A Critical Review of Digital Mental Wellness Systems for University Students

The rapid growth of psychological distress among university students has intensified the need for scalable, technology‑driven mental health interventions. Traditional counseling infrastructures are frequently constrained by limited resources and delayed response times. Consequently, digital mental wellness systems integrating mobile applications, web‑based cognitive behavioral therapy (CBT), artificial intelligence (AI), predictive analytics, and sensor‑based monitoring have emerged as viable alternatives. This review synthesizes contemporary research contributions in these domains, examining their therapeutic effectiveness, scalability, and ethical implications. The study compares architectural models and intervention strategies to identify converging evidence and research gaps. Findings indicate that integrated hybrid systems combining structured CBT modules, AI‑assisted interaction, and predictive risk detection provide the most sustainable framework for student mental health support within academic institutions.