Automated Python-based AI learning text responder

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Summary:
Create a Python-based auto-reply system that detects, responds to, and learns from texts related to AI and educational content. The system should:

• Detect incoming messages related to AI or learning topics
• Auto-generate and send informative responses using a mix of pattern matching and AI/ML (where appropriate)
• Incorporate a learning loop by adapting responses using user feedback or previous messages

Implementation Notes:

• Leverage Python libraries for NLP (e.g., NLTK, spaCy, or transformers)
• Start with rule-based keyword/pattern detection and optionally integrate with ML/AI models as needed
• Consider exposing the reply system as a function or via an interface for easy integration (web, chat, etc.)

Tasks:

• Research and select the best approach for message filtering/detection
• Implement an initial pattern-based classifier
• Add hooks for integrating ML/AI-based response generators
• Design a simple interface (CLI, API, or web endpoint)
• Build a feedback mechanism to learn and improve

Expected Outcome:
A working Python module or script capable of automated, adaptive replies to AI and learning-related input text. The system should be modular for further enhancement.