Cognitive reinforcement for aircrew coordination with autonomous collaborative platforms in next-generation fighters
High-tempo domains such as defense, medicine, and transportation have been transformed by AI-based systems that accelerate information processing and decision support. Modern air combat reflects this evolution, with automated systems continuously generating tactical options that require aircrews to decide more frequently under time pressure. This shift is exemplified by the deployment of Autonomous Collaborative Platforms (ACPs), i.e., AI-enabled teamed drones operating alongside crewed fighters and proposing actions such as target validation. This study examines whether the processing of target-validation situations proposed by ACPs can be improved through cognitive training. Forty military aircrew members participated in a virtual reality flight mission and were assigned to either a control or a training group. Both groups completed identical pretest and posttest separated by 24 hours, while only the training group performed a 45-minute computer-based training session focused on repeated target-validation tasks. Results show that training significantly reduced error rates and response times, in line with theories of expertise acquisition. In contrast, perceived workload decreased similarly in both groups, suggesting a dissociation between objective performance gains and subjective workload. These findings support the relevance of cognitive training for preparing aircrews for future collaborative combat involving ACPs.