JMIR Publications

ABSTRACT

Background:

Child abuse severely disrupts the healthy growth and development of children, resulting in long-term physical as well as emotional consequences. However, in real-world, despite the continuous increase in reported abuse cases, the chronic shortage of certified child-abuse professionals has significantly increased the workload of individual counselors, making timely intervention increasingly difficult.

Objective:

To reduce counselors’ workload and enhance the efficacy of child abuse detection, we propose Conversational AI for child abuse detection (CACAD).

Methods:

CACAD utilizes a large language model (LLM) to conduct counseling and detect four types of child abuse: neglect, emotional, physical, and sexual. During the question generation process, the LLM serves as the primary agent, supported by two auxiliary modules. In the process of the counseling, abusive question detection module filters out harmful questions to protect children, while the next question category prediction module selects the most appropriate question to guide the counseling flow more flexibly. In addition, during abuse prediction, CACAD leverages uncertainty quantification to dynamically flag cases as pending review for counselor confirmation.

Results:

Experimental results using a Korean child-and-adolescent counseling dataset show that CACAD achieves an exact match of 0.907 and a macro-F1 score of 0.939 in predicting child abuse categories. Furthermore, in human evaluation, CACAD demonstrated highly reliable performance in counseling sessions.

Conclusions:

These findings highlight the potential of LLM-based conversational agents to effectively support detection of child abuse through counseling in real-world.