Methodological Frameworks for AI-Assisted Literature Reviews presents a systematic and critical examination of how artificial intelligence can be integrated into the literature review process to enhance rigor, efficiency, and analytical depth in scholarly research. As the volume of scientific publications continues to grow exponentially, conventional manual approaches to literature review face significant limitations in scalability and comprehensiveness. This book addresses these challenges by proposing structured methodological frameworks that combine AI capabilities with human scholarly judgment.
The book explores AI applications across all stages of the literature review lifecycle, including literature discovery, screening and selection, data extraction, thematic synthesis, and identification of research gaps. It critically evaluates commonly used AI-driven tools and techniques—such as natural language processing, semantic search, citation network analysis, and automated summarization—while emphasizing transparency, reproducibility, and research integrity.
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