The Emergence of Generative AI in Scholarly Communication Through Lexical Classification, Disciplinary Diffusion, and Citation-Based Recognition

Generative artificial intelligence (GenAI) has gained prominence in scholarly communication, yet its lexical classification, disciplinary diffusion, semantic organization, and association with citation-based recognition remain understudied. This study analyzes 487,753 research and review articles from OpenAlex (August 2019–March 2026), combining rule-based lexical classification with interrupted time-series models, disciplinary mapping, keyword co-occurrence analysis, and citation-count models. The results show a discrete level change in the visibility of GenAI terminology after November 2022. This discontinuity remained robust under classification-error sensitivity analyses, whereas inference regarding the subsequent slope was more sensitive to classification assumptions. The proportion of GenAI-classified publications captured by high-specificity lexical rules increased from 86.65% before December 2022 to 93.64% afterward, consistent with greater lexical concentration. GenAI classification was broadly but unevenly distributed across fields, with the highest prevalence in Computer Science (49.42%) and Decision Sciences (38.57%). A curated keyword network showed overlapping thematic concentrations rather than sharply separated semantic subfields. Month-adjusted citation models showed a positive association between high-specificity classification and citation counts, with a smaller association with GenAI citations after December 2022. Overall, the study characterizes the temporal, lexical, disciplinary, thematic-network, and citation-related dimensions of GenAI’s emergence in scholarly communication.