Mathematical Sciences Department Numerical Methods Seminar - Zhongqiang Zhang

Monday, August 24, 2026
12:00 p.m. to 1:00 p.m.
Location
Floor/Room #
202

Numerical Methods Seminar
 

Monday, August 24

12-1pm

Stratton 202
 

Speaker: Zhongqiang Zhang
 

Title: Integrating AI into Teaching and Learning: A Redesign of MA 3257 / CS 4032

Abstract: The growing use of artificial intelligence in mathematical problem-solving, programming, and technical communication has motivated the thoughtful integration of AI into numerical methods courses.

During summer 2026, our PhD student Oluwaseyi Idegbekwu carried out a project to redesign MA 3257 / CS 4032, Numerical Methods for Linear and Nonlinear Systems, under my supervision. The project focused on responsible AI use, verification of AI-generated output, and deeper student understanding of fundamental concepts such as conditioning, stability, convergence, residuals, and computational methods.

The project produced a substantial collection of revised course resources, including lecture notes, slide decks, homework assignments, quizzes, classroom activities, a comprehensive final examination, a course cheatsheet, and separate AI-use guides for students and instructors. A documented workflow—combining prompt design, AI-assisted drafting, instructor review, student-perspective critique, human verification, revision, and LaTeX compilation—was used to improve mathematical accuracy, clarity, consistency, and classroom usability.

The AI-use guides provide practical strategies for evaluating AI-generated mathematics and MATLAB code. The project also documents recurring AI failure cases, including missing assumptions, inconsistent notation, unsupported convergence claims, and solutions that appear plausible but are numerically unreliable.

These resources establish a foundation for incorporating explicit AI-verification tasks more broadly into future homework, classroom activities, and assessments. They also provide the department and the wider STEM community with a practical starting point for developing courses that promote AI literacy while preserving mathematical reasoning, academic integrity, and human responsibility.