2023
EMNLP
EMNLP 2023
Three Questions Concerning the Use of Large Language Models to Facilitate Mathematics Learning
Abstract
AbstractDue 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.
🌉
Interdisciplinary Bridge
— Artificial Intelligence and Interdisciplinary and Machine Learning and Natural Language Processing
🧭
Keyword Pioneer
— mathematics learning
🐣
Hot Topic Early Bird
— educational technology
🐝
Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Artificial Intelligence > Core AI > Human-AI Interaction
Natural Language Processing > Generation > Language Modeling
Natural Language Processing > Resources & Methods > Large Language Models
Interdisciplinary > Social > Education
Machine Learning > Learning Types > Deep Learning
Interdisciplinary > Education