Use of Generative AI in Academia: Collaborative Autoethnographic Perspectives from NNES and NES Scholars

Authors

Tianshi Hao, Pepperdine University, Malibu, California, USAFollow
Ziyue Guo, University of Western Ontario, London, Ontario, Canada
Jade Winn, University of Western Ontario, London, Ontario, Canada
Paul Sparks, University of Southern California, Los Angeles, California, USA

Abstract

The advancement of Generative AI (GenAI) has shaped the ways people learn, teach, and perform research in the field of academia. However, there is a lack of studies that explore how GenAI makes differences on both Native English Speaking (NES) and Non-Native English-Speaking (NNES) scholars' academic work and experiences. This study aims to examine the experiences and perceptions of two Asian NNES Ph.D. students and two American NES professors regarding the use of GenAI tools in their academic work, through a Collaborative Autoethnography (CAE) approach. Data were collected through individual and group reflections, analyzed using the Technology Acceptance Model (TAM). The study revealed that NNES scholars primarily use GenAI tools to overcome linguistic barriers in academic writing, while NES scholars are more driven by curiosity and the potential for academic innovation. Both groups identified benefits and challenges, including improved writing quality and ethical concerns regarding GenAI use. GenAI tools hold promise for enhancing academic productivity and leveling linguistic disparities in academia. However, there is a need for ethical guidelines and training to mitigate over-reliance, overcome hallucination phenomenon and ensure responsible use. Future research should focus on broader applications and long-term impacts of GenAI in academia.

Keywords

artificial intelligence, collaborative autoethnography, technology Acceptance Model, ethics, educational innovation

Author Bio(s)

Tianshi Hao has Ph.D. in Global Leadership and Change from Pepperdine University and an MAT in TESOL from the University of Southern California, he uses training in education technology and leadership development. He currently works as a knowledge engineer at Hyperthesis LLC, where he designs prompts for AI‑driven data extraction, validates the outputs, builds classification systems and perform synthesis and statistical analysis. Please direct correspondence to tianshi.hao@pepperdine.edu

Ziyue Guo, University of Western Ontario, London, Ontario, Canada, is currently a Research and Teaching Assistant at Western University with skills in Qualitative and Quantitative research methodologies, research design, data analysis, and course design.

Jade G. Winn. ade Winn is an associate university librarian at the University of Southern California (USC). In 2009, she joined the USC Libraries faculty as the Head of Social Work and Education Library Services. Since promotion to her current role as the Assistant Dean of Instruction, Assessment and Engagement, she has spent nearly a decade supporting programs and planning for USC Libraries. Jade’s research and expertise focus on global citizenship as an outcome of higher education; information literacy; evidence synthesis research methods; diversity, equity, inclusion, accessibility, and anti-racism (DEIA+AR) pedagogy & curriculum; non-medical sciences systematic reviews (NMSSRs); artificial intelligence (AI) in education; and advanced quantitative research methods in social sciences. Her current responsibilities involve developing and launching evidence synthesis services, including systematic and scoping reviews, for USC’s University Park Campus.

Paul Sparks leads technology courses in the doctoral and master's programs at Pepperdine. Previously, he was director of training at Epoch Internet where he developed its curriculum. Dr. Sparks was also an educational technologist and information systems specialist for Rockwell International. He was previously a high school and adult education instructor in the Whittier Union High School District.

Publication Date

12-31-2025

Creative Commons License

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 4.0 International License.

DOI

10.46743/2160-3715/2025.7532

Recommended APA Citation

Hao, T., Guo, Z., Winn, J. G., & Sparks, P. (2025). Use of generative AI in academia: Collaborative autoethnographic perspectives from NNES and NES scholars. The Qualitative Report, 30(12), 4834-4856. https://doi.org/10.46743/2160-3715/2025.7532

ORCID ID

0000-0003-2456-0953