IMPACT FACTOR : 5.212

Evaluating the Efficacy and User Experience of AI-Based Mental Health (KEY IJW**********218)

• Sora Pazer

Abstract

This study examines young adultsperceptions of an AI-based mental health support system, focusing on comfort, perceived mood detection accuracy, timeliness of depression alerts, and recommendation helpfulness. A sample of 61 participants aged 16 to 30 completed a survey assessing these factors on a 5-point Likert scale, supplemented by usage frequency and access methods. Results indicated high comfort levels in emotional sharing, moderate satisfaction with mood detection accuracy, and positive responses to alert timeliness. However, limitations such as lack of personalization and the potential for alert fatigue highlight the challenges of relying solely on AI for mental health support. Correlation analysis further revealed that younger users and those with previous therapy experience reported higher satisfaction levels. This study underscores the value of AI in enhancing mental health support accessibility, while also advocating for hybrid approaches that integrate human oversight to address complex psychological needs. Future research should explore long-term engagement, personalization algorithms, and ethical considerations to optimize AI-based mental health interventions.