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Related Experiment Video

Updated: Jul 12, 2026

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models

Published on: December 23, 2025

Conversational AI for Child Abuse Detection Through Multistage Counseling: Model Development and Validation Study.

Hyun-Young Moon¹, Youn-Gyu Jin¹, YoonJu Kim¹

¹Department of Artificial Intelligence, The Catholic University of Korea, Bucheon, Republic of South Korea.

Journal of Medical Internet Research

|July 10, 2026

Summary

This study introduces Conversational Artificial Intelligence for Child Abuse Detection (CACAD), an AI framework that supports counselors by detecting child abuse during counseling sessions. CACAD effectively aids in early intervention and reduces professional workload.

Keywords:

child abuse detection conversational artificial intelligence counseling large language model pretrained language model

Related Experiment Videos

Last Updated: Jul 12, 2026

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models

Published on: December 23, 2025

Area of Science:

Artificial Intelligence
Child Psychology
Clinical Informatics

Background:

Child abuse has severe, long-term physical and emotional consequences.
Increased reported cases and a shortage of professionals strain child abuse services.
Timely intervention is crucial but challenging due to high workloads.

Purpose of the Study:

To reduce counselor workload in child abuse detection and counseling.
To introduce a conversational AI framework (CACAD) for child abuse detection.
To support both counseling and abuse identification processes.

Main Methods:

Utilizing a large language model (LLM) for counseling and detecting four types of child abuse (neglect, emotional, physical, sexual).
Employing auxiliary modules for question generation, next question category prediction, and abusive question filtering.
Implementing an instruction-tuned LLM with uncertainty quantification for reliable abuse detection and flagging cases for review.

Main Results:

CACAD achieved high performance in child abuse detection (exact match 0.907, macro-F1 0.939) on a Korean dataset.
Demonstrated effectiveness in counseling tasks like next question prediction and abusive question detection.
Expert evaluation confirmed CACAD's reliability, with uncertainty-based prediction identifying cases needing human review.

Conclusions:

LLM-based conversational agents can reliably detect child abuse during counseling.
CACAD integrates abuse detection, safe question generation, and uncertainty handling into a unified framework.
Such AI systems can significantly support counseling processes and improve child welfare outcomes.