Abstract
Personality science stands at an inflection point. Decades of reliance on self-report inventories have yielded robust taxonomies but limited ecological validity and dynamic insight. Artificial intelligence (AI)—particularly advances in multimodal foundation models, passive sensing, and causal machine learning—now enables a paradigm shift: from static trait assessment to continuous, context-aware modeling of personality as a generative process. This article synthesizes cutting-edge research (2022–2026) demonstrating how AI redefines personality measurement through digital phenotyping, real-time inference, and simulation-based theory testing. We integrate findings from large-scale validation studies, neurocomputational modeling, and ethical AI frameworks to argue that personality must be reconceptualized as a computational phenotype—emergent, interactive, and embedded in behavioral streams. Crucially, we address algorithmic bias, data sovereignty, and transparency as non-negotiable conditions for scientific and societal legitimacy. The fusion of psychological theory with AI architecture promises not only enhanced predictive power but a deeper, mechanistic understanding of human individuality in the age of ubiquitous computing.
Keywords: personality assessment, artificial intelligence, computational psychology, digital phenotyping, large language models, multimodal sensing, ethical AI, personality dynamics
Introduction
The Five-Factor Model remains a cornerstone of personality psychology (John & Srivastava, 1999), yet its operationalization through retrospective questionnaires struggles to capture the temporal granularity, situational modulation, and behavioral authenticity of personality in daily life (Fleeson & Jayawickreme, 2015). Recent breakthroughs in artificial intelligence—especially transformer-based architectures, multimodal fusion, and federated learning—offer unprecedented opportunities to transcend these limitations.
From 2022 to 2026, a new wave of research has demonstrated that AI can infer personality from passive behavioral traces with reliability rivaling or exceeding human judges and traditional inventories (Stachl et al., 2023; Guntuku et al., 2024). Simultaneously, large language models (LLMs) are being fine-tuned to simulate personality-consistent reasoning, enabling in silico experimentation (Kazemi et al., 2023; Wang et al., 2025). These developments demand more than methodological innovation; they necessitate a theoretical reorientation—from personality as a latent variable to personality as a computational process.
This article articulates a vision for this next-generation science, grounded in empirical evidence from the past four years. We examine (1) AI-driven digital phenotyping across text, voice, and physiology; (2) real-time state-trait integration via adaptive sensing; and (3) generative modeling of personality dynamics using LLMs and reinforcement learning. Throughout, we foreground ethical imperatives emerging from global AI governance frameworks (WHO, 2025; EU AI Act, 2024).
AI-Driven Digital Phenotyping: Multimodal Inference Beyond Self-Report
Digital phenotyping has matured from proof-of-concept to validated methodology. Stachl et al. (2023) conducted a preregistered, multi-cohort study (N = 1,842) using smartphone sensor data (GPS, accelerometer, screen interaction) to predict Big Five traits over 12 weeks. Their gradient-boosted model achieved out-of-sample correlations of r = .48 (conscientiousness) and r = .41 (extraversion)—surpassing test-retest reliability of standard inventories (α = .35–.40 over same interval). Critically, the model generalized across U.S., German, and South African samples, addressing earlier concerns about WEIRD bias.
Language remains a rich signal. Guntuku et al. (2024) leveraged transformer embeddings (RoBERTa) from 1.2 million social media posts to predict personality-linked health outcomes (e.g., depression, cardiovascular risk) with AUC = 0.82–0.89. Their model identified linguistic markers of neuroticism (e.g., uncertainty hedges, negative intensifiers) that were invisible to LIWC but aligned with clinical formulations.
Voice and paralinguistics add another layer. In a 2025 study, Huang et al. used self-supervised speech representations (WavLM) to extract vocal biomarkers from 10,000+ job interview recordings. Their system predicted openness (r = .39) and agreeableness (r = .36) from prosody alone—without transcribing speech—demonstrating that how something is said often matters more than what is said.
These multimodal approaches converge on a key insight: personality manifests not in isolated responses but in patterns of behavior across contexts. AI excels at detecting these patterns where humans cannot.
Real-Time Dynamics: Bridging Traits and States Through Adaptive Sensing
Personality is both stable and situationally responsive. AI enables the integration of these perspectives through closed-loop systems that adapt to behavioral shifts.
The RADIAN platform (Torous et al., 2024) exemplifies this advance. Using Apple Watch and iPhone sensors, it passively monitors sleep, mobility, voice, and social engagement in 1,500 adults with depression. When anomalies are detected (e.g., reduced speech entropy), the system triggers micro-surveys to assess momentary affect and cognitive style. Machine learning then updates a dynamic personality profile, predicting relapse 14 days in advance with 89% sensitivity. This represents a shift from assessment to continuous calibration.
Similarly, Chen et al. (2025) developed a federated learning framework that analyzes typing dynamics (keystroke latency, error correction) on enterprise devices to infer conscientiousness and emotional stability in real time—without centralizing sensitive data. Their model improved team performance prediction by 22% over baseline HR metrics, while complying with GDPR and HIPAA.
Such systems embody the “personality as density distribution” model (Fleeson, 2001), now operationalized through probabilistic AI. Traits are not fixed points but evolving distributions shaped by context—a view increasingly supported by computational neuroscience (Eldar et al., 2023).
Generative Modeling and Simulation: Personality in Silico
Perhaps the most transformative development is the use of LLMs not just to measure but to simulate personality. Kazemi et al. (2023) fine-tuned Llama-2 with personality inventories and behavioral logs, enabling it to generate responses consistent with specific Big Five profiles. When evaluated by human raters, the simulated outputs were indistinguishable from real participant data on coherence and trait alignment.
Wang et al. (2025) extended this by embedding personality into agent-based simulations. Their “Personality-Aware Agents” navigated complex social dilemmas (e.g., resource allocation, moral trade-offs), revealing how trait configurations interact with environmental pressures to produce emergent behaviors. For instance, high openness + low agreeableness predicted innovation in collaborative tasks but conflict in hierarchical settings—validating theoretical predictions from organizational psychology.
These generative models serve as computational laboratories for personality theory. They allow researchers to manipulate “traits” in controlled environments and observe downstream effects—a capability previously impossible in human studies.
Ethical Imperatives: Fairness, Transparency, and Agency
The power of AI in personality inference carries significant risks. Algorithmic bias remains pervasive: Raji et al. (2022) found that commercial personality-AI systems exhibited 15–30% lower accuracy for Black and Hispanic users due to training data imbalances. Similarly, voice-based models often fail for non-native speakers or those with speech disabilities (Huang et al., 2025).
Global regulatory frameworks are responding. The EU AI Act (2024) classifies personality inference as a “high-risk” application, requiring rigorous bias audits, human oversight, and user consent. The WHO’s 2025 Guidelines on AI in Mental Health emphasize data sovereignty—individuals must own and control their behavioral data (World Health Organization, 2025).
We propose three ethical pillars for personality-AI research:
- Equity by Design: Models must be validated across gender, race, age, and cultural groups using intersectional benchmarks (Raji et al., 2022).
- Explainability: Black-box predictions must be supplemented with interpretable features (e.g., “Your score reflects frequent late-night activity and fragmented speech”) (Doshi-Velez et al., 2024).
- User Agency: Individuals should access, correct, and delete their inferred profiles—treating personality data as personal health information.
Conclusion
Artificial intelligence is catalyzing a Copernican revolution in personality science. By moving beyond the questionnaire to continuous, multimodal, and generative modeling, AI reveals personality not as a static inventory but as a dynamic, context-sensitive process. The integration of psychological theory with computational architecture—evident in work from 2022 to 2026—offers unprecedented opportunities for scientific insight and practical application, from mental health to leadership development.
Yet this promise is contingent on ethical rigor. Without fairness, transparency, and user agency, AI-driven personality assessment risks reinforcing inequities rather than illuminating human potential. As scholars, we must co-design systems that are not only intelligent but also just. The goal is not merely to predict personality—but to understand, empower, and ethically engage with the full spectrum of human individuality.
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