NotebookLM as a Source-Grounded AI Research Assistant: A Narrative Review of Cognitive Load Implications and Research Originality Concerns

The proliferation of AI-powered tools in academic settings has prompted urgent questions about their cognitive and epistemic effects on researchers. Among emerging tools, Google's NotebookLM occupies a distinctive position: unlike general- purpose large language models (LLMs) such as ChatGPT or Gemini, it operates exclusively within a user-defined document corpus, grounding every response in cited, user-supplied sources. This architecture raises theoretically significant questions about cognitive load and research originality — two constructs central to the quality and integrity of academic knowledge production. This paper presents a critical narrative review of the literature at the intersection of source-grounded AI, Cognitive Load Theory (CLT), and academic originality, with particular attention to what current evidence suggests — and fails to address, about NotebookLM specifically. Drawing on 45 peer-reviewed studies and technical reports published between 2015 and 2024, we identify four major themes: (1) the cognitive affordances and constraints of source-grounded versus open-ended AI retrieval; (2) the applicability of CLT's load taxonomy to AI-mediated research tasks; (3) empirical evidence on AI's effects on creative and scholarly originality; and (4) the methodological and ethical challenges of studying AI-human research collaboration. We synthesise these themes into a conceptual framework — the Grounded Retrieval–Cognitive Redistribution (GRCR) model — that predicts when and for whom source-grounded AI tools are likely to reduce extraneous cognitive load without displacing germane elaboration. We conclude by identifying critical gaps in the literature and proposing a research agenda for empirical investigation of NotebookLM and similar grounded-retrieval systems in academic contexts.