Evaluating LLM “Research Assistants” and Their Risks for Novice Researchers

Abstract LLM research assistants promise gains in efficiency and productivity, purporting to “streamline” the challenging, recursive, and often messy work of identifying, evaluating, analyzing, and synthesizing the existing research on a given topic. These tools have prompted concern in scholarly research communities, particularly among educators, who understand the processes of research-based writing—including rhetorical analysis, source evaluation, and the ability to grasp and analyze a set of research questions and to locate them in a larger context—as crucial activities, integral to building students’ intellectual abilities and skills. Through a close examination of one LLM research assistant, Google's NotebookLM, this analysis emphasizes four major points about NotebookLM and LLM research assistants more generally: (1) They are not good at summarizing texts: they get things wrong, make things up, and do so in complex, nonobvious ways; (2) Their outputs mimic, but do not actually produce, the outcomes of human reading comprehension and source synthesis; (3) They are proprietary black boxes; and (4) They risk harm to the cognitive development of their users. By helping students to develop practical understandings of how LLM systems generate what is called “research,” educators can empower them to assess the true capabilities, limitations, and consequences of these products.

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Publication Details

Journal
Critical AI
Published
2025-10-01
DOI
https://doi.org/10.1215/2834703x-12095982
Primary Topic
Academic integrity and plagiarism
Type
article
Field-Weighted Citation Impact
0.00
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article

Evaluating LLM “Research Assistants” and Their Risks for Novice Researchers

Tiffany DeRewal
Critical AI
Academic integrity and plagiarism
article

Evaluating LLM “Research Assistants” and Their Risks for Novice Researchers

Tiffany DeRewal
article en

Abstract

Abstract LLM research assistants promise gains in efficiency and productivity, purporting to “streamline” the challenging, recursive, and often messy work of identifying, evaluating, analyzing, and synthesizing the existing research on a given topic. These tools have prompted concern in scholarly research communities, particularly among educators, who understand the processes of research-based writing—including rhetorical analysis, source evaluation, and the ability to grasp and analyze a set of research questions and to locate them in a larger context—as crucial activities, integral to building students’ intellectual abilities and skills. Through a close examination of one LLM research assistant, Google's NotebookLM, this analysis emphasizes four major points about NotebookLM and LLM research assistants more generally: (1) They are not good at summarizing texts: they get things wrong, make things up, and do so in complex, nonobvious ways; (2) Their outputs mimic, but do not actually produce, the outcomes of human reading comprehension and source synthesis; (3) They are proprietary black boxes; and (4) They risk harm to the cognitive development of their users. By helping students to develop practical understandings of how LLM systems generate what is called “research,” educators can empower them to assess the true capabilities, limitations, and consequences of these products.

Critical AIVol. 3(2)
Quality Education
Openalex Percentile: Top 30%
Academic integrity and plagiarism
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Evaluating LLM “Research Assistants” and Their Risks for Novice Researchers — Tiffany DeRewal · Critical AI (2025) | TGRS Research Map | TGRS