Page 167 - AI for Good Innovate for Impact

AI for Good Innovate for Impact

2�3 Future Works

• Model Enhancements: Expand to detect complex emotions, add voice journaling with
speech-to-text, and include multimedia data (emojis, images).
• Collaborations: Partner with EdTech and HealthTech startups, integrate with university 4.1-Healthcare
student portals, and collaborate with AI mental health research labs.
• Expert Oversight: Involve licensed psychiatrists for AI validation, feedback, and ethical
oversight.
• Standardization: Review and standardize emotional markers using psychological scales,
Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder 7-item (GAD-
7).
• Actions: Offer affirmations, coping tips, or alert wellness teams for high-risk emotions
based on inferences.

3 Use case requirements

• REQ-01: It is critical that sentiment analysis on user-generated journal entries is done.
• REQ-02: it is critical that mood pattern detection is done over time.
• REQ-03: It is critical that a personalized affirmations are generated using CBT-based
guidelines and emotion regulation strategies.
• REQ-04: It is expected that User authentication via biometric (face/fingerprint).
• REQ-05: It is expected that an end-to-end encryption for stored emotional data will be
integrated.
• REQ-06: It is of added advantage that a dashboard for mood trends, triggers, and
emotional logs.
• REQ-07: It is expected that the habit tracking & future message features is integrated.

4 Sequence diagram

Figure 23�1: Sequence Diagram of our system

The proposed AI-powered journaling system follows the sequence diagram for core operations
that support student mental well-being through immediate emotional detection. The AI

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