"Elicit for Researchers: AI-Powered Literature Review and Systematic Evidence Mapping" For researchers facing an overwhelming literature landscape, Elicit offers a powerful new way to accelerate evidence discovery without sacrificing rigor. This book is written for experienced scholars, review specialists, lab leads, and advanced graduate researchers who need more than a tool walkthrough: they need a defensible operating model for AI-assisted review work. It treats Elicit not as a shortcut, but as a serious research environment to be used with methodological discipline and critical judgment. Across the book, readers learn how to design answerable review questions, choose the right review type, build transparent evidence corpora, conduct auditable screening, and create robust extraction schemas for structured data capture. It examines the realities of corpus coverage, PDF parsing, table and figure extraction, verification workflows, and evidence synthesis, then carries those outputs into reporting, export, and publication-ready artifacts. The result is a complete framework for moving from search to synthesis with traceability intact. Distinctive in both scope and tone, the book emphasizes advanced practice, boundary conditions, and failure-aware decision-making rather than introductory AI enthusiasm. It is ideal for readers already familiar with literature reviews and scholarly workflows who want to integrate Elicit into high-stakes, reproducible research.