EDM 2024 Workshop: Leveraging Large Language Models for Next Generation Educational Technologies

Large Language Models (LLMs) have emerged as a powerful tool in Education Data Mining (EDM), offering novel approaches to analyzing educational data and enhancing learning experiences[1]. They provide novel opportunities in designing and augmenting learning environments, aiding students and educators in various aspects of the learning process, and helping researchers gain insight into a variety of domains within the field of education. LLMs have been increasingly integrated into learning platforms in a variety of ways to support students, teachers, and researchers. On the students' side, they offer new possibilities for adaptive learning, personalized tutors, and real-time feedback[5-7]. On the teachers' side, LLMs can generate interactive educational content, such as quizzes and simulations, that adapt to individual teaching preferences and hence assist teachers when generating lesson plans[8]. Moreover, LLMs serve as an effective tool for researchers in a variety of settings, including educational data generation and analysis[2]. 

However, LLMs also present new challenges, such as finding ways to effectively implement LLM-based technologies into educational environments to aid, rather than detract, students from learning and ensuring that no toxic or irrelevant content is presented to students. They also present a new host of ethical questions regarding the degree to which LLMs can go in aiding and providing feedback to students[3,4].

The goal of this workshop is to bring together researchers, educators, and practitioners to discuss these opportunities and challenges in leveraging LLMs for education.

This workshop will explore the various usages, implementations, and theories supporting the deployment of LLMs into online education. Further, we will examine the dangers, ethical considerations, and potential solutions related to the use of LLMs in education, ensuring a comprehensive understanding of their impact on learning environments. We also intend to explore the best ways to analyze the performance of LLMs, and use LLMs as tools for analysis. We invite papers (of 4 to 8 pages in EDM Proceedings format, not including references) addressing issues related to using LLMs in educational contexts and learning engineering platforms. The key issues and topics of interest for the workshop are listed below.

We invite papers to submit to the following easychair link by May 10: https://easychair.org/conferences/?conf=edm2024 

We will notify applicants by May 31st of their acceptance.

(To submit use the EDM submission link then submit to the LLM Workshop track)

Exploring the use of LLMs for adaptive learning and personalized feedback.

Exploring the use of LLMs for knowledge tracing.

Investigating ethical considerations when using LLMs for education.

Assessing the effectiveness of LLMs for learning.

Integrating LLMs into the teaching curriculum and online learning platforms.

Ensuring fairness and equitable access to LLMs.

Exploring the role of LLMs in collaborative learning environments.

Investigating the impact of LLMs on student motivation and engagement.

Examining the potential of LLMs for generating educational content and resources.

Training educators to effectively utilize LLMs in a professional environment and fostering skills needed.

Utilizing and fine-tuning open-source LLMs for usage in education. 

Neil Heffernan, nth@wpi.edu, Worcester Polytechnic Institute

Rose E. Wang, rewang@stanford.edu, Stanford University

Christopher MacLellan, cmaclellan3@gatech.edu, Georgia Tech

Arto Hellas, arto.hellas@aalto.fi, Aalto University

Chenglu Li, chenglu.li@utah.edu, University of Utah

Candace Walkington, cwalkington@mail.smu.edu, Southern Methodist University

Joshua Littenberg-Tobias, joshua_tobias@wgbh.org, WGBH Education

David Joyner, djoyner3@gatech.edu, Georgia Tech

Steven Moore, stevenmo@andrew.cmu.edu, Carnegie Mellon University

Adish Singla, adishs@mpi-sws.org, Max Planck Institute for Software Systems

Zach A. Pardos, pardos@berkeley.edu, University of California Berkeley

Maciej Pankiewicz, mpank@upenn.edu, University of Pennsylvania

Juho Kim, juhokim@kaist.ac.kr, KAIST

Shashank Sonkar, ss164@rice.edu, Rice University

Clayton Cohn, clayton.a.cohn@vanderbilt.edu, Vanderbilt University

Anthony Botelho, abotelho@coe.ufl.edu, University of Florida

Andrew Lan, andrewlan@cs.umass.edu, University of Massachusetts Amherst

Lan Jiang, lanj3@illinois.edu, University of Illinois at Urbana-Champaign

Mingyu Feng, mfeng@wested.org, WestEd

Tanja Käser, tanja.kaeser@epfl.ch, EPFL

Anna Rafferty, arafferty@carleton.edu, Carleton College

Eamon Worden, elworden@wpi.edu, Worcester Polytechnic Institute 

[1] P. Denny, S. Gulwani, N. T. Heffernan, T. Käser, S. Moore, A. N. Rafferty, and A. Singla. Generative AI for Education (GAIED): Advances, Opportunities, and Challenges. CoRR, abs/2402.01580, 2024.

[2] D. Hirunyasiri, D. R. Thomas, J. Lin, K. R. Koedinger, and V. Aleven. Comparative Analysis of GPT-4 and Human Graders in Evaluating Praise Given to Students in Synthetic Dialogues. CoRR, abs/2307.02018, 2023.

[3] E. Kasneci et al. ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education. Learning and Individual Differences, 103, 2023.

[4] M. Lee et al. Evaluating Human-Language Model Interaction. CoRR, abs/2212.09746, 2022.

[5] H. A. Nguyen, H. Stec, X. Hou, S. Di, and B. M. McLaren. Evaluating ChatGPT’s Decimal Skills and Feedback Generation in a Digital Learning Game. In European Conference on Technology Enhanced Learning, pages 278–293. Springer, 2023.

[6] M. Pankiewicz and R. S. Baker. Large Language Models (GPT) for Automating Feedback on Programming Assignments. In International Conference on Computers in Education Conference Proceedings, Volume I, pages 68–77, 2023.

[7] Z. A. Pardos and S. Bhandari. Learning gain differences between ChatGPT and human tutor generated algebra hints. arXiv preprint arXiv:2302.06871, 2023.

[8] Z. Wang, J. Valdez, D. Basu Mallick, and R. G. Baraniuk. Towards Human-like Educational Question Generation with Large Language Models. In International Conference on Artificial Intelligence in Education, pages 153–166. Springer, 2022.