From Adoption to Mastery: A Conceptual Framework for AI Integration in Academic Research

By 2025, over 80% of academic researchers report using generative artificial intelligence tools (Wiley, 2025; HEPI, 2025; UNESCO, 2025), yet the translation of this adoption into mature, methodologically grounded research practice has proceeded markedly slower. McKinsey’s State of AI 2025 (n = 1,993 organisations) captures the structural manifestation at the cross-sectoral level: 88% of organisations use AI, but only 1% describe deployments as mature. This paper conceptualises the structural gap between adoption and mastery – the adoption-mastery gap – as a distinct object of study in research management. The paper conducts a scoping review applying the PRISMA Extension for Scoping Reviews (PRISMA-ScR; Tricco et al., 2018), aligned with the PRISMA 2020 reporting framework (Page et al., 2021), on a curated analytical corpus of 66 sources complemented by 18 foundational theoretical and methodological anchors (88 total bibliography entries). Sources were identified through triangulated multi-agent retrieval across three independent generative search interfaces, anchored in The Lens database with verification through Crossref records and indexing status confirmed across scholarly databases (OpenAlex, The Lens, Crossref, WoS and Scopus). The paper offers four contributions: a theoretical conceptualisation of the adoption-mastery gap with the augmentation trap as its causal mechanism; a descriptive taxonomy of five mature AI-assisted research practices; a four-lever institutional framework for doctoral schools anchored in research administration literature; and a theoretical hypothesis – to be tested in subsequent empirical work – of East European structural isolation as a regional driver of the gap.