The dual impact of generative AI on research: structural stability and attention reallocation
DOI:
https://doi.org/10.47989/ir31iConf64268Keywords:
Generative AI, Research landscape, Network analysis, Research focus shiftAbstract
Introduction. We present an exploratory analysis of the short-term effects of generative AI (GenAI) on the research landscape. As GenAI becomes more deeply embedded across multiple stages of scientific work, there is an increasing need to examine its broader impact on all forms of research.
Method. We retrieved 24,309,359 publications (2020–2024) from OpenAlex across 26 disciplines after excluding records with missing references or unspecified research fields. Distinct research topics were identified via graph clustering algorithms for subsequent analysis.
Analysis. Using a multidimensional framework, we calculated topic diversity indices to examine temporal evolution, constructed topic networks to measure structural changes via clustering coefficients, and assessed GenAI publication proportions among hot topics.
Results. The available evidence detected no significant changes in overall topic variety or network structural properties from 2020 to 2024. However, within the most prominent topics, GenAI-related publications increased significantly, indicating internal reallocation of research attention.
Conclusion. Initial evidence suggests no detectable macro-structural changes coexist with micro-level attention shifts toward GenAI within established domains. This dual pattern suggests that GenAI is less a disruptive force than a catalyst that amplifies research attention within established domains.
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Copyright (c) 2026 Xiaoting Xu , Naixuan Zhao , Jiang Li , Xiao Hu
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