Large language models such as ChatGPT have increased scholarly output, but whether this productivity boost produces genuine intellectual advancement remains untested. I address this gap by measuring the semantic novelty of 13,847 articles published between 2020 and 2025 in 44 Information Systems journals. Using SPECTER2 embeddings, I operationalize novelty as the cosine distance between each paper and its nearest prior neighbors. A difference-in-differences design with the November 2022 release of ChatGPT as the treatment break reveals a heterogeneous pattern: authors affiliated with institutions in non-English-dominant countries show a 0.18 standard deviation decline in relative novelty compared to authors in English-dominant countries (beta = -0.176, p < 0.001), equivalent to a 7-percentile-point drop in the novelty distribution. This finding is robust across alternative novelty specifications, treatment break dates, and sub-samples, and survives a placebo test at a pre-treatment break. I interpret these results through the lens of construal level theory, proposing that LLMs function as proximity tools that shift researchers from abstract, exploratory thinking toward concrete, convention-following execution. The paper contributes to the growing debate on whether LLM-driven productivity gains come at the cost of intellectual diversity.

翻译：诸如ChatGPT之类的大型语言模型提升了学术产出量，但这种生产力提升是否真正推动了知识进步仍有待检验。本文通过测量2020年至2025年间发表在44本信息系统期刊上的13,847篇文章的语义新颖性来填补这一空白。利用SPECTER2嵌入方法，我将新颖性操作化定义为每篇论文与其最近邻文献之间的余弦距离。以2022年11月ChatGPT发布作为处理断点的双重差分设计揭示了异质性模式：与非英语主导国家机构附属作者相比，英语主导国家机构附属作者的相对新颖性下降0.18个标准差（beta = -0.176，p < 0.001），相当于新颖性分布中下降7个百分点。该发现在不同的新颖性指标设定、处理断点日期及子样本中均保持稳健，并在处理前断点处的安慰剂检验中依然成立。我通过建构水平理论框架解释这些结果，提出大型语言模型作为邻近工具发挥作用，将研究人员从抽象探索性思维转向具体惯例遵循性执行。本文为关于大型语言模型驱动的生产力提升是否以知识多样性为代价的持续争论提供了重要见解。