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.
Kuo et al. (Wed,) studied this question.
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