Learning Mathematics with Large Language Models: A Comparative Study with Computer Algebra Systems and Other Tools

Nikolaos Matzakos; Spyridon Doukakis; Maria Moundridou

doi:10.3991/ijet.v18i20.42979

Learning Mathematics with Large Language Models

A Comparative Study with Computer Algebra Systems and Other Tools

Authors

Nikolaos Matzakos School of Pedagogical and Technological Education: Athens, GR https://orcid.org/0000-0001-8647-6082
Spyridon Doukakis Ionian University: Corfu, GR
Maria Moundridou School of Pedagogical and Technological Education: Athens, GR https://orcid.org/0000-0002-5463-2041

DOI:

https://doi.org/10.3991/ijet.v18i20.42979

Keywords:

Large Language Models (LLMs), ChatGPT, Bard Experiment, Computer Algebra Systems (CAS), Maxima, Computational Knowledge Engines, Wolfram|Alpha, Mathematical Education, Higher Education

Abstract

Artificial intelligence (AI) has permeated all human activities, bringing about significant changes and creating new scientific and ethical challenges. The field of education could not be an exception to this development. OpenAI’s unveiling of ChatGPT, their large language model (LLM), has sparked significant interest in the potential applications of this technology in education. This paper aims to contribute to the ongoing discussion on the role of AI in education and its potential implications for the future of learning by exploring how LLMs could be utilized in the teaching of mathematics in higher education and how they compare to the currently widely used computer algebra systems (CAS) and other mathematical tools. It argues that these innovative tools have the potential to provide functional and pedagogical opportunities that may influence changes in curriculum and assessment approaches.

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Published

2023-10-17

How to Cite

Matzakos, N., Doukakis, S., & Moundridou, M. (2023). Learning Mathematics with Large Language Models: A Comparative Study with Computer Algebra Systems and Other Tools. International Journal of Emerging Technologies in Learning (iJET), 18(20), pp. 51–71. https://doi.org/10.3991/ijet.v18i20.42979

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Issue

Vol. 18 No. 20 (2023)

Section

Papers

License

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