From Technological Disruption to Institutionalised Assimilation—A Computational Content Analysis, Semantic Embedding and Longitudinal Discourse Drift Study Based on the Proceedings of the 2025 Intelligence Research Summit at the US Intelligence University

Against the backdrop of artificial intelligence (AI) and cyber intelligence (CyINT) becoming increasingly embedded within intelligence systems, the core challenge facing intelligence organisations is no longer ‘whether to adopt new technologies’, but rather ‘how to transform technological disruption into governable, measurable, and trainable institutional capabilities’. This paper examines the proceedings of the Intelligence Studies Summit 2025, published by the National Intelligence University (NIU), to propose the Institutional Absorption Discourse Model (IADM). (Institutional Absorption Discourse Model, IADM). Through computational content analysis, semantic embedding, and longitudinal discourse drift detection, it conducts computable modelling on this academic-practical hybrid corpus—a ‘non-news stream, non-policy text’—comprising conference proceedings. Findings reveal: textual discourse follows a distinct phased progression—‘technological disruption → threat framing → governance and accountability → measurability → education and disciplinary institutionalisation’; governance and accountability discourse significantly lags behind technological topics in sequence yet erupts concentratedly as institutional modules; education and effectiveness measurement constitute stabilisers for institutional absorption. This paper's theoretical contribution lies in translating intelligence discourse into a testable chain of institutional mechanisms. Its methodological contribution proposes a quasi-longitudinal modelling paradigm for conference proceedings, providing an operational pathway for auditing AI governance and intelligence research.