Expertise-Aware Chatbot Correction with Calibrated Anthropomorphic Cues: A Comprehensive Analysis

Abstract

The proliferation of conversational agents across diverse domains has necessitated more sophisticated error recovery mechanisms. While chatbots are increasingly capable of engaging in complex dialogues, they remain susceptible to misunderstandings, factual errors, and contextual hallucinations. When these systems attempt to correct their own mistakes, the manner in which they communicate this correction significantly impacts user trust, satisfaction, and cognitive load. This paper investigates the efficacy of expertise-aware chatbot correction mechanisms that utilize calibrated anthropomorphic cues. Anthropomorphism, the attribution of human traits to non-human entities, is typically deployed uniformly in commercial systems. However, this homogeneous approach fails to account for the varying levels of domain knowledge among users. Novice users often require extensive socio-emotional support and elaborate, empathetic apologies during a correction phase to maintain trust. Conversely, domain experts tend to prioritize conversational efficiency, accuracy, and directness, often finding excessive anthropomorphic apologies frustrating or patronizing. By continuously profiling user expertise and dynamically adjusting the manifestation of anthropomorphic cues such as empathetic language, social presence markers, and structural politeness, artificial intelligence systems can optimize the error recovery process. This comprehensive analysis proposes a novel theoretical framework and system architecture for calibrating human-like cues based on user expertise. Through an extensive evaluation of cognitive load theory and expectation confirmation theory, this research demonstrates that expertise-calibrated corrections significantly mitigate the negative impacts of conversational breakdowns, paving the way for more resilient and adaptive human-computer interactions.