How can AI help improve the quality of the literature review section?

AI tools enhance literature review quality by automating literature searches, analysis, and synthesis, significantly increasing efficiency and comprehensiveness. They assist researchers in identifying relevant sources, extracting key themes, and organizing information systematically. These tools leverage natural language processing for topic extraction, content summarization, and thematic clustering from vast datasets. They identify citation networks and knowledge gaps efficiently. However, their effectiveness depends on data quality and input specificity; human oversight remains critical for context understanding and critical evaluation to mitigate algorithmic biases. Applicable across research domains, users must ensure transparency in methodology and verify AI-generated insights. Practical implementation involves selecting AI platforms supporting academic search databases and bibliometric analysis. Researchers input research questions and keywords, utilize AI for source screening and summarization, then manually synthesize findings, validate interpretations, and identify gaps. This accelerates the review process, enhances systematic coverage of literature, and reduces human error, supporting more robust research foundations.