AI-Enhanced Personality Modeling: Integrating Natural Language Understanding and Human Behavioral Signals for Adaptive Human-Computer Interaction
Keywords:
personality computing, natural language understanding, behavioral signal processing, adaptive interfaces, human-centered AI, multimodal fusionAbstract
Adaptive human-computer interaction systems that continuously tailor their behavior to an individual user require robust, multidimensional models of human personality. While psychological trait theories such as the Big Five provide a stable foundation, the computational inference of personality from natural language and behavioral signals introduces deep system-level challenges spanning architecture, data fusion, fairness, and sustainability. This paper presents a system-oriented analysis of AI-enhanced personality modeling that integrates natural language understanding with multimodal behavioral sensing to enable real-time adaptation in interactive environments. We examine the structural trade-offs among alternative modeling pipelines, from unimodal text-based estimation to hybrid architectures that fuse linguistic, paralinguistic, smartphone, and environmental data streams. The discussion extends beyond algorithmic performance to address critical infrastructure concerns, including latency-sensitive deployment architectures, the robustness of inference under distributional shift and concept drift, and the governance frameworks required to manage biases embedded in self-report training labels. A central theme is the interplay between the richness of integrated signals and the fragility of personality predictions when exposed to noisy, adversarial, or culturally heterogeneous inputs. By synthesizing perspectives from affective computing, federated learning, user modeling, and AI ethics, we articulate a research agenda that emphasizes cross-layer governance, energy-efficient edge inference, and policy-informed data stewardship. The analysis highlights that sustainable personality-adaptive systems must reconcile personalization accuracy with privacy preservation, and that transparent, contestable inference mechanisms are prerequisites for trustworthy deployment in education, healthcare, and workplace technologies.
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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.
