Abstract

The growing integration of Generative Artificial Intelligence (GAI) in higher education raises important questions about its role in supporting college students during the writing process. I used a mixed-methods approach, with a focus on quantitative questionnaires and qualitative written artifacts, to analyze patterns of GAI usage and perceptions by testing 34 college students. My thesis will explore these questions: (1) How do college students approach writing tasks in college?, (2) How do college students utilize generative AI to assist with writing tasks?, (3) What are the perceptions of college students on the use of generative AI to assist with writing tasks?, and (4) How confident are college students in the quality of their writing with and without the use of generative AI? The findings indicate that students utilize certain activities related to the writing process including planning which was mentioned by all participants during writing session #1. Planning and GAI dependency were equally emphasized by all but one participant during the second writing session. This emphasizes that most participants engage in prior preparation before starting a writing task and remain cautious of dependency concerns among GAI. Although students expressed concerns regarding dependency, accuracy, and ethical use, they continue to incorporate GAI into their work. Although GAI had a higher report of decreased confidence among the experimental group, whereas one participant in the control group reported a decrease in confidence, most reported little to no change, indicating that GAI is an assistive resource rather than a replacement for existing writing strategies. Overall, GAI appears to enhance existing writing practices. These results offer valuable insights for the integration of GAI in education and for informing responsible GAI use in higher education. Future research should examine the long-term effects of AI use on student learning and writing development with a larger sample size. Besides college students, other demographics can include non-college students and their use of Generative AI.

Publication Date

5-2026

Document Type

Thesis

Student Type

Graduate

Degree Name

Human-Computer Interaction (MS)

College

Golisano College of Computing and Information Sciences

Advisor

Elissa Weeden

Advisor/Committee Member

Kirsten Condry

Advisor/Committee Member

Lawrence Roth

Campus

RIT – Main Campus

Download

Share

COinS