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Abstract
Conducting systematic literature reviews is traditionally a laborious, manual process involving the extraction of distinct data points from hundreds of academic papers, but this presentation introduces a structured, multi-tool workflow using the Scopus API, NotebookLM and Gemini to automate and enhance this task. By employing a pipeline that includes batch processing PDFs in NotebookLM to prevent data omissions and using Gemini for high-level synthesis, the workflow saves an estimated 30+ hours of manual extraction per review and allows sole researchers to tackle large-scale projects previously requiring teams. Validated by emerging research and peer comparison, this approach democratises access to large-scale reviews, though it necessitates a shift in researcher responsibility toward rigorous prompt engineering and 'human-in-the-loop' validation to ensure accuracy and avoid model drift.
Overview
to extract data points & prevent omissions"] B --> C["Human validate a data sample;
refine prompts and rerun if inaccurate"] C --> D["Gemini for high-level
synthesis and drafting"] D --> E["Human-led finalisation
of the review"] E --> F["Large scale review by a sole researcher,
saving 30+ hours per review"]
Background References
Bernabeu, P. (2024). rscopus_plus. OSF. https://doi.org/10.17605/OSF.IO/BUZQ6
Malik, M., & Sime, J. A. (2025). Teamwork, co-regulation, and socially shared regulation skills within engineering education studies: A GenAI-assisted scoping review. ASEE Annual Conference & Exposition, Montreal, Quebec, Canada. https://doi.org/10.18260/1-2--57199
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