Cheating or Learning? A Descriptive Case Study on the Use of Generative AI in Academic Research

• In Event: AI for Equity, Access, and Innovation in Postsecondary Writing and Learning Contexts

Abstract

Objectives
The use of generative AI in higher education has become a prevalent topic in research, with growing interest in studying its impact and perceptions surrounding teaching and learning (Chan & Hu, 2023; Irfan et al., 2025; Yan et al., 2024; Zhang et al., 2024). Despite this interest, there is a need for more context-specific studies on generative AI, especially in academic research. Oftentimes, using generative AI in educational research is associated with cheating or misinterpreted as academic dishonesty (Oravec, 2023). While misuse could happen, this is not the case when generative AI is used intentionally, transparently, and responsibly. As a matter of fact, cheating has been a growing academic and ethical concern in higher education for so long (Brimble, 2016; Macfarlane, 2004). It can happen with Generative AI or without, for example, by hiring others to complete assignments. However, when used responsibly, generative AI can serve as a research tutor, assistant, or partner that enhances understanding, supports critical thinking, and streamlines analysis to promote greater academic rigor (Phan & Le, 2025).

Theory
This study is guided by the theoretical framework of the “intelligence augmentation” (IA) concept, by Chris Dede, which states that AI tools can augment human intelligence by complementing tedious "reckoning" tasks (Dede, Etemadi, & Forshaw, 2021) such as locating papers, finding connections, and citing resources in appropriate ways. This strategic partnership may help researchers and students to concentrate on higher-order "judgment" skills, thereby fostering deeper intellectual engagement.

Method:
The study employed a descriptive case study analysis to examine the use of generative AI in a higher education class. A case study is a qualitative research approach that involves an in-depth analysis and contextual examination of the case over a period of time to provide a holistic view of using generative AI in educational research (Stake, 2013). This study was conducted over a 14-week online graduate course with 11 students, focused on the topic of AI in Education. In the course, students used multiple generative AI tools to brainstorm ideas, explore research questions, review literature, and explore methodology. The study integrated rich data from instructor notes, observations of student AI use, and students' artifacts to provide a thick description of the phenomena.

Results:
Data was analyzed using thematic techniques and organized into two major categories: 1) possible opportunities and 2) challenges that students encountered when using generative AI for academic research. Findings of the potential benefits include: Lowering the stress of conducting literature review; enhancing critical thinking; personalizing research topics; increasing engagement; and promoting student accountability. On the other hand, research revealed several challenges, such as over-reliance on Generative AI, data privacy, digital divide barriers, and the high cost of the AI tools.

Significance:
These findings challenge the misconception that using generative AI in academic research is tied to cheating. Instead, they emphasize the importance of equipping future leaders with the skills to use AI responsibly and effectively. The insights also help higher education leaders and faculty develop thoughtful AI guidelines and policies for integrating AI into research contexts meaningfully.

Author

• Samaa Haniya, Pepperdine University