This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.
Nazia Perween,
Atique-ur-Rahman,
- Professor, Department of Mathematics, Al-Kabir Institute of Technology and Management, Jharkhand, India
- Professor, Department of Mathematics, Al-Kabir Institute of Technology and Management, Jharkhand, India
Abstract
Artificial Intelligence (AI) has transformed numerous scientific fields, yet its integration with classical mathematics such as trigonometry is still emerging. This paper explores how AI enhances trigonometric problem solving, learning, and real-world applications. We analyse AI-driven tools for teaching trigonometry, AI in geometric and spatial reasoning, usage in robotics and computer vision, and future directions for research. Key challenges, methodologies, and case studies are discussed to provide a comprehensive overview of the current interdisciplinary landscape. Artificial Intelligence (AI) has significantly transformed scientific computation and mathematical applications, creating new opportunities for integrating classical mathematical disciplines with modern intelligent systems. Trigonometry, a foundational branch of mathematics concerned with relationships between angles, sides of triangles, and periodic functions, plays a critical role in numerous AI-driven technologies. This research paper examines the interdisciplinary relationship between AI and trigonometry, focusing on computational enhancement, educational innovation, and real-world technological applications. The study analyses how symbolic AI systems automate trigonometric identity simplification and equation solving, while machine learning models approximate trigonometric functions for efficient numerical computation. It further explores neural-symbolic integration, where rule-based reasoning and deep learning methods are combined to achieve both mathematical precision and adaptive flexibility. In educational settings, AI-powered intelligent tutoring systems personalize trigonometry instruction, diagnose conceptual misunderstandings, and improve learning outcomes through adaptive assessment and real-time feedback mechanisms. In applied domains, AI-enhanced trigonometric modeling is essential in robotics, computer vision, navigation systems, and signal processing. Applications such as inverse kinematics in robotic manipulators, triangulation in stereo vision, and depth estimation in autonomous systems rely heavily on trigonometric principles optimized by AI algorithms. Despite these advancements, several challenges persist, including model interpretability, symbolic–numerical trade-offs, data dependency, and ethical considerations in academic environments. The paper concludes by identifying future research directions, including explainable AI for symbolic mathematics, hybrid computational architectures, AI-assisted discovery of trigonometric identities, and immersive visualization technologies. Overall, the study demonstrates that AI not only utilizes trigonometric foundations but also enhances their computational efficiency, accessibility, and applicability in modern intelligent systems.
Keywords: Artificial Intelligence, Trigonometry, Machine Learning, Neural Networks, Computer Vision
References
1. Russel, S., & Norvig, P. (2016). Artificial Intelligence: A Modern Approach. Pearson.
2. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
3. Koedinger, K. R., et al. (2015). “Intelligent Tutoring Goes to School in the Big City.” International
Journal of Artificial Intelligence in Education.
4. Silver, D., et al. (2017). “Mastering Chess and Shogi by Self-Play with a General Reinforcement
Learning Algorithm.” Nature.
5. Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction. MIT Press.
6. LeCun, Y., Bengio, Y., & Hinton, G. (2015). “Deep Learning.” Nature.
7. Hartley, R., & Zisserman, A. (2003). Multiple View Geometry in Computer Vision. Cambridge
University Press.
8. Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.
Journal of Artificial Intelligence Research & Advances
| Volume | 13 | |
| 03 | ||
| Received | 10/06/2026 | |
| Accepted | 19/08/2026 | |
| Published | 30/09/2026 | |
| Publication Time | 112 Days |