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Abstract: Generative large language models such as ChatGPT and Claude have sparked debate throughout higher education about their potential and threats to the current pedagogical models for research. This paper scans the literature on the benefits and drawbacks of using large language models in teaching and research and compares and contrasts two literature reviews on the subject, one written by a human author and one produced by Claude. The comparison explores areas where the generative artificial intelligence excels, such as quickly summarizing points of agreement across sources, as well as its limitations, such as struggling with synthesis, providing incomplete citations, and hallucinating false information. While Claude was able to identify key themes and sources, the areas where it struggles show that without human intervention (providing context and analysis), the tool cannot produce a literature review that would stand on its own. However, the experiment demonstrates potential for these models to augment and accelerate research workflows when leveraged responsibly alongside human scholars.
Sparkman et al. (Sat,) studied this question.