Prospect and Challenges of Using Artificial Intelligence in Research and Development of Mathematics Education: A Systematic Literature Review.
Artificial Intelligence (AI) is revolutionizing communication and teaching in mathematics education. The goals of this paper are to investigate potential outcomes of the introduction of AI into mathematics education and the implications that this may have for the future of mathematics education. The outcomes conceal the intrusion consequences of AI potentials and challenges in mathematics education development through machine language, accepted language processing, technology and educational skills, ChatGPT, social robots, smart learning, tutoring, text generation, collaborative learning, educational assessment, and mathematics development. We demonstrate Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). We searched Google Scholar, Web of Science, Taylor & Francis Online, Scopus, IEEE XPLORE, Science Direct, ProQuest, and Springer Link for AI research published between 2021 and 2024. The findings of the Systematic Literature Review (SLR) from the sample studied indicate the capabilities of AI across subject areas, challenges and difficulties, stages of test development, the influential and innovative power of AI tools, and solving skills through creative thinking. Following an additional removal of duplicates from the study and quality assessment screening of the papers, 14 publications out of 40 papers satisfied the exclusion/inclusion and filtering criteria. Most of the reviewed studies used mixed and quantitative methods. The authors perceived, accomplished, and recommend that AI should be used to modify the content-based needs of students across all levels of education and specifically in mathematics education courses like EDME 206, 208, 303, emotional connection and intelligence to rupture inventiveness in learning mathematics through a qualitative approach among others. Keyword: Mathematics education; Artificial intelligence; Creative thinking; National development; Systematic
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2024-12-31
- DOI
- https://doi.org/10.5281/zenodo.15080393
- Primary Topic
- Intelligent Tutoring Systems and Adaptive Learning
- Type
- article
- Field-Weighted Citation Impact
- 0.00