With the rapid expansion of research on educational technology for older adults, conducting comprehensive literature reviews has become increasingly complex. Artificial Intelligence (AI) tools such as Google’s NotebookLM offer the potential to streamline this process by enabling researchers to interactively query and summarize large document collections. This study presents a methodological evaluation of NotebookLM as an AI-assisted literature review tool and explores its implications for research in educational technology targeting older adult learners. A critical, tool-focused evaluation of NotebookLM’s interaction with a curated corpus assesses its research value for age-sensitive educational technologies. The tool was applied to a curated corpus of 20 academic sources, revealing its strengths in automation, speed, and thematic synthesis. A SWOT analysis was employed to assess its capabilities, highlighting strengths (e.g., efficiency, breadth), opportunities (e.g., mapping emerging trends), but also weaknesses such as occasional hallucinated references and limited capacity for critical appraisal. To address these limitations, we applied a human-in-the-loop approach, including manual verification of sources, prompt refinement, and triangulation with traditional review methods, ensuring reliability and scholarly rigor. Ethical considerations, especially the need for human oversight, were emphasized due to the potential for misinformation and bias in AI-generated outputs. Findings suggest that while NotebookLM can accelerate the process of identifying key themes (e.g., digital inclusion, lifelong learning, assistive technologies), it cannot substitute for rigorous, human-led analysis. Instead, a human-AI partnership is recommended, where AI performs the labor-intensive groundwork and researchers ensure the quality and validity of results. This work contributes to the growing body of scholarship on integrating AI into academic research workflows and highlights the importance of transparency and ethical responsibility in the use of AI in literature reviews.