Enhancing Adaptive Learning Through Spectrum of Individuality Theory: A Neuroplasticity-Informed AI Approach to Dynamic Behavioral Modeling in Education

Khritish Swargiary

LatIA, 2025, vol. 3, 72

Abstract: This study investigates the efficacy of integrating the Spectrum of Individuality Theory (SIT)—a dynamic, neuroplasticity-informed framework—into artificial intelligence (AI) systems for adaptive learning. Traditional AI models, rooted in static personality frameworks like the Five-Factor Model (FFM), often fail to capture real-time behavioral variability, limiting their adaptability. In a mixed-methods experiment, 120 undergraduate students were stratified into SIT-driven (n=60) and FFM-based (n=60) AI learning groups. The SIT system utilized real-time EEG and eye-tracking data to adjust content delivery, while the FFM system relied on fixed trait categorizations. Results demonstrated that the SIT group outperformed the FFM group in cognitive retention (mean post-test scores: 25.3 vs. 22.7; p

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:dbk:rlatia:v:3:y:2025:i::p:72:id:1062486latia202572

DOI: 10.62486/latia202572

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