Feasibility of human-in-the-loop multimodal generative artificial intelligence chatbot for school-based adolescent mental health support

Figure 1 Participant flow diagram illustrating screening, pragmatic allocation, follow-up, and analysis. This figure shows the participant flow, including the number of students screened, assessed for eligibility, pragmatically assigned to the intervention or waitlist control group, lost to follow-up, and included in the final analyses.

Figure 2 Human-in-the-loop architecture and supervised safety workflow of the Duoduo system. Schematic illustration of how verbal, visual, and vocal inputs were processed into a summarized emotional context to support acceptance and commitment therapy -guided dialogue generation and response delivery, alongside the human-led risk review and escalation workflow (Supplementary material). RAG: Retrieval-augmented generation; LLM: Large language model; ACT: Acceptance and commitment therapy.

Figure 3 Selected user-facing and monitoring interfaces of the Duoduo platform. A and B: The images show the user-facing chat interface and emotional feedback display; C: The intervention stage selection interface; D: The monitoring interface used to flag risk-related content for staff review (Supplementary material).

Figure 4 Distribution of Working Alliance Inventory-Short Revised total and subscale scores. Box plots show the median (line), interquartile range (box), and values within 1.5 × interquartile range (whiskers). WAI: Working Alliance Inventory.

Figure 5 Daily user messages and daily chatbot use duration across the intervention period. A: The number of user messages by day in the study (X-axis) and by participant (Y-axis). Color intensity represents the number of messages sent per day; B: The time of use (minutes) by day in the study (X-axis) and by participant (Y-axis). Color intensity represents the time of use per day.