@inproceedings{arana-etal-2025-foundations,
title = "Foundations of {PEERS}: Assessing {LLM} Role Performance in Educational Simulations",
author = "Arana, Jasper Meynard and
Carandang, Kristine Ann M. and
Casin, Ethan Robert and
Alis, Christian and
Tan, Daniel Stanley and
Legara, Erika Fille and
Monterola, Christopher",
editor = "Zhao, Jin and
Wang, Mingyang and
Liu, Zhu",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-srw.66/",
doi = "10.18653/v1/2025.acl-srw.66",
pages = "908--918",
ISBN = "979-8-89176-254-1",
abstract = "In education, peer instruction (PI) is widely recognized as an effective active learning strategy. However, real-world evaluations of PI are often limited by logistical constraints and variability in classroom settings. This paper introduces PEERS (Peer Enhanced Educational Realistic Simulation), a simulation framework that integrates Agent-Based Modeling (ABM), Large Language Models (LLMs), and Bayesian Knowledge Tracing (BKT) to emulate student learning dynamics. As an initial step, this study focuses on evaluating whether LLM-powered agents can effectively assume the roles of teachers and students within the simulation. Human evaluations and topic-based metrics show that LLMs can generate role-consistent and contextually appropriate classroom dialogues. These results serve as a foundational milestone toward building realistic, AI-driven educational simulations. Future work will include simulating the complete PEERS framework and validating its accuracy through actual classroom-based PI sessions. This research aims to contribute a scalable, cost-effective methodology for studying instructional strategies in controlled yet realistic environments."
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<abstract>In education, peer instruction (PI) is widely recognized as an effective active learning strategy. However, real-world evaluations of PI are often limited by logistical constraints and variability in classroom settings. This paper introduces PEERS (Peer Enhanced Educational Realistic Simulation), a simulation framework that integrates Agent-Based Modeling (ABM), Large Language Models (LLMs), and Bayesian Knowledge Tracing (BKT) to emulate student learning dynamics. As an initial step, this study focuses on evaluating whether LLM-powered agents can effectively assume the roles of teachers and students within the simulation. Human evaluations and topic-based metrics show that LLMs can generate role-consistent and contextually appropriate classroom dialogues. These results serve as a foundational milestone toward building realistic, AI-driven educational simulations. Future work will include simulating the complete PEERS framework and validating its accuracy through actual classroom-based PI sessions. This research aims to contribute a scalable, cost-effective methodology for studying instructional strategies in controlled yet realistic environments.</abstract>
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%0 Conference Proceedings
%T Foundations of PEERS: Assessing LLM Role Performance in Educational Simulations
%A Arana, Jasper Meynard
%A Carandang, Kristine Ann M.
%A Casin, Ethan Robert
%A Alis, Christian
%A Tan, Daniel Stanley
%A Legara, Erika Fille
%A Monterola, Christopher
%Y Zhao, Jin
%Y Wang, Mingyang
%Y Liu, Zhu
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-254-1
%F arana-etal-2025-foundations
%X In education, peer instruction (PI) is widely recognized as an effective active learning strategy. However, real-world evaluations of PI are often limited by logistical constraints and variability in classroom settings. This paper introduces PEERS (Peer Enhanced Educational Realistic Simulation), a simulation framework that integrates Agent-Based Modeling (ABM), Large Language Models (LLMs), and Bayesian Knowledge Tracing (BKT) to emulate student learning dynamics. As an initial step, this study focuses on evaluating whether LLM-powered agents can effectively assume the roles of teachers and students within the simulation. Human evaluations and topic-based metrics show that LLMs can generate role-consistent and contextually appropriate classroom dialogues. These results serve as a foundational milestone toward building realistic, AI-driven educational simulations. Future work will include simulating the complete PEERS framework and validating its accuracy through actual classroom-based PI sessions. This research aims to contribute a scalable, cost-effective methodology for studying instructional strategies in controlled yet realistic environments.
%R 10.18653/v1/2025.acl-srw.66
%U https://aclanthology.org/2025.acl-srw.66/
%U https://doi.org/10.18653/v1/2025.acl-srw.66
%P 908-918
Markdown (Informal)
[Foundations of PEERS: Assessing LLM Role Performance in Educational Simulations](https://aclanthology.org/2025.acl-srw.66/) (Arana et al., ACL 2025)
ACL
- Jasper Meynard Arana, Kristine Ann M. Carandang, Ethan Robert Casin, Christian Alis, Daniel Stanley Tan, Erika Fille Legara, and Christopher Monterola. 2025. Foundations of PEERS: Assessing LLM Role Performance in Educational Simulations. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop), pages 908–918, Vienna, Austria. Association for Computational Linguistics.