One possible solution could be to use a combination of natural language processing (NLP) and machine learning techniques. By analyzing the user's response to the AI's answer, the NLP algorithms could identify whether the user is correcting the AI or asking for more information. The machine learning algorithms could then use this information to improve the AI's response in the future.

For example, if the user corrects the AI's response to "what is 2+2?" by saying "no, it's 5 because you forgot to carry the 1", the AI could be programmed to recognize that it made a mistake and ask for more information. It could say something like "thank you for correcting me. Can you please explain how you arrived at that answer?"

By using a combination of NLP and machine learning, the AI could be trained to handle a variety of situations and respond appropriately, without hardcoding specific responses for each scenario. This would allow the AI to be more flexible and adaptable to changing situations and user needs.
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