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

2026-02-04

AI Summary

This work proposes an AI collaborator framework that addresses the common limitation in existing personalized systems—namely, the disjointed treatment of user profiles and contextual information, which hinders precise adaptive interaction. By establishing a bidirectional feedback loop between the user and an analyst agent, the framework jointly models profile understanding and context awareness. Built upon an agent-based architecture, the system integrates interpretable natural language interaction, active learning–driven dynamic categorization, and context-aware personalized response generation. Empirical results demonstrate significant improvements in both user profile classification accuracy and interactive experience, yielding an interpretable, evolvable framework for personalized services across diverse scenarios.

Technology Category

• Humans and AI: Intelligent User Interfaces
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Multiagent Systems: Adversarial Agents

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• persona classification
• contextualized personalization
• adaptive interaction
• context-aware insights
• personalized response generation

Innovation

• persona-context integration
• adaptive personalization
• explainable AI
• active learning
• agentic AI-copilot