Generative AI in Academia: A Comprehensive Review of Applications and Implications for the Research Process. | ||||
| International Journal of Engineering and Applied Sciences-October 6 University | ||||
| Article 8, Volume 2, Issue 1, January 2025, Page 91-110 PDF (929.64 K) | ||||
| Document Type: Review Article | ||||
| DOI: 10.21608/ijeasou.2025.349520.1041 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Ahmed M Hanafi   1; Mohamed s. Ahmed2; Mohamed M. Al-mansi 1; Omar A. Al-Sharif 1
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| 1Department of Mechatronics Engineering, Faculty of Engineering, October 6 University, 6th of October City, 12585, Giza, Egypt. | ||||
| 2Department of Basic science, Faculty of Engineering, October 6 University, 6th of October City, 12585, Giza, Egypt. | ||||
| Abstract | ||||
| Generative Artificial Intelligence (GenAI) is redefining academic research, offering unprecedented tools and methodologies that enhance efficiency and innovation across the research lifecycle. GenAI is transforming academic research, offering innovative tools that enhance efficiency and innovation across the research lifecycle. This paper explores how GenAI reshapes key research processes, including idea generation, literature reviews, data analysis, and post-publication activities. Tools like ChatGPT streamline workflows, uncover novel insights, and promote interdisciplinary collaboration. GenAI’s ability to generate synthetic datasets, automate hypothesis creation, and provide advanced analytical support accelerates scientific discovery. However, these advancements raise ethical and practical challenges, such as risks of plagiarism, algorithmic bias, data privacy concerns, and diminished critical thinking skills. The paper addresses the evolving guidelines from academic publishers and emphasizes the importance of transparency, accountability, and human oversight in using GenAI. Ethical issues surrounding AI-generated content, authorship, and intellectual property are also critically examined. Additionally, this paper introduces a comprehensive framework for responsibly integrating GenAI into research. It focuses on best practices and strategies to mitigate associated risks, ensuring that GenAI’s transformative potential drives knowledge creation while preserving academic integrity and addressing emerging challenges. | ||||
| Keywords | ||||
| Generative Artificial Intelligence (GenAI); Academic Research Lifecycle; AI Tools in Academia; Prompt Engineering; Transformative Research Methodologies | ||||
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