Can AI automatically generate research questions in related fields?

AI systems can automatically generate potential research questions within specified domains. Current capabilities demonstrate the technical feasibility of this task through specialized natural language processing models. Effective generation relies on substantial domain-specific training data, appropriate algorithms like transformers or topic modeling, and computational linguistics techniques. Constraints include dependence on data quality and scope, limited capacity for true conceptual innovation, and challenges in contextual nuance assessment that necessitate human review. Applicability varies across disciplines based on available literature corpora and conceptual structures. Such automated generation accelerates literature synthesis by surfolding under-explored areas derived from existing publications. It aids researchers in identifying knowledge gaps during preliminary review phases. Value emerges primarily in augmenting human ideation, providing structured starting points, and reducing cognitive load in systematic topic exploration. However, it functions as an assistive tool rather than replacing critical scholarly judgment in final question formulation.