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All Journal Jurnal Studi Agama dan Masyarakat SAMARAH: Jurnal Hukum Keluarga dan Hukum Islam Pena Justisia: Media Komunikasi dan Kajian Hukum Jurnal Ilmiah Profesi Pendidikan Al-MAIYYAH : Media Transformasi Gender dalam Paradigma Sosial Keagamaan Hunafa: Jurnal Studia Islamika International Journal of Research in Counseling PANDALUNGAN : Jurnal Pengabdian kepada Masyarakat Journal of Islamic Education and Pedagogy
Sapruddin Sapruddin
Fakultas Syariah IAIN Palu
Religion Arts Humanities Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Other

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Journal : International Journal of Research in Counseling

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Early Intervention Strategies for Self-Harm Prevention Using AI-Driven Behavior Tracking in Teenagers Samsinas, Samsinas; Sapruddin, Sapruddin; Widoyo, Heru; Marzuki , Marzuki
International Journal of Research in Counseling Vol. 4 No. 1 (2025)
Publisher : Yayasan Minang Darussalam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70363/ijrc.v4i1.306

Abstract

Background. Self-harm among teenagers has become an increasing public health concern, often linked to emotional distress, social pressure, and undiagnosed mental health issues. Traditional intervention strategies often detect these behaviors after they occur. The emergence of artificial intelligence (AI) in mental health opens new possibilities for earlier detection and proactive intervention, especially through behavior tracking technologies. Purpose. This study aimed to explore early intervention strategies for self-harm prevention by utilizing AI-driven behavior tracking tools among teenagers. The research also examined the potential effectiveness of AI in identifying early warning signs based on digital behavior patterns. Method. This mixed-methods study involved 150 teenagers aged 13–18 across three urban schools. AI-based applications were installed on participants’ devices with consent to monitor digital activity patterns (e.g., sleep irregularities, social withdrawal, online search behavior). Psychological assessments and structured interviews were also conducted. Data were analyzed using a combination of statistical trend analysis and qualitative content analysis. Results. Findings indicate that AI algorithms successfully detected behavioral anomalies correlated with self-harm risk, such as significant decreases in social interaction, increased usage of depressive language, and disrupted sleep patterns. The AI tool enabled counselors to initiate timely interventions before self-harm behaviors escalated. Participants reported greater emotional safety and support when interventions occurred early. Conclusion. AI-driven behavior tracking shows promise as an early intervention tool for preventing self-harm in teenagers. Integrating such technology with school counseling programs could enhance mental health support systems. However, ethical concerns regarding privacy and data sensitivity must be addressed to ensure responsible implementation.
Co-Authors Abidin Abidin andi intan cahyani, andi intan Arief Budiono Ayunira, Lia Martha B, M Taufan Batara Indra, Andi Darussalam, Fajrul Ilmy Emiyanti, Emiyanti Fitriyana Fitriyana, Fitriyana Hamiyuddin, Hamiyuddin Hanafi, Muhammad Amin Kartika, Marshanda Dwi Marzuki Marzuki Marzuki Mera, Nasaruddin Murniati Ruslan Nawas, M Zuhri Abu Rahelli, Yeti Samsinas Samsinas Syahruddin Syahruddin Taufan B., M. Ubay, Ubay Widoyo, Heru