Abstract: Artificial Intelligence (AI) has emerged as a transformative tool for solving mathematical challenges across diverse domains, ranging from algebra and geometry to calculus and number theory. This study investigates the role of AI in mathematics by analyzing three representative platforms—MathGPT.org, Math-GPT.ai, and StudyX.ai—and by proposing ten Python-based problem-solving models tailored to Olympiad-style problems. The methodology integrates rule-based reasoning, brute-force search, and heuristic strategies, while benchmarking is inspired by the AI Math Olympiad (AIMO) Progress Award competition on Kaggle. A comparative evaluation was conducted to assess accuracy, reasoning depth, and computational efficiency. Results show that AI solvers can provide step-by-step solutions, interactive visualizations, and adaptive learning support, but their performance varies depending on problem type and strategy. This study highlights both the potential and limitations of AI in mathematics education and research, emphasizing the need for automated model selection (AutoML) and formal benchmarking to strengthen credibility. The findings demonstrate that AI can simultaneously promote automated problem-solving and enhance personalized STEM learning.

Trinh Quang Minh, Ngo Thi Lan, Bui Xuan Tung and Phan Thanh Tuyen. “AI Mathematical: Solving Math Challenges Using Artificial Intelligence Models”. International Journal of Advanced Computer Science and Applications (IJACSA) 17.1 (2026). http://dx.doi.org/10.14569/IJACSA.2026.0170112

@article{Minh2026,
title = {AI Mathematical: Solving Math Challenges Using Artificial Intelligence Models},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2026.0170112},
url = {http://dx.doi.org/10.14569/IJACSA.2026.0170112},
year = {2026},
publisher = {The Science and Information Organization},
volume = {17},
number = {1},
author = {Trinh Quang Minh and Ngo Thi Lan and Bui Xuan Tung and Phan Thanh Tuyen}
}