Integrated prevention-intervention framework connecting adaptive survey chatbots to mental health assessment tools

Design · All designs
Evidence · 3 claims (3 for) · 1 study (1 design), q2 · 1 of 1 report an effect size · 3 claims rest on one study

Description

The dissertation's proposed framework (Objective 3) unifies the preventive layer (TigerGPT plus AURA) with the intervention layer (PsychoGPT and SMMR). "This design allows insights from survey chatbot's adaptive dialogues, such as emerging student concerns or early signs of distress, to flow directly into specialized models like PsychoGPT or SMMR." By aligning both strategies under one system, administrators can enhance campus-wide satisfaction initiatives while identifying and supporting individuals experiencing psychological challenges.

Design Implications

Context

Requirements

• Deployment of both the survey chatbot layer and the mental health assessment models within campus well-being services

Constraints

• The intervention tools were tested on public datasets rather than live campus data; the dissertation states they demonstrate capabilities with potential to be adapted for real campus contexts

Target Learners

• university students, faculty, and staff

Learning Goals

• campus satisfaction monitoring and early detection of psychological distress

Claims

• Aura Rl Improves Response Quality [+M]
• Psycho Analyst F1 Daic Woz [+M]
• Smmr Outperforms Single Model [+M]

Related Designs

Examples

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Key Sources

• Jinwen Tang. (2026). NEW AI-DRIVEN TOOLS FOR ENHANCING CAMPUS WELL-BEING: A PREVENTION AND INTERVENTION APPROACH. Doctoral dissertation, University of Missouri-Columbia. https://arxiv.org/abs/2605.10804

Page metadata

Field	Value
type	design
id	integrated-prevention-intervention-ai-framework
title	Integrated prevention-intervention framework connecting adaptive survey chatbots to mental health assessment tools
description	The dissertation's proposed framework (Objective 3) unifies the preventive layer (TigerGPT plus AURA) with the intervention layer (PsychoGPT and SMMR).
status	review
generated	by process:settle-candidates on 2026-10-10
trust	unverified — no one has confirmed this page's content
sources	1 — jinwen-tang-2026

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