Transformative Synergy: Exploring the Intersection of AI and Math Education (79817)

Session Information: Mathematics Teaching Experiences
Session Chair: Erh-Tsung Chin

Sunday, 14 July 2024 16:30
Session: Session 5
Room: G08 (Ground)
Presentation Type:Oral Presentation

All presentation times are UTC0 (Europe/London)

The presentation elucidates the varied applications of AI in math education. It investigates the dynamic interplay between Artificial Intelligence (AI) and mathematics education, examining the transformative potential that emerges when these two fields intersect. In my numerical analysis course, Artificial Intelligence (AI) was integrated to enhance the learning experience and facilitate problem-solving and code-writing. Not all equations involving a single variable can be solved analytically, and finding integrals and derivatives isn't always straightforward. Numerical tools provide methods for solving equations and computing integrals/derivatives, but with the integration of AI, these tools have become faster and more comprehensive, offering all details with a single click. This demonstrates that students were able to approach complex mathematical problems with greater efficiency and effectiveness. For example, AI-powered symbolic math solvers were used in the course to guide students through intricate mathematical equations step-by-step, aiding in their understanding and solution process. Moreover, and instead of basic programing languages, predictive analytics models were employed to anticipate students' performance and customize interventions accordingly. In addition to AI-driven tutoring systems, coding solutions for equations, integrals, derivatives, became simpler, with mistakes easily identified and corrected. As result, the integration of AI in the numerical analysis course not only enriched students' understanding of mathematical concepts but also equipped them with valuable coding skills in utilizing cutting-edge technologies for problem-solving in mathematical contexts. Furthermore, there has been an improvement of 38% in the course learning outcomes, with 85% of students reporting increased comfort and familiarity with mathematics and computational/coding skills.

Authors:
Roger Nakad, Notre Dame University Lebanon, Lebanon


About the Presenter(s)
Dr. Roger Nakad is currently an Associate Professor in Mathematics and the Interim Dean of the Faculty of Natural and Applied Sciences at Notre Dame University Louaize-NDU-Lebanon

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Posted by Clive Staples Lewis

Last updated: 2023-02-23 23:45:00