@inproceedings{yen-hsu-2023-three,
title = "Three Questions Concerning the Use of Large Language Models to Facilitate Mathematics Learning",
author = "Yen, An-Zi and
Hsu, Wei-Ling",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.201",
doi = "10.18653/v1/2023.findings-emnlp.201",
pages = "3055--3069",
abstract = "Due to the remarkable language understanding and generation abilities of large language models (LLMs), their use in educational applications has been explored. However, little work has been done on investigating the pedagogical ability of LLMs in helping students to learn mathematics. In this position paper, we discuss the challenges associated with employing LLMs to enhance students{'} mathematical problem-solving skills by providing adaptive feedback. Apart from generating the wrong reasoning processes, LLMs can misinterpret the meaning of the question, and also exhibit difficulty in understanding the given questions{'} rationales when attempting to correct students{'} answers. Three research questions are formulated.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="yen-hsu-2023-three">
<titleInfo>
<title>Three Questions Concerning the Use of Large Language Models to Facilitate Mathematics Learning</title>
</titleInfo>
<name type="personal">
<namePart type="given">An-Zi</namePart>
<namePart type="family">Yen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Wei-Ling</namePart>
<namePart type="family">Hsu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2023-12</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Findings of the Association for Computational Linguistics: EMNLP 2023</title>
</titleInfo>
<name type="personal">
<namePart type="given">Houda</namePart>
<namePart type="family">Bouamor</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Juan</namePart>
<namePart type="family">Pino</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Kalika</namePart>
<namePart type="family">Bali</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Singapore</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Due to the remarkable language understanding and generation abilities of large language models (LLMs), their use in educational applications has been explored. However, little work has been done on investigating the pedagogical ability of LLMs in helping students to learn mathematics. In this position paper, we discuss the challenges associated with employing LLMs to enhance students’ mathematical problem-solving skills by providing adaptive feedback. Apart from generating the wrong reasoning processes, LLMs can misinterpret the meaning of the question, and also exhibit difficulty in understanding the given questions’ rationales when attempting to correct students’ answers. Three research questions are formulated.</abstract>
<identifier type="citekey">yen-hsu-2023-three</identifier>
<identifier type="doi">10.18653/v1/2023.findings-emnlp.201</identifier>
<location>
<url>https://aclanthology.org/2023.findings-emnlp.201</url>
</location>
<part>
<date>2023-12</date>
<extent unit="page">
<start>3055</start>
<end>3069</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Three Questions Concerning the Use of Large Language Models to Facilitate Mathematics Learning
%A Yen, An-Zi
%A Hsu, Wei-Ling
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F yen-hsu-2023-three
%X Due to the remarkable language understanding and generation abilities of large language models (LLMs), their use in educational applications has been explored. However, little work has been done on investigating the pedagogical ability of LLMs in helping students to learn mathematics. In this position paper, we discuss the challenges associated with employing LLMs to enhance students’ mathematical problem-solving skills by providing adaptive feedback. Apart from generating the wrong reasoning processes, LLMs can misinterpret the meaning of the question, and also exhibit difficulty in understanding the given questions’ rationales when attempting to correct students’ answers. Three research questions are formulated.
%R 10.18653/v1/2023.findings-emnlp.201
%U https://aclanthology.org/2023.findings-emnlp.201
%U https://doi.org/10.18653/v1/2023.findings-emnlp.201
%P 3055-3069
Markdown (Informal)
[Three Questions Concerning the Use of Large Language Models to Facilitate Mathematics Learning](https://aclanthology.org/2023.findings-emnlp.201) (Yen & Hsu, Findings 2023)
ACL