A Survey of Large Language Models as Research Assistants

Abstract

Large Language Models (LLMs) offer new opportunities to improve nearly every phase of academic research, from ideation and literature discovery to experimental design, manuscript preparation, and peer review. This survey examines how LLMs have been used as research assistants over the past two years, integrating findings from multiple academic databases to identify best practices and emerging methodologies. To clarify the scope of the review, we address the following research questions: (1) In what ways have LLMs been used to support or automate different stages of the research lifecycle? (2) What challenges and ethical concerns arise from the use of LLMs in academic research? (3) What trends and promising directions can guide the responsible integration of LLMs as research assistants?By analyzing peer-reviewed studies and selected reports from the past two years, we identify patterns of adoption, emerging methods, and concerns about accuracy hallucination, and authorial integrity. We find that while LLMs significantly reduce time spent on routine tasks, their use must be accompanied by human oversight and rigorous validation frameworks.These insights lay the groundwork for the discussion that follows, which begins by contextualizing the current research environment and the role of LLMs within it. While LLMs cannot replace human expertise, they hold promise in transforming scholarly inquiry into a more efficient, collaborative, and innovative process.

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