Research on Integrating Narrative Simulation and Visual AI into Teaching Management to Build Mental Health Capacity in Universities

Summary

This study integrates narrative simulation and visual AI into teaching management systems to improve university students' mental health, emotional resilience, and engagement.

Abstract

University students increasingly face mental health challenges, including anxiety, depression, and stress, yet most higher education environments lack proactive systems for emotional monitoring and support. This study aimed to design, implement, and evaluate a hybrid teaching management system that integrates narrative simulation with visual artificial intelligence (AI) to promote mental health literacy and emotional resilience among students. The system includes a narrative decision-making module simulating stress scenarios and an AI-powered emotion recognition tool (based on facial expression detection) embedded in classroom settings. A mixed-methods design was employed with 332 undergraduate students and 15 faculty members from multiple universities. Pre- and post-intervention surveys, usage logs, and real-time emotional data from visual AI were collected. Quantitative data were analyzed using descriptive statistics, paired t-tests, ANOVA, and multiple linear regression. Results indicated statistically significant improvements in students' mental health scores (p < 0.01), emotional awareness, and decision-making confidence. This integrated approach demonstrates both usability and scalability, offering instructors early emotional insight and students a reflective learning environment. The method is best suited for institutions equipped with AI-capable classrooms and trained ethical oversight.

Introduction

Mental health has become a pressing concern in higher education, with rising levels of anxiety, depression, and burnout reported among university students worldwide¹. These psychological burdens, often linked to academic overload, social pressures, and financial uncertainty, negatively impact academic performance, retention, and long-term well-being². While many institutions have implemented counseling services and crisis interventions, such approaches tend to be reactive and limited in reach³. There is a growing demand for proactive, scalable, and embedded strategies that support students' ps....

Protocol

Prior to data collection, ethical approval was obtained from the Institutional Review Board of Nanjing University of Posts and Telecommunications (Approval Code: NJUPT-IRB-2024-0312). All participants provided written informed consent after being briefed on the study scope, the voluntary nature of participation, the use of visual AI for emotion tracking, and data confidentiality procedures. Participation could be discontinued at any time without penalty. The software and equipment used are listed in the Table of Materials.

1. Study preparation

1. Definition of the operational timeline and end-to-end flow
   Two....

Results

Descriptive analysis

Pre-post comparisons indicated consistent declines in adverse affect following implementation. Post-test means decreased to 2.87 for anxiety, 2.74 for depression, and 2.69 for stress, from pre-test means of 3.25, 3.11, and 3.08, respectively (Figure 2). Descriptive outcomes are summarized in Table 4, and the item-level instrument structure is provided in Table 3. To evaluate AI reliability wit.......

Discussion

The study demonstrates that when narrative simulation is combined with visual AI, emotional awareness becomes a shared process rather than a one-directional intervention. Students were not merely responding to a digital tool; they were engaging in a structured emotional reflection that connected cognitive appraisal with embodied experience. Across the dataset, both quantitative and qualitative results revealed a consistent pattern of reduced anxiety and improved emotional regulation, suggesting that the protocol can func.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Hardware	Facial Recognition Camera	Logitech (or similar)	C920 HD Webcam
Instrument	Interview Transcript Collection	Manual entry	Audio recordings/text
Questionnaire	Modified DASS-21 Scale	Adapted by authors	5-point Likert format
Software	Microsoft Azure Emotion API	Microsoft	Emotion API v3 (Cloud)
Software	SPSS Statistics	IBM	Version 26
Software	Narrative Simulation System	Self-developed	Web-based (HTML5)
System Platform	University Teaching Management System	Internal platform	Integrated with AI module