The Impact Of Generative AI Tools on Research Productivity and Scholarly Writing among University Faculty and Students

Abstract

The world of higher education and scholarly writing has experienced a revolutionary transformation with the advent of Generative Artificial Intelligence (AI), ushering in a new era of possibilities in knowledge production and research dissemination. From literature review to brainstorming, data analysis, writing and publishing research, university researchers and students are increasingly turning to applications such as large language models and AI writing tools to help them. The technologies have clearly brought in new and important ways to increase research productivity and efficiency, but also generated much discussion about academic integrity, authorship, excessive reliance on technology, and the nature of scholarly work. The incorporation of Generative AI into academia is still fairly recent, however, and little is known about how it actually affects research productivity and writing by HE stakeholders from a qualitative perspective. This study aims to investigate how generative AI technologies affect research productivity, understand their effect on academic writing processes, highlight opportunities and challenges in research and writing with AI, and develop a conceptual understanding of responsible and responsible use of AI in research and writing. The research method is qualitative which consists of three stages such as integrative literature review, thematic analysis, conceptual synthesis and document analysis of scholarly articles, institutional report and policy documents. The results show that generative AI tools significantly boost the efficiency and effectiveness of research processes, including time to information, writing assistance, and idea generation and scholarly communication. However, the study also reveals that the concept of academic integrity, ethics, critical engagement and originality and authorship has serious problems. The research builds on the existing literature in HE scholarship and ET and the emerging literature on the responsible use of generative AI in education by proposing a conceptual model for using generative AI responsibly in research and academic writing. It finds that the potential for generative AI in higher education in the long term will rely on the right balance between technological innovation and the continued maintenance of ethical, human-centered and intellectually rigorous scholarly practices.