Paper Type
ERF
Abstract
As the demand for accessible mental health interventions grows, AI-driven therapy offers a promising solution, particularly for stress management. This study investigates the potential of AI-driven therapy to improve emotional clarity and reduce the stigma associated with seeking mental health support. Drawing on Cognitive Behavioral Therapy (CBT), which links thoughts, emotions, and behaviors, the research investigates how AI therapists may impact emotional and behavioral outcomes. Using a three-group experimental design, the study compares the effects of AI therapists, journaling, and human therapists on stress management. The study aims to provide insights into AI’s role in stress reduction, emotional processing, and lowering barriers to therapy. By evaluating both traditional and AI-supported mental health interventions, this research contributes to the understanding of AI’s potential in the future of mental health care delivery.
Paper Number
1908
Recommended Citation
Wang, Runyu; Siau, Keng; and Zhang, Zili, "AI-Assisted Stress Management: Efficacy Compared to Traditional Methods" (2025). AMCIS 2025 Proceedings. 2.
https://aisel.aisnet.org/amcis2025/health_it/sig_health/2
AI-Assisted Stress Management: Efficacy Compared to Traditional Methods
As the demand for accessible mental health interventions grows, AI-driven therapy offers a promising solution, particularly for stress management. This study investigates the potential of AI-driven therapy to improve emotional clarity and reduce the stigma associated with seeking mental health support. Drawing on Cognitive Behavioral Therapy (CBT), which links thoughts, emotions, and behaviors, the research investigates how AI therapists may impact emotional and behavioral outcomes. Using a three-group experimental design, the study compares the effects of AI therapists, journaling, and human therapists on stress management. The study aims to provide insights into AI’s role in stress reduction, emotional processing, and lowering barriers to therapy. By evaluating both traditional and AI-supported mental health interventions, this research contributes to the understanding of AI’s potential in the future of mental health care delivery.
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