Case study
Mental health AI screening platform
Screened 6,000 students for mental health risk in just over a week, flagging cases manual review had missed.
Industry
Education / Healthcare
Key result
6,000
Students assessed
Timeline
1 week screening run
Services
Education
The Challenge
The Challenge
Mental health is one of the biggest issues facing students today, but identifying which students need help is hard. You cannot just look at a student and know, and there are thousands of them. The traditional approach was simple: a counselor sits down with a student for about an hour, asks questions, and writes up an assessment. That works well for one student. It falls apart completely when you have thousands. A district trying to screen everyone the old way would take months, or years.
Most districts never even tried, and at-risk students stayed invisible until something went wrong. Fidelity Healthcare Group serves students across the Southeastern United States and knew the problem was bigger than any counselor team could handle alone. They needed a way to screen a large student population quickly, securely, and accurately — without replacing the human judgement of trained counselors. The AI had to help, not take over.
The challenge was not just speed. The data had to stay private. The surveys had to reach students across different schools and devices. The results had to be easy for counselors to act on. And the whole system had to be reliable enough to trust when a student's wellbeing was on the line. The platform had to scale screening to every student, surface the ones who needed attention, and put counselors back where they belong — having real conversations and making a real difference.
The Solution
The Solution
We built an automated survey distribution system that sends assessments to students across the district without requiring counselors to manually reach out to each one. Students complete the survey on any device — phone, tablet, or desktop — and the platform handles distribution, reminders, and response collection automatically, turning what used to be a one-on-one time sink into a scalable, district-wide process. That is how 6,000 students got assessed in just over a week instead of months.
The AI analyzes student responses against validated clinical frameworks for depression and mental health risk. It looks at combinations of signals — sleep patterns, mood, social connection, concentration — that individually might seem small but together paint a clearer picture. It surfaces students whose response patterns match early indicators of concern and places them in a prioritized counselor review queue. The AI never diagnoses — it flags, and trained humans decide what happens next. This combination caught students that pure manual review had been missing.
The frontend was built in React and Next.js, designed alongside educators and students to feel approachable rather than clinical. Students see a clean, straightforward survey experience that does not feel intimidating. Counselors get a dashboard showing flagged students, risk levels, and the specific signals that triggered each flag. Administrators see Google Looker analytics dashboards with district-wide trends — which schools have higher rates of concern, how response rates track over time, and where to focus resources next.
The backend is built in C# and hosted on Microsoft Azure. Azure SQL Database stores all student data with encryption at rest, and the system uses role-based access controls so only authorized staff see sensitive information. Amazon SES handles automated email notifications, alerting counselors when a student is flagged and sending survey reminders to students who have not yet responded. The stack was chosen for compliance, performance, and the ability to scale across a large district without degrading.
Results
Results
6,000
Students assessed in just over one week
1 week
vs. months with manual screening
AI
Catches at-risk students that manual review missed
District-wide
Deployment across the Southeastern US
1 hour
Manual assessment time per student, before
FERPA-compliant, Azure secure hosting
Compliance
Before & after
Before and after
Before
Assessment took 1 hour per student, done manually by counselors
6,000 students processed in just over one week
District-wide screening was impractical — too slow, too costly
Automated survey distribution runs without adding staff workload
At-risk students missed because there was no way to screen everyone
AI-powered risk analysis identifies students manual review missed
No centralized data — no visibility into district-wide trends
Google Looker dashboards give administrators real-time visibility
Counselors spent most of their time on process, not student support
Counselors focus their time on intervention, not paperwork
Common questions
Common questions
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