Date of Award

Spring 3-26-2025

Document Type

Honors Thesis

Department/Major

Computer Science

Additional Department

Sociology

First Advisor

Isaiah F. A. Cohen

Second Advisor

KC Santosh

Third Advisor

Zachary Tschetter

Keywords

AI, higher education, personalized learning, ethical, mixed method, crossdisciplinary

Subject Categories

Artificial Intelligence and Robotics | Educational Sociology

Abstract

Artificial Intelligence (AI) is redefining higher education, captivating scholars with its promise to personalize learning and streamline institutions. However, underneath this assurance exists a network of ethical challenges, disparities in equity, and inquiries regarding academic integrity that require our focus. In pursuit of this goal, this research employs a mixed-methods strategy—through the implementation of surveys and semi-structured interviews—to investigate the transformative effects of AI on higher education, concentrating on its repercussions for teaching techniques, learning results, and institutional processes. This study’s findings indicate that AI can personalize educational experiences to meet individual needs, ease course administrative workload, and assist with the implementation of innovative pedagogy. Some respondents, however, also expressed concerns about academic integrity, excess student dependence on technology, and expanding educational inequities. Respondent familiarity with AI was noted to be associated with favorable views regarding AI's application in education alongside a significant sociodemographic divide in both familiarity and perspective. Connections between gender, race, and sexual orientation emphasize the significant and diverse effects of familiarity and perspectives on AI, revealing further inequalities that require attention in order to promote fair AI integration.

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