Three Questions Concerning the Use of Large Language Models to Facilitate Mathematics Learning

An Zi Yen; Wei Ling Hsu

Three Questions Concerning the Use of Large Language Models to Facilitate Mathematics Learning

An Zi Yen^*, Wei Ling Hsu

^*Corresponding author for this work

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

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.

Original language	English
Title of host publication	Findings of the Association for Computational Linguistics
Subtitle of host publication	EMNLP 2023
Publisher	Association for Computational Linguistics (ACL)
Pages	3055-3069
Number of pages	15
ISBN (Electronic)	9798891760615
State	Published - 2023
Event	2023 Findings of the Association for Computational Linguistics: EMNLP 2023 - Singapore, Singapore Duration: 6 Dec 2023 → 10 Dec 2023

Publication series

Name	Findings of the Association for Computational Linguistics: EMNLP 2023

Conference

Conference	2023 Findings of the Association for Computational Linguistics: EMNLP 2023
Country/Territory	Singapore
City	Singapore
Period	6/12/23 → 10/12/23

Cite this

@inproceedings{2968041343d64f34811da7e1024c205a,

title = "Three Questions Concerning the Use of Large Language Models to Facilitate Mathematics Learning",

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.",

author = "Yen, {An Zi} and Hsu, {Wei Ling}",

note = "Publisher Copyright: {\textcopyright} 2023 Association for Computational Linguistics.; 2023 Findings of the Association for Computational Linguistics: EMNLP 2023 ; Conference date: 06-12-2023 Through 10-12-2023",

year = "2023",

language = "English",

series = "Findings of the Association for Computational Linguistics: EMNLP 2023",

publisher = "Association for Computational Linguistics (ACL)",

pages = "3055--3069",

booktitle = "Findings of the Association for Computational Linguistics",

address = "美國",

}

Yen, AZ & Hsu, WL 2023, Three Questions Concerning the Use of Large Language Models to Facilitate Mathematics Learning. in Findings of the Association for Computational Linguistics: EMNLP 2023. Findings of the Association for Computational Linguistics: EMNLP 2023, Association for Computational Linguistics (ACL), pp. 3055-3069, 2023 Findings of the Association for Computational Linguistics: EMNLP 2023, Singapore, Singapore, 6/12/23.

Three Questions Concerning the Use of Large Language Models to Facilitate Mathematics Learning. / Yen, An Zi; Hsu, Wei Ling.
Findings of the Association for Computational Linguistics: EMNLP 2023. Association for Computational Linguistics (ACL), 2023. p. 3055-3069 (Findings of the Association for Computational Linguistics: EMNLP 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Hsu, Wei Ling

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N2 - 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.

AB - 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.

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BT - Findings of the Association for Computational Linguistics

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Three Questions Concerning the Use of Large Language Models to Facilitate Mathematics Learning

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