Authors : Dr. Suvarna Hindole; Akshata

Volume/Issue : Volume 10 - 2025, Issue 11 - November

Google Scholar : https://tinyurl.com/wcx7kvut

Scribd : https://tinyurl.com/fa3532k4

DOI : https://doi.org/10.38124/ijisrt/25nov773

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The rapid ascent of Artificial Intelligence (AI) has profoundly reshaped industries, research, and daily life, creating an unprecedented demand for skilled professionals. While programming proficiency and algorithmic understanding are often foregrounded in AI education, this research paper argues that a deep and robust mathematical foundation is not merely advantageous but critically indispensable for genuine comprehension, innovation, and ethical development within the field. This paper explores the specific mathematical disciplines—including linear algebra, calculus, probability and statistics, discrete mathematics, and optimization theory—that form the bedrock of modern AI methodologies, from machine learning to deep neural networks and reinforcement learning. We analyze current trends in undergraduate AI curricula, identify potential gaps in mathematical rigor, and propose pedagogical strategies and curriculum recommendations to integrate these essential mathematical concepts more effectively. By fostering a profound understanding of the mathematical underpinnings, undergraduate programs can empower students to transcend mere application, enabling them to design novel algorithms, interpret complex models, and navigate the evolving challenges of AI with true expertise.

Keywords : Artificial Intelligence, Machine Learning, Undergraduate Education, Mathematics, Linear Algebra, Calculus, Probability, Statistics, Optimization, Curriculum Development.

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