In a continual learning setting where concepts are dynamic or evolving, the proposed self-explaining architecture can be extended by incorporating mechanisms for concept adaptation and updating. One approach could involve implementing a concept drift detection system that monitors changes in the data distribution and triggers concept re-learning when significant drift is detected. This re-learning process can involve updating the concept prototypes based on the new data distribution to ensure that the model adapts to evolving concepts. Additionally, the architecture can include a memory module to store past concepts and experiences, allowing for efficient adaptation to new concepts while retaining knowledge of previous ones. By integrating these adaptive mechanisms, the self-explaining architecture can effectively handle dynamic concepts in a continual learning setting.