When using AI tools to write literature reviews, how can one enhance their comprehensiveness?

To enhance the comprehensiveness of literature reviews generated with AI tools, rigorous methodologies focusing on systematic search strategies and iterative refinement are essential. This involves strategic human-AI collaboration to mitigate the risk of omissions inherent in automated systems. Crucially, comprehensiveness relies on meticulously designed search queries using precise, diverse keywords and Boolean operators reflecting the review's scope. Validating the AI's identified sources against established academic databases ensures broad coverage beyond its initial dataset. Employing multiple AI tools or platforms with different underlying datasets diversifies results. Users must actively guide the AI by defining inclusion/exclusion criteria and continuously refining prompts based on initial outputs, critically analyzing generated content to identify potential gaps necessitating manual search supplementation. Implement this by first brainstorming and structuring a comprehensive keyword taxonomy. Feed these structured queries into the AI, then cross-reference the suggested sources against major databases (e.g., Scopus, Web of Science) and subject-specific repositories. Analyze outputs for topical or methodological gaps; iteratively refine queries or explore alternative AI tools to address these. Remember, while AI accelerates discovery, ultimate comprehensiveness requires active researcher oversight, critical evaluation of AI suggestions, and verification against authoritative sources.