<![CDATA[This study analyzes developments in artificial intelligence (AI) for defense command and control (C2) systems through an in-depth synthesis of 25 Scopus-indexed international journals (10 Q1, 8 Q2, and 7 Q3) published between 2021 and 2023. The study identified six major AI technology categories that dominate defense C2 research: Decision Support Systems (24%), Explainable AI & Trust (24%), Situational Awareness (16%), Machine Learning & Deep Learning (12%), Multi-Agent Systems (12%), and Security & Risk Management (12%). The research gaps analysis revealed critical challenges in legacy system integration, standardization of explainability metrics, AI adaptation to dynamic adversary tactics, management of operator cognitive load, implementation of an ethical framework, and resilience against adversarial attacks. This research found that while technologies such as Deep Reinforcement Learning and Multi-Agent Systems have reached Technology Readiness Level (TRL) 6-8 (approaching the operational stage), Human-Autonomy Teaming implementations are still at TRL 3-5, indicating significant further research needs. The analysis also shows a sharp increase in publication trends, from 1 in 2021 to 13 in 2023 (an ~1300% increase), reflecting the rapidly increasing global research intensity. This study recommends developing hybrid frameworks for federated learning, military-domain-specific explainable AI techniques, multi-agent reinforcement learning algorithms with transfer learning, and AI accountability mechanisms integrated with international humanitarian law as future research priorities. The findings and recommendations are expected to support the academic community, military practitioners, and policymakers in accelerating the responsible and effective adoption of defense C2 AI.]]>
