The integration of artificial intelligence (AI) into calculus education and research represents a paradigm shift in how mathematical concepts are taught, learned, and discovered. This article presents a comprehensive review of contemporary AI tools designed for calculus applications, categorizing them into three primary domains: learning and tutoring platforms, automated assessment systems, and research assistants for mathematical discovery. Through systematic analysis of twelve representative tools—including AxiomProver, AlphaEvolve, CalcTutor, and various interactive learning platforms—we examine their architectural foundations, pedagogical approaches, and empirical performance metrics. The review synthesizes recent benchmark studies evaluating large language models on calculus tasks, revealing that while current systems achieve up to 94.71% accuracy on procedural differentiation problems, significant limitations persist in conceptual understanding and complex problem-solving contexts. We conclude by proposing a framework for tool selection based on user objectives and discussing implications for the future of mathematical pedagogy and research methodology.
Mr. M. Naveen Raj (2026) studied this question.