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Rysbekova, Gulzhan (2026) AI diffusion and institutional lag: framework for institutional AI adaptation. In: The 40th edition of the BAM Conference, 7 - 11 September, 2026, Royal Holloway, University of London. (In Press)
The rapid diffusion of generative artificial intelligence (AI) presents a deeper institutional adaptation problem. This developmental paper examines AI adoption in higher education as a disruption to capability formation and credential legitimacy. Drawing on exploratory mixed-methods data from 55 business and management students at a UK university, the study identifies emerging tensions between institutional governance and assessment regimes and students’ anticipated professional realities. The findings suggest that rapid technological diffusion may generate institutional lag, producing uneven capability development and perceived misalignment between assessment practices and workplace demands. The paper theorises these dynamics through institutional theory, technological legitimacy, and human-AI complementarity perspectives, proposing a developmental framework linking legitimacy, capability, and governance. By conceptualising student voice as organisational intelligence, the study contributes to emerging organisational scholarship on technological disruption and human capital reconfiguration, and positions higher education as an analytically revealing context for examining institutional adaptation under accelerated technological change.
![[thumbnail of 260612 LMU repository BAM26 AI HE submitted.pdf]](https://repository.londonmet.ac.uk/style/images/fileicons/text.png "260612 LMU repository BAM26 AI HE submitted.pdf")
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