This conceptual article undertakes a theoretical adaptation of competency frameworks, taking the global competency learning approaches as domain theory and examining them through a decolonial and critical psychology lens as method theory.
5G Advanced and 6G V2X Environments require intelligence that is real-time, resource-light and robust across high-density vehicle mobility, non-stationary channels and hard ware constrained edge nodes. Existing AI-driven resource man agement approaches — including CNNs, LSTMs, GNNs, Deep Reinforcement Learning, Transformers, and Federated Learning — have limitations, such as high computational overhead, large data requirements, poor generalization under topology churn, and catastrophic forgetting under continuous retraining.