PersoPilot: An Adaptive AI-Copilot for Transparent Contextualized Persona Classification and Personalized Response Generation

TL;DR

PersoPilot is an adaptive AI system that combines persona understanding with contextual analysis to enable transparent, personalized interactions and insights for users and analysts, improving service personalization.

Contribution

It introduces PersoPilot, a novel AI-Copilot that integrates persona and context for adaptive, explainable personalization and active learning-based classification.

Findings

Supports natural language preferences and recommendations

Provides transparent, reasoning-powered labeling for analysts

Adapts over time with new labeled data

Abstract

Understanding and classifying user personas is critical for delivering effective personalization. While persona information offers valuable insights, its full potential is realized only when contextualized, linking user characteristics with situational context to enable more precise and meaningful service provision. Existing systems often treat persona and context as separate inputs, limiting their ability to generate nuanced, adaptive interactions. To address this gap, we present PersoPilot, an agentic AI-Copilot that integrates persona understanding with contextual analysis to support both end users and analysts. End users interact through a transparent, explainable chat interface, where they can express preferences in natural language, request recommendations, and receive information tailored to their immediate task. On the analyst side, PersoPilot delivers a transparent, reasoning-powered labeling assistant, integrated with an active learning-driven classification process that adapts over time with new labeled data. This feedback loop enables targeted service recommendations and adaptive personalization, bridging the gap between raw persona data and actionable, context-aware insights. As an adaptable framework, PersoPilot is applicable to a broad range of service personalization scenarios.