Can Generative Artificial Intelligence Enhance Research Output?—An Empirical Analysis Based on the 2024 National Doctoral Graduate Survey

Abstract

In the era of digital transformation, generative artificial intelligence (GenAI) has become an important tool for enhancing research efficiency. However, its specific impact on research output remains underexplored. Based on the data from the 2024 national doctoral graduate survey, this study investigated the impact of GenAI on doctoral students’ research output using propensity score matching and inverse probability weighted regression adjustment methods. The findings showed that GenAI use increased doctoral students’ total research output by 6.5%, international publications by 6.9%, and top-tier journal publications by 16.5%. However, the technological dividends were not equally shared, with factors such as gender and age constituting substantial barriers to using GenAI. Moreover, heterogeneity analysis revealed that the benefits of GenAI use varied significantly across different disciplines and academic environments. For doctoral students with insufficient mentor guidance, although GenAI contributed to an increase in the total number of papers and international journal publications, it failed to yield significant benefits for top-tier journal publications. Accordingly, this study recommends the systematic integration of GenAI into doctoral training systems, the development of intelligent resource-sharing platforms, and the strengthening of ethical norms and fairness safeguards for GenAI use. These measures aim to promote the rational application of the technology and the equitable sharing of digital dividends, thereby fostering the high quality and sustainable development of doctoral education.

Keywords

generative artificial intelligence (GenAI) / research output / doctoral education / heterogeneous benefits

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Higher Education Press