How can AI be used to reduce overcitation in literature reviews?

AI can detect and reduce excessive citations in literature reviews through automated analysis and optimization of reference usage. It identifies redundant or low-value citations, suggesting more efficient referencing practices. Key principles involve employing natural language processing to analyze citation context, citation density, and relevance to the core arguments. AI algorithms scan text to identify superfluous citations by clustering references by topic or assessing citation necessity through semantic analysis. Challenges include accurate context interpretation and the need for high-quality training data covering diverse disciplinary citation norms. The technology functions best with digitized texts and benefits from clear citation annotation. Implementation involves integrating AI tools into literature review workflows: scanning manuscripts for citation clusters, identifying redundant references pointing to identical concepts, suggesting consolidations by prioritizing seminal sources, and flagging areas where citation density exceeds typical disciplinary thresholds. This enhances review quality, improves readability, and allows readers to focus on substantive findings. Careful review of suggestions by authors remains essential for validating relevance.