Supporting Student Mental Health With the Safespace Generative AI Chatbot: Mixed Methods Feasibility Study

Clinical Snapshot

Key Findings

• P Value: Not reported; authors explicitly state inferential statistics were not performed due to insufficient sample size

• Effect Size: Not applicable (no inferential analysis conducted; all results are descriptive proportions)

• Primary Outcome: Feasibility confirmed at a preliminary level: 20 of 42 enrolled students (48%) actively used the chatbot, generating 286 user-chatbot interactions over 2–4 weeks. Highest engagement occurred in early morning and late-night hours.

• Nnt Or Sensitivity: Not applicable for this feasibility study. Exploratory finding: participants with elevated depression scores engaged in emotional disclosure in 99% of sessions (38 sessions, 8 participants) versus 84% of sessions (26 sessions, 12 participants) among those with low depressive symptoms — descriptive only, no statistical testing performed

• Confidence Interval: Not reported

Clinical Application

Implementation feasibility is partially supported for a blended-care model in university settings with existing counselling infrastructure. Technical barriers and design limitations identified by participants suggest the current iteration requires further development before broader deployment. The 48% active engagement rate from enrolled participants indicates meaningful dropout that must be addressed in future iterations. Australian university students face well-documented mental health challenges, with Beyond Blue and headspace data consistently showing high rates of psychological distress in this cohort. Australian universities are required under the National Guidelines for Student Mental Health to provide accessible mental health support. A GenAI chatbot offering off-hours support could complement existing services such as university counselling, Student Wellbeing Apps, and crisis lines (e.g., Lifeline 13 11 14). However, the TGA classifies software-based mental health tools as Software as a Medical Device (SaMD) under the Therapeutic Goods Act 1989, and any commercial deployment of Safespace or equivalent tools in Australia would require TGA conformity assessment. RACGP guidelines on digital mental health tools emphasise the need for evidence of safety and efficacy prior to recommendation. PBS listing is not relevant at this stage. Data privacy considerations under the Australian Privacy Act 1988 and the My Health Records Act 2012 would require careful attention given the sensitive nature of mental health disclosures to AI systems. University students aged approximately 18–30 years with mild-to-moderate mental health concerns, particularly those seeking support outside standard counselling hours or as a between-session adjunct to existing therapy. Not evaluated for students with severe mental illness, active suicidality, or psychosis.

Abstract

BACKGROUND: Generative artificial intelligence (GenAI) chatbots have the potential to provide personalized mental health support to individuals at scale. OBJECTIVE: This study evaluates the feasibility and usage patterns of the Safespace GenAI chatbot, an artificial intelligence (AI)-driven smartphone app that offers a large language model-powered interactive chatbot to support mental health. METHODS: Using a mixed methods approach, we explored baseline attitudes toward GenAI chatbots and chatbot usage patterns, conducted a qualitative content analysis of participants' experiences, and descriptively assessed patterns related to preintervention depressive symptoms. The study included an initial sample of 42 university students, 20 of whom actively used the chatbot over 2 to 4 weeks, generating 286 user-chatbot interactions. RESULTS: Preintervention surveys indicated that the majority of participants anticipated that the chatbot would be helpful (27/42, 64%) and that they trusted its privacy safeguards (39/42, 93%). Usage patterns suggested that the highest levels of interaction occurred early in the morning and late at night, when peer and professional support may be inaccessible. The qualitative analysis indicated that participants appreciated using the chatbot for reflection as a blended-care tool between their counseling sessions, while also naming technical barriers and specific design needs required to sustain engagement. In addition, our exploratory analyses descriptively showed that participants with elevated depression scores engaged in emotional disclosure during 99% (38 sessions with 8 participants) of their sessions, compared to 84% (26 sessions of 12 participants) of those with low symptoms. Due to the small sample size, future adequately powered studies are needed to inferentially examine these observed patterns. CONCLUSIONS: These findings provide initial insights into the usage and engagement dynamics of the Safespace GenAI chatbot and highlight directions for future research to optimize GenAI-driven mental health interventions.

References

1. 1.Pinna, M., Galletta, S., Ash, E., & Elmer, T. (2026). Supporting student mental health with the Safespace generative AI chatbot: Mixed methods feasibility study. JMIR Formative Research. https://doi.org/10.2196/85427