Soonchunhyang University
Convergence Research Center for Emotional & Intelligent Child-Care Systems

Government R&D Strategist · AI Convergence Architect · Research Program Designer

Designing empirically validated, human-centered AI systems aligned with national R&D governance frameworks.

Metaverse-based developmental assessment · Psychology-grounded LLMs · Quantitative AI evaluation · Digital behavioral analytics

Research centered on AI-enabled developmental diagnostics, digital communication–based emotional assessment, and structured evaluation architectures for public-sector deployment.

• AI-based affective recognition systems
• VR-based developmental disorder screening platforms
• Integrated child-development data convergence models
• National-level KPI benchmarking frameworks

Designing research roadmaps, integration logic, and performance governance structures across programs.

• Policy-driven problem structuring
• Technical differentiation modeling
• Stage-gated development planning
• KPI-based validation systems
• Government review and evaluation response strategy development

Bridging academic research with institutional accountability and national evaluation standards.

Immersive VR environments for early detection and behavioral analytics.
AI inference systems embedded within scalable child-care ecosystems.

LLM architectures grounded in cognitive and psychological theory.
Human-aligned reasoning models with measurable interpretability metrics.

KPI-driven benchmarking pipelines for human-centered AI.
Structured validation frameworks aligned with national evaluation standards.

AI-enabled restructuring of public and child-care infrastructures.
Performance-measurable institutional deployment models.

Developing AI-based stress and emotional assessment models using digital communication data.

• Integration of conversational text and behavioral signals (response latency, editing patterns, interaction dynamics)
• Generative AI–based data augmentation for counseling dialogue expansion
• Transfer learning–enhanced Korean emotional prediction models
• Explainable AI (SHAP, LIME) for indicator extraction and interpretability
• Statistical validation against standardized psychological scales (e.g., PSS, DASS-21)

This research strengthens quantitative evaluation frameworks within human-centered AI systems.

• AI-driven mental health assessment systems
• Digital transformation in public and child-care infrastructures
• Behavioral signal analytics in conversational AI
• Explainable and statistically validated AI governance models

Open to interdisciplinary and international collaboration.

📍 Soonchunhyang University
📧 monicakim89@sch.ac.kr
📧 chomyung8912@gmail.com
🐙 GitHub: @monicakim89