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.
Authors
- Ivan Colak-Antic
- Simone Matteo Locatelli
- Lauriane Fivaz
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2026-05-29
- DOI
- https://doi.org/10.5281/zenodo.20320465
- Primary Topic
- Explainable Artificial Intelligence (XAI)
- Type
- article
- Field-Weighted Citation Impact
- 0.00