Designing A Generative AI Platform For Tactical Training Of Junior Officers

Tactical decision-making lies at the core of officer education, yet current training methods remain constrained by structural and pedagogical limitations. Traditional wargames offer valuable experience but are organisationally difficult, time-intensive, and often emphasise flows of play over cognitive decision-making. Tactical Decision Games (TDGs) partially address this gap, but their effectiveness is limited by instructor capacity, single-point decisions, limited scenario diversity, and significant instructor-to-trainee bias. Recent advances in large language models (LLMs) and conversational agents offer a promising avenue to mitigate these shortcomings. AI-driven platforms could generate numerous, varied, and realistic tactical scenarios; support multi-step decision cycles with adaptive adversaries; visualise battlefield dynamics; and provide transparent, personalised feedback.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-05-29
DOI
https://doi.org/10.5281/zenodo.20320464
Primary Topic
Explainable Artificial Intelligence (XAI)
Type
article
Field-Weighted Citation Impact
0.00
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article

Designing A Generative AI Platform For Tactical Training Of Junior Officers

Ivan Colak-Antic, Simone Matteo Locatelli, Lauriane Fivaz
Zenodo (CERN European Organization for Nuclear Research)
Explainable Artificial Intelligence (XAI)
article

Designing A Generative AI Platform For Tactical Training Of Junior Officers

Ivan Colak-Antic, Simone Matteo Locatelli, Lauriane Fivaz
article en

Abstract

Tactical decision-making lies at the core of officer education, yet current training methods remain constrained by structural and pedagogical limitations. Traditional wargames offer valuable experience but are organisationally difficult, time-intensive, and often emphasise flows of play over cognitive decision-making. Tactical Decision Games (TDGs) partially address this gap, but their effectiveness is limited by instructor capacity, single-point decisions, limited scenario diversity, and significant instructor-to-trainee bias. Recent advances in large language models (LLMs) and conversational agents offer a promising avenue to mitigate these shortcomings. AI-driven platforms could generate numerous, varied, and realistic tactical scenarios; support multi-step decision cycles with adaptive adversaries; visualise battlefield dynamics; and provide transparent, personalised feedback.

Zenodo (CERN European Organization for Nuclear Research)
Peace, Justice and strong institutions
Openalex Percentile: Top 5%
Explainable Artificial Intelligence (XAI)
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