Theses and Dissertations

Date of Award

8-1-2026

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Mathematics

First Advisor

Hortensia Alzaga-Elizondo

Second Advisor

Younggon Bae

Third Advisor

Kaitlyn S. Serbin

Abstract

This qualitative study examined how five university mathematics teachers evaluated and modified lesson plans generated by ChatGPT and how their revisions reflected Mathematical Knowledge for Teaching (MKT). Data were collected through individual semi-structured interviews and the original and modified lesson-plan documents. During the interviews, participants generated lesson plans on mathematical topics, identified features they liked and disliked, modified the plans, and explained their decisions. The data were analyzed through initial coding, comparison of evidence across participants, and development of themes. The findings showed that teachers viewed AI-generated lesson plans as useful starting points rather than materials that were ready to use without review. They valued instructional questions, alignment with learning goals, conceptual support, student involvement, attention to student difficulties, and practical guidance. They also identified concerns about lesson sequence, prior knowledge, practice, conceptual depth, instructional style, and real-life relevance. Teachers relied on professional judgment and MKT when evaluating and revising the plans.

Comments

Copyright 2026 Susmita Pathak. All Rights Reserved. https://proquest.com/docview/3409041734

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