Google Scholar) and extensive media coverage.

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.