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This paper aims to comprehensively examine the multifaceted role of generative Artificial Intelligence (AI) within academic settings, exploring its diverse applications, opportunities, and challenges. Employing a systematic review approach, this study synthesizes and analyzes the existing literature pertaining to the integration of AI in academia. It critically evaluates the varied applications of generative AI tools across different domains such as literature review, visualization, content generation, plagiarism detection, language enhancement, data analysis, and journal selection. The examination reveals a myriad of advantages brought forth by generative AI applications, including a substantial reduction in researchers’ workloads, time-saving mechanisms, the extraction of valuable insights from extensive datasets, and an overall enhancement in the quality of scholarly outputs. However, alongside these benefits, several challenges and limitations emerge. These include concerns regarding accuracy and reliability, ethical implications, limitations in linguistic and contextual understanding, potential hindrance to critical thinking and creativity, issues with data visualization, training requirements, staying updated with recent research, and the complexity and costs associated with specialized training. This paper provides a comprehensive and structured overview of the applications, advantages, and challenges of utilizing generative AI in academic settings. It synthesizes existing knowledge, critically evaluates the implications, and highlights the need for a balanced approach to harness the full potential of AI while mitigating ethical and practical challenges. The paper's contribution lies in offering a holistic view of AI's impact on academia, emphasizing the need for collaborative efforts among stakeholders to maximize benefits while ensuring ethical standards and academic integrity.
Panda et al. (Sat,) studied this question.