Self-Correction Feedback Loops in Conversational AI Frameworks

Abstract

This article examines the role of self-correction feedback loops in enhancing the reliability and
coherence of conversational AI frameworks. The study evaluates how iterative response evaluation,
contextual alignment checks, and structured refinement mechanisms enable conversational agents to
reduce misunderstandings, maintain continuity across multi-turn dialogues, and adapt to evolving user
intent. Experiments conducted across simple information queries, clarification-based exchanges, and
multi-step goal-driven tasks demonstrate that systems with activated self-correction loops
significantly outperform baseline conversational models in clarity, recovery consistency, and sustained
dialog coherence. Quantitative evaluation further shows that these improvements come with only a
moderate increase in response latency, preserving real-time usability. The results highlight self
correction feedback loops as foundational components for building conversational systems that are
robust, adaptive, and suitable for deployment in real-world, continuously evolving interaction
environments.