Fraunhofer Develops AI Supported Virtual Learning Environment to Enhance Bundeswehr Training and Teaching Quality

© Philipp Plum / Fraunhofer FOKUS
A virtual learning environment is to support soldiers and instructors in the future.

(IN BRIEF) Fraunhofer researchers have studied how artificial intelligence can be integrated into the Bundeswehr’s planned virtual learning environment to improve training and teaching quality. The project evaluated several AI functions, including a secure chatbot, a competence assessment tool called KoApp, personalized learning pathways and a recommendation system with learning progress tracking. The chatbot, which operates in a closed network and provides instructor-approved answers, was particularly well received by participants and proved useful for navigating complex regulations. The system also allows instructors to monitor individual and group learning progress through visual dashboards. Developed by Fraunhofer FKIE in collaboration with Fraunhofer IOSB and Fraunhofer FOKUS, the project demonstrates how AI can enhance digital education while remaining an optional support tool for instructors. Further studies are planned to expand the system’s capabilities and support long-term learning across the Bundeswehr.

(PRESS RELEASE) MUNICH, 3-Mar-2026 — /EuropaWire/ — Fraunhofer researchers are exploring how artificial intelligence can enhance digital education for the Bundeswehr by improving learning outcomes and supporting instructors through intelligent tools. In a recent study, researchers examined how AI-based functions could be integrated into the military’s planned virtual learning environment and evaluated their potential benefits for both learners and teaching staff.

The Bundeswehr intends to establish a comprehensive virtual learning environment for its personnel. Currently, soldiers rely on conventional learning management systems such as Moodle to build competencies in areas including mathematics, language training and social skills, as well as to prepare for examinations. The joint research project focused on identifying AI-supported features that could expand the capabilities of these existing platforms.

The study was led by the Fraunhofer Institute for Communication, Information Processing and Ergonomics FKIE with contributions from the Fraunhofer Institute for Optronics, System Technologies and Image Exploitation IOSB and the Fraunhofer Institute for Open Communication Systems FOKUS. Researchers followed a structured process that began with a detailed requirements analysis involving learners, instructors, supervisors and training planners. Their input was translated into practical user scenarios, which were then evaluated to determine which functions could be realistically implemented and how they would improve learning and teaching processes.

Several AI-supported tools were tested during the project. One of the key elements was a secure chatbot designed to operate in a closed network environment, allowing sensitive information to be handled safely. The chatbot provides reliable, quality-controlled answers based on instructor-approved content and can include references to original sources. Participants frequently used the chatbot to locate relevant information and navigate complex regulations, and many praised its ease of use.

Researchers also evaluated the integration of a competence assessment application known as KoApp, which connects directly to the learning management system. The monitored course focused on practical knowledge and was not graded, making it difficult to evaluate learning progress with traditional methods. KoApp addresses this issue by documenting acquired skills and presenting them visually, enabling instructors to monitor individual progress and tailor their guidance accordingly.

Instructors also benefited from a dashboard that provides statistical insights into the knowledge level of each course group. Additional features included personalized learning pathways generated by AI, allowing participants to follow alternative sequences of course material based on their individual needs. A recommendation system combined with a learning progress tracker helped participants manage their studies more effectively.

Because the AI models adapt to individual learning progress, users can proceed at a pace suited to their abilities, avoiding both excessive workload and unnecessary delays. This individualized approach improves both the quality of instruction and the overall learning experience.

The project also relied on a learning middleware system previously developed by Fraunhofer researchers. This software enables different AI components to communicate with each other despite varying data formats and simplifies their integration into existing learning platforms.

Future research is expected to involve additional training facilities and user groups to further refine the system. The long-term objective is to support continuous professional development through optional AI-based tools that assist instructors while preserving their central role in the training process.

Media Contact:

Silke Wiesemann
Head of PR and Communications
Fraunhofer Institute for Communicaton, Information Processing and Ergonomics FKIE
Fraunhoferstraße 20
53343 Wachtberg, Deutschland
Phone +49 228 9435-103
silke.wiesemann@fkie.fraunhofer.de
www.fkie.fraunhofer.de

SOURCE: Fraunhofer-Gesellschaft