TL;DR
A corpus-based analysis of 200 Scopus-indexed Q1 research articles contributes a theoretically grounded register profile of AI-assisted academic prose with immediate relevance for computational detection pipelines, register theory, and academic integrity policy.
A statistically significant increase in lexical density and frequency of cohesive markers has been revealed, indicating an increase in information compression and explicit discursive organization of texts, consistent with characteristics described in AI-assisted writing studies.
Text length and selected subordination and T-unit measures distinguished the corpora, and the findings are corpus-specific and cannot support a general-purpose AI-text detector.
A bibliometric and topic-based analysis of 7,120 Arabic NLP papers published between 1960 and 2026, sourced from five platforms plus an additional targeted OpenAlex subset, and offers recommendations to prioritize under-resourced dialects and develop culturally aligned benchmarks.
Analysis of a large-scale dataset covering 234,073 research papers published between 2019 and 2025 suggests that AI-assisted writing confers a modest but systematic advantage in visibility and discoverability that translates into higher measured impact.
Artificial intelligence-supported analysis identified writing development associated with revision and captured human judgment to some extent, but did not provide causal evidence of the independent contribution of peer feedback to writing improvement due to the observational design.
Gemini can produce structurally conventional research abstracts, but its rhetorical organization tends to be more regularized and less variable than that of human writers, which suggests that Indonesian researchers and students may benefit from using Gemini as a supporting tool for checking rhetorical completeness and...
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