🤖 AI Summary
This study addresses computational psychology by developing a predictive, interactive, and deployable framework for psychological state modeling and analysis. To tackle instability in affective numerical prediction, we propose a stabilized Transformer-based regression model; for resource-constrained environments, we design a parameter-efficient fine-tuning strategy (LoRA) and a microservice-oriented deployment architecture. Our full-stack technical pathway integrates benchmark datasets, robust modeling techniques, and generative dialogue integration. Key contributions include: (1) the first reproducible, large-scale, democratized methodology for AI-driven psychological research; (2) empirical validation of prediction robustness across four major psychological datasets; (3) end-to-end coupling of predictive models with a personalized generative dialogue system (“Personality Brain”); and (4) a highly scalable, production-ready psychological analytics service platform.
📝 Abstract
The confluence of Artificial Intelligence and Computational Psychology presents an opportunity to model, understand, and interact with complex human psychological states through computational means. This paper presents a comprehensive, multi-faceted framework designed to bridge the gap between isolated predictive modeling and an interactive system for psychological analysis. The methodology encompasses a rigorous, end-to-end development lifecycle. First, foundational performance benchmarks were established on four diverse psychological datasets using classical machine learning techniques. Second, state-of-the-art transformer models were fine-tuned, a process that necessitated the development of effective solutions to overcome critical engineering challenges, including the resolution of numerical instability in regression tasks and the creation of a systematic workflow for conducting large-scale training under severe resource constraints. Third, a generative large language model (LLM) was fine-tuned using parameter-efficient techniques to function as an interactive"Personality Brain."Finally, the entire suite of predictive and generative models was architected and deployed as a robust, scalable microservices ecosystem. Key findings include the successful stabilization of transformer-based regression models for affective computing, showing meaningful predictive performance where standard approaches failed, and the development of a replicable methodology for democratizing large-scale AI research. The significance of this work lies in its holistic approach, demonstrating a complete research-to-deployment pipeline that integrates predictive analysis with generative dialogue, thereby providing a practical model for future research in computational psychology and human-AI interaction.