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DTSTART:20070311T020000
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DTSTART;TZID=America/New_York:20260824T120000
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URL;TYPE=URI:https://www.wpi.edu/news/calendar/events/mathematical-sciences
 -department-numerical-methods-seminar-zhongqiang-zhang
SUMMARY:Mathematical Sciences Department Numerical Methods Seminar - Zhongq
 iang Zhang
DESCRIPTION:Numerical Methods Seminar\nMonday, August 24\n12-1pm\nStratton 
 202\nSpeaker: Zhongqiang Zhang\nTitle: Integrating AI into Teaching and Le
 arning: A Redesign of MA 3257 / CS 4032\nAbstract: The growing use of arti
 ficial intelligence in mathematical problem-solving, programming, and tech
 nical communication has motivated the thoughtful integration of AI into nu
 merical methods courses.\nDuring summer 2026, our PhD student Oluwaseyi Id
 egbekwu carried out a project to redesign MA 3257 / CS 4032, Numerical Met
 hods for Linear and Nonlinear Systems, under my supervision. The project f
 ocused on responsible AI use, verification of AI-generated output, and dee
 per student understanding of fundamental concepts such as conditioning, st
 ability, convergence, residuals, and computational methods.\nThe project p
 roduced a substantial collection of revised course resources, including le
 cture notes, slide decks, homework assignments, quizzes, classroom activit
 ies, a comprehensive final examination, a course cheatsheet, and separate 
 AI-use guides for students and instructors. A documented workflow—combinin
 g prompt design, AI-assisted drafting, instructor review, student-perspect
 ive critique, human verification, revision, and LaTeX compilation—was used
  to improve mathematical accuracy, clarity, consistency, and classroom usa
 bility.\nThe AI-use guides provide practical strategies for evaluating AI-
 generated mathematics and MATLAB code. The project also documents recurrin
 g AI failure cases, including missing assumptions, inconsistent notation, 
 unsupported convergence claims, and solutions that appear plausible but ar
 e numerically unreliable.\nThese resources establish a foundation for inco
 rporating 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 developin
 g courses that promote AI literacy while preserving mathematical reasoning
 , academic integrity, and human responsibility.\n
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