Personalized Prompt Learning for User-Centric Conversational AI Systems
Keywords:
personalized prompts, conversational AI, parameter-efficient tuning, user modeling, fairness, system architectureAbstract
The rapid proliferation of large language models has reshaped conversational artificial intelligence, yet a critical gap remains in adapting these systems to individual users without costly fine-tuning or static prompt engineering. Personalized prompt learning emerges as a system-level paradigm that dynamically modulates instruction representations to align with user preferences, interaction histories, and contextual goals. This paper presents an interdisciplinary analysis of personalized prompt learning architectures, situating the approach within the broader landscape of parameter-efficient adaptation, user modeling, and socio-technical infrastructure. We examine structural trade-offs between centralized prompt optimization and on-device personalization, highlighting the interplay of latency, privacy, and model capacity. Governance frameworks are proposed to address fairness, representation harm, and the risk of echo chambers when prompts are continuously tuned to user behavior. Deployment considerations span edge-cloud orchestration, sustainability metrics, and lifecycle management of prompt modules. Robustness is analyzed through adversarial user inputs, distributional shifts, and feedback loops that may amplify biases. We further discuss policy implications for transparency, auditability, and user agency in systems that learn from interpersonal interaction. By synthesizing advances in selective prompt insertion, instruction tuning, and federated learning, we outline a reference architecture that treats prompts as adaptive, user-specific mediators between foundation models and situated dialogue. The paper contributes a comprehensive systems perspective, moving beyond algorithmic novelty to address the infrastructural and ethical conditions under which personalized conversational AI can be responsibly realized.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
