Educators and students in the age of artificial intelligence: A qualitative study of mathematics education in community colleges
Tran, Duy Q.
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Permalink
https://hdl.handle.net/2142/132633
Description
Title
Educators and students in the age of artificial intelligence: A qualitative study of mathematics education in community colleges
Author(s)
Tran, Duy Q.
Issue Date
2025-10-29
Director of Research (if dissertation) or Advisor (if thesis)
Cope, William
Doctoral Committee Chair(s)
Cope, William
Kalantzis, Mary
Committee Member(s)
Magee, Liam
Bruno, Paul
Department of Study
Educ Policy, Orgzn & Leadrshp
Discipline
Educ Policy, Orgzn & Leadrshp
Degree Granting Institution
University of Illinois Urbana-Champaign
Degree Name
Ed.D.
Degree Level
Dissertation
Keyword(s)
Artificial Intelligence in Education, Community College Mathematics, Synchronous and Asynchronous Learning, Technology Adoption, Pedagogical Strategies, Educational Equity, Academic Integrity, Qualitative Research
Abstract
This qualitative phenomenological study investigated how community college mathematics instructors perceive the use and impact of AI tools on their instructional practices, and what the implications are for enhancing teaching effectiveness and delivery, alongside how students perceive the influence of these tools on their engagement, understanding of mathematical concepts, and overall learning outcomes, and what the implications are for student success in mathematics courses. Additionally, it explored how faculty and student perceptions of AI tool usage differ across synchronous and asynchronous learning environments at four U.S. colleges in Arizona, Illinois, and California, and what the implications are for modality-specific pedagogical strategies, guided by the Unified Theory of Acceptance and Use of Technology (UTAUT) and enriched by Teaching for Robust Understanding, Realistic Mathematics Education, Critical Mathematics Education, and Community of Inquiry frameworks. Data, collected in 2025 through 23 semi-structured Zoom interviews with faculty and 54 open-ended surveys with students, were analyzed using Braun and Clarke’s (2006) thematic analysis in NVivo, involving a diverse sample of 23 faculty and 54 students. The study uncovered six faculty themes, including instructional efficiency, engagement, academic integrity concerns, institutional support, critical thinking/ethical issues, and modality-specific impacts, and six parallel student themes, such as engagement/efficiency, limited conceptual understanding, ethical use/dependency risks, social influences, access/equity challenges, and modality-specific impacts, revealing a complex interplay of benefits and challenges. AI tools enhanced grading and visualization, yet posed risks of over-reliance and widened equity gaps, notably for the 42.6% first-generation students in asynchronous settings where 82.6% of faculty and 75.9% of students were enrolled. The influence of California’s Assembly Bill 1705, which restricts remedial courses, highlighted AI and open educational resources as vital supports for transfer-level mathematics. These findings suggest exploratory pathways for faculty training, modality-specific policies, and equitable AI integration, contributing to the development of pedagogy and policy in community college mathematics education while inviting further research to validate these implications.
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