AI-Driven Transformation of Military Education and Training: A Comparative SWOT Analysis for the Republic of Korea Armed Forces

Purpose: This study aims to analyze how artificial intelligence (AI)–driven technologies are transforming military education and training systems, with a particular focus on strategic implications for the Republic of Korea (ROK) Armed Forces. As AI, virtual simulation, and data analytics reshape modern warfare and command decision-making, understanding their integration into professional military education has become essential for future readiness. Method: Using a comparative case study and SWOT analytical framework, this paper examines AI-based education and training innovations in the United States, the United Kingdom, Israel, France, and Japan. Data were collected from official defense reports, academic literature, and institutional documents to identify each nation’s approaches to AI integration in military learning. The analysis evaluates the ROK military’s strengths, weaknesses, opportunities, and threats in adopting AI-centered education systems. Results: The comparative analysis revealed several significant patterns in how leading militaries are integrating AI into their education and training frameworks. First, technological integration and cognitive learning convergence emerged as a global trend. The United States and the United Kingdom employ AI-enabled simulations and adaptive learning systems that provide personalized training feedback. Israel’s Defense Forces demonstrated real-time combat learning through automated data analysis, enhancing decision-making accuracy during mission rehearsal exercises. France and NATO have expanded AI-assisted wargaming for strategic and ethical decision training, showing that artificial intelligence is being used not only for operational efficiency but also for cultivating strategic judgment. Second, the ROK Armed Forces exhibit strong foundational readiness for AI-based transformation. Korea’s advanced ICT infrastructure, high data literacy within the defense industry, and disciplined training culture serve as major strengths. However, several critical weaknesses were identified: insufficient AI literacy among instructors, limited cross-branch data integration, and the absence of a unified defense learning framework. These factors hinder the scalability and coherence of AI-based training initiatives. Third, emerging opportunities include expanding international cooperation with the United States, NATO, and other AI defense clusters. The Korean National Defense AI Strategy (2024–2030) provides policy momentum to institutionalize AI education and simulation systems. Yet threats remain—especially overreliance on technology, ethical dilemmas in AI decision support, and data security vulnerabilities. Overall, the results underscore that Korea’s transition to AI-driven military learning requires not just technological acquisition but a holistic transformation linking pedagogy, leadership, and human –machine teaming principles. Conclusion: The study concludes that the Republic of Korea Armed Forces stand at a strategic crossroads in the evolution of military education and training. While existing infrastructures provide a solid base, the full potential of AI integration can only be realized through comprehensive reform of institutional design, curriculum philosophy, and interservice coordination. The future of effective military learning lies in harmonizing human cognitive adaptability with algorithmic precision. To achieve this transformation, three strategic initiatives are recommended: 1. Establish an AI-Integrated Joint Training Command to unify data resources, coordinate doctrine, and manage simulation ecosystems. 2. Develop the Korean Defense Learning Model (K-DLM) that combines AI analytics, experiential learning, and ethical leadership education. 3. Implement AI Literacy and Leadership Programs to cultivate officers capable of interpreting and supervising AI-generated insights responsibly.