How can AI be used to sort out and classify problems in academic research?

Artificial intelligence can systematically analyze research literature to identify, categorize, and structure problems within academic domains through advanced computational methods. These techniques automate the discovery of patterns, themes, and significant gaps across vast datasets. Key principles involve robust preprocessing of textual data (cleaning, tokenization), applying appropriate computational linguistics or machine learning techniques (like topic modeling, clustering, or natural language processing), and rigorous validation against established research taxonomies. Essential conditions include access to comprehensive, high-quality datasets and domain expertise for contextual interpretation. The scope primarily encompasses literature review synthesis, gap analysis, and emerging trend identification, though accuracy depends heavily on training data quality and algorithm selection. Application involves leveraging these AI classifications to accelerate literature reviews, map interdisciplinary connections, and identify high-impact research opportunities. Value lies in streamlining knowledge synthesis, revealing novel research directions obscured by data volume, and supporting strategic research planning. Practically, researchers typically implement this by first aggregating literature, selecting suitable AI tools (e.g., LDA, BERT embeddings), running analyses, then refining results through expert validation to define problem categories and priorities.