AUTHOR=Almufarreh Ahmad , Ahmad Ashfaq , Arshad Muhammad , Onn Choo Wou , Elechi Robinson 

TITLE=Ethical implications of ChatGPT and other large language models in academia

JOURNAL=Frontiers in Artificial Intelligence

VOLUME=Volume 8 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2025.1615761

DOI=10.3389/frai.2025.1615761

ISSN=2624-8212

ABSTRACT=The rapid advancement of technology in the digital age has significantly transformed human communication and knowledge exchange. At the forefront of this transformation are Large Language Models (LLMs), powerful neural networks trained on vast text corpora to perform a wide range of Natural Language Processing (NLP) tasks. While LLMs offer promising benefits such as enhanced productivity and human-like text generation, their integration into academic settings raises pressing ethical concerns. This study investigates the ethical dimensions surrounding the use of LLMs in academia, driven by their increasing prevalence and the need for responsible adoption. A mixed-methods approach was employed, combining surveys, semi-structured interviews, and focus groups with key stakeholders, including students, faculty, administrators, and AI developers. The findings reveal a high level of LLM adoption accompanied by concerns related to plagiarism, bias, authenticity, and academic integrity. In response, the study proposes concrete strategies for ethical integration, including: (1) the establishment of transparent usage policies, (2) the incorporation of LLM literacy training into academic curricula, (3) the development of institutional review frameworks for AI-generated content, and (4) ongoing stakeholder dialogue to adapt policies as the technology evolves. These recommendations aim to support the responsible and informed use of LLMs in scholarly environments. The widespread influence of technological advancement has notably transformed communication and knowledge sharing, with LLMs playing a central role. These advanced neural networks, trained on extensive text datasets, have become valuable tools for generating human-like text and improving efficiency. However, their growing use in academic contexts raises significant ethical concerns. This study investigates these issues, focusing on the implications of LLM integration in scholarly environments. Using mixed methods, including surveys, semi-structured interviews, and focus groups, the research gathered insights from students, faculty, administrators, and AI developers. The findings highlight substantial adoption of LLMs alongside concerns about plagiarism, bias, and academic integrity. Based on this input, the study proposes guidelines for their responsible and ethical use in academia.