The AI-Augmented Systematic Review: Validating a Low-Resource Workflow for Independent Scholarship

Objective: Systematic Literature Reviews (SLRs) traditionally require substantial resources (500–800 hours), creating barriers for independent researchers. This study tests the "Democratization Hypothesis": that an AI-augmented workflow can achieve comparable rigor with significantly reduced resources. Methods: A comparative analysis was conducted on peer-reviewed methodological studies (2023–2025). The workflow utilized a novel "Hypothesis-Driven AI Querying" (HDAQ) protocol followed by manual verification. Results: The workflow demonstrated a 95.5% reduction in screening time (25.5 hours vs. 564 hours) while maintaining high sensitivity (>95%) when human-verified. AI extraction error rates (1.51%) were lower than reported human error rates (4.37%) in structured tasks. Conclusion: The evidence supports the democratization of science, allowing single independent researchers to produce rigorous systematic reviews, provided that hybrid human-in-the-loop verification is maintained.