Leadership Styles and Educational Satisfaction in Military Universities: Rapport with MZ Learners in the AI Era
DOI:
https://doi.org/10.63332/joph.v5i6.2347Keywords:
Leadership Styles, MZ Generation, Rapport, Educational SatisfactionAbstract
This study investigates the role of instructors’ leadership styles in fostering rapport and enhancing educational satisfaction among MZ generation cadets at military-related special-purpose universities in South Korea, within an AI-driven educational landscape. Integrating transformational, fun, and servant leadership, the research examines how rapport mediates the relationship between these styles and educational satisfaction, assessing their effectiveness in AI-enhanced settings. A survey of 280 cadets from three military academies and ROTC programs was conducted, with data analyzed using SPSS version 25.0 through exploratory factor analysis, multiple regressions, and mediation analysis. Findings reveal that all leadership styles significantly influence rapport, with fun leadership showing the strongest effect (β = 0.404), followed by servant (β = 0.240) and transformational leadership (β = 0.145). Rapport significantly enhances educational satisfaction (R² = .729) and partially mediates the leadership-satisfaction relationship, with transformational leadership exhibiting the greatest total effect (0.797). Notably, while fun leadership effectively engages MZ learners through AI tools like ramified learning modules, its influence on satisfaction via rapport is weaker compared to transformational and servant leadership. These results underscore the need for a diversified leadership approach that balances AI technologies with human-centered engagement to align with generational characteristics. Instructors are encouraged to adopt flexible leadership strategies to maximize educational satisfaction in AI-enhanced military education.
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
CC Attribution-NonCommercial-NoDerivatives 4.0
The works in this journal is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
