Combat experiment analysis of common situational understanding operation and weapon recommendation performance in AI command decision support systems

2026-04-15T06:10:25+00:00

This study empirically examines the application of an AI-enabled decision support system for command and control by integrating AI-based weapon recommendation functions into the Common Operational Picture (COP) procedures of conventional C4I systems. The validation was conducted through battalion-level combat experiments under the Korean Army TIGER framework. The analysis focused on three key aspects: (1) a comparison between AI-generated recommendation time and the time required for human commanders to assess, decide, and issue orders across different target types; (2) a classification of adoption outcomes between AI-recommended weapons and actual fire commands into identical, similar, and non-identical categories; and (3) an evaluation of doctrinal consistency based on simplified tactical concepts (C1–C4) derived from METT-TC. The results indicate that in the initial decision-making phase following target input, human commanders required on average several hundred seconds, whereas the AI system generated recommendations within approximately 1–2 seconds, demonstrating significant time efficiency. Furthermore, AI-generated weapon recommendations showed a predominance of identical or similar adoption across multiple targets, and a high level of alignment was also observed in terms of tactical concept consistency. These findings suggest that AI-based recommendations maintain doctrinal coherence with predefined tactical criteria even under constrained conditions characterized by limited information and manually synchronized operational environments. To be end, our study reflects real-world operational constraints in which sensor and C4I data integration is not fully automated, and COP updates rely on manual input following staff verification and approval. By empirically analyzing AI-supported weapon recommendation and decision-making processes under such conditions, this research provides meaningful practical implications for the deployment of AI-driven decision support systems in operational military environments.

Journal of Advances in Military Studies

Combat experiment analysis of common situational understanding operation and weapon recommendation performance in AI command decision support systems