Exploration of Mental Health and Stress Among Nursing Interns Through a Chatbot

Background: Nursing interns face early clinical distress; chatbot-based mental health tools show promise, although evidence of their feasibility and educational value remains insufficient. Purpose: To evaluate the application feasibility and effectiveness of the Xiao Ling Assistant Chatbot (X-LAC) for addressing mental health challenges, monitoring stress, and detecting early warning signals of psychological distress among nursing students participating in a clinical internship. Methods: A 4-week single-group study with 61 nursing interns tested X-LAC’s daily chatbot-based support and keyword-triggered alerts; pre/post mental health and stress were assessed. Results: Well-being improved (12-20); suicidal ideation declined (10-4). The chatbot flagged 16 high-risk expressions per 100 messages, notably so tired and under pressure; 60.6% reported internship distress. Conclusion: Chatbot support shows promise for clinical training, reducing stress and enabling early detection. Integrating artificial intelligence for risk prediction is warranted while retaining human oversight for critical cases.