Year: 2025
Type: conference-paper
Abstract: This study tackles the vital gaps present in how military education systems train people for UAV reconnaissance. By closely examining the development of drone technology, the uses of military reconnaissance, and the effects on modern warfare, we were able to identify the systemic problems within the current curricula. Our proposed reform combines AI, machine learning (ML), and the analysis of big data. We also incorporate some creative teaching methods like simulation training and project-based learning (PBL). The results show some quite remarkable improvements: the operational readiness has increased by 30%, the mission success rates have hit 90% (compared to an initial 60%), and the decision-making has become 25% quicker. Regarding implementation, it requires allocating resources in stages, conducting faculty development programs, and having strategies to reduce the risks related to adoption. The three-year reform framework includes continuous evaluation and the integration of cross-disciplinary technology to ensure it remains applicable on the battlefield. UAV reconnaissance holds great significance these days. The combination of AI with it has led to numerous changes. When considering curriculum reform in this relevant field, it's crucial to think about how to incorporate elements such as simulation training in a better way. Simulation training can play a vital part in strengthening the understanding and skills related to tactical decision-making. Additionally, swarm technology is an area that simply can't be overlooked. It has an influence on the overall operation and effectiveness. And adaptive learning is another important aspect, which is helpful for making adjustments and improvements according to different situations and requirements.
Authors: Hongyan Wang, Yunfei Ling, Xiaofeng Wang
Institution: PLA Air Force Aviation University
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W4414386162
FWCI:
Cites:
Cited by:
Related to: