https://www.ubplj.org/index.php/jaoi <p>The <em>Journal of Applied Operational Intelligence </em>(JAOI) has just been launched by University of Buckingham Press (UBP), aiming to bridge the gap between high-quality peer-reviewed academic research and the practitioner community on contemporary intelligence issues. The journal provides a unique emphasis on applied empirical research that seeks to address how organisations can enhance the day-to-day policies and practices in the intelligence arena. JAOI aims to address the ‘so what?’ question underpinned by academic rigor.</p> en-US ian.stanier@buckingham.ac.uk (Ian Stanier) info@unibuckinghampress.com (Christian Muller) Fri, 13 Mar 2026 09:36:38 +0000 OJS 3.2.1.1 http://blogs.law.harvard.edu/tech/rss 60 https://www.ubplj.org/index.php/jaoi/article/view/2609 <p>Concerns over foreign electoral interference have grown since the 2016 U.S. presidential election, yet public-facing intelligence assessments continue to rely on vague probabilistic language that limits clarity, consistency, and operational insight. This study introduces an exploratory AI-facilitated framework designed to systematically quantify the likelihood of foreign election interference across U.S. elections from 2016 to 2036. Drawing on declassified intelligence assessments from the ODNI, NIC, and CISA, corroborated by open-source intelligence (OSINT), we applied a three-phase natural language processing (NLP) protocol using OpenAI’s tools to extract, classify, and scale linguistic indicators of confidence. These were then mapped to probabilistic values based on Sherman Kent’s CIA estimative language and modeled using Monte Carlo simulations to account for uncertainty. Named Entity Recognition and sentiment analysis identified country-specific attribution patterns, while lexical scaling translated narrative judgments into quantifiable interference probabilities. Results revealed persistently high likelihoods of Russian interference, alongside growing probabilistic signals from China and Iran over time. A hierarchical linear model confirmed significant variation by election year and actor, and simulation-based forecasts suggest increasing probabilistic risk through 2036. This framework offers a replicable, data-driven model for transforming qualitative intelligence into structured probability distributions, providing analysts and policymakers with an evidence-based tool to track, compare, and forecast adversarial influence strategies with greater transparency and granularity.</p> Brandon May, Marek Palace, Dominika Gurbisz, Janelle Jacobson Copyright (c) 2026 Journal of Applied Operational Intelligence https://www.ubplj.org/index.php/jaoi/article/view/2609 Fri, 13 Mar 2026 00:00:00 +0000