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.- Anthology ID:
- 2023.findings-emnlp.201
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2023
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3055–3069
- Language:
- URL:
- https://aclanthology.org/2023.findings-emnlp.201
- DOI:
- 10.18653/v1/2023.findings-emnlp.201
- Cite (ACL):
- An-Zi Yen and Wei-Ling Hsu. 2023. Three Questions Concerning the Use of Large Language Models to Facilitate Mathematics Learning. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 3055–3069, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Three Questions Concerning the Use of Large Language Models to Facilitate Mathematics Learning (Yen & Hsu, Findings 2023)
- PDF:
- https://preview.aclanthology.org/aacl-23-doi-ingestion/2023.findings-emnlp.201.pdf