Can AI replace traditional literature retrieval methods in academic research?

Artificial intelligence cannot fully replace traditional literature retrieval methods but significantly enhances them through advanced automation. Current AI systems excel at processing large datasets and identifying patterns beyond manual capabilities, enabling rapid preliminary screening and semantic search expansions. However, AI effectiveness depends critically on training data quality and algorithmic transparency, which introduces risks of bias, false positives, or overlooking novel interdisciplinary connections. Researchers must maintain human oversight for contextual interpretation, source validation, and refining search strategies based on nuanced research questions—tasks where AI lacks depth. Integrating AI into literature retrieval workflows offers substantial efficiency gains. Practical implementation typically combines AI-powered tools for broad, initial discovery phases with systematic manual evaluation for quality control and synthesis. This synergy accelerates literature identification across large repositories while preserving essential scholarly rigor in critical assessment. Key applications include AI-assisted database queries, citation tracking, and trend analysis, thereby freeing researchers to focus on higher-order analysis and interpretation while minimizing oversight risk.