Intelligent Self-Correction Feedback System (ASCQ): Enhancing AI Response Accuracy through Human-Driven Evaluation Loops

This research introduces the "Intelligent Self-Correction Feedback System (ASCQ)," a novel concept designed to enhance the performance and reliability of AI-generated outputs by integrating real-time human feedback into the correction loop. The system identifies common patterns of mistakes made by AI models and dynamically updates response behavior based on contextual human corrections, aiming to increase precision, credibility, and adaptability over time. This paper lays the groundwork for future implementations in educational, medical, and customer service applications.