The explosive expansion of scholarly publications has made traditional literature review methods increasingly unmanageable. Researchers now face information overload, fragmented knowledge sources, and difficulty in identifying research gaps. In response, artificial intelligence (AI) tools have emerged to assist in literature discovery, automatic summarization, and visualization of scholarly content. This paper examines how AI-driven technologies can strengthen research quality by making literature review and knowledge mapping more efficient and thorough. We conducted a qualitative review of academic and industry sources (e.g. library science, computer science, educational research) to identify prominent AI applications, tools, benefits, and concerns. Our analysis finds that AI can greatly accelerate literature search and analysis, broaden coverage across disciplines, and generate visual knowledge maps that reveal key themes and connections (Chen, 2017). These capabilities enable more evidence-based and inclusive writing processes. However, important challenges arise: algorithmic bias, opaque decision-making, and over-reliance on automation can undermine integrity. Ethical considerations such as data privacy and clear attribution also demand careful attention. We conclude that with proper oversight and training, AI tools can serve as valuable allies for researchers, enhancing efficiency and insight while maintaining scholarly rigor.
Ramakrishna Rao Pulletikurty (Thu,) studied this question.