- Complexity: Advanced
- Deployment: HeyQuinn recommended
The problem
College advisors carry caseloads too large to review every student's engagement signals by hand. Many students who leave are not failing academically, they disengage for operational reasons that go unnoticed until it is too late. Advisors need a way to see early warning signs across systems that do not talk to each other.
- Advisor caseloads make manual review of every student impossible
- Warning signs live scattered across the learning management system and the student information system
- Students with strong grades still drop out for reasons advisors never see
- Drafting personalized outreach and success plans for every flagged student takes time advisors do not have
The solution
A workflow connects to the learning management system and student information system to flag at-risk students, then drafts personalized outreach and a success plan for each one, while an advisor decides what goes out.
- Reads engagement and performance data from Canvas, Banner and PeopleSoft
- Flags students showing risk signals against the institution's criteria
- Drafts a personalized outreach message and a success plan for each flagged student
- Presents the draft and supporting data to the advisor for review
- Sends the outreach only after the advisor approves it
Where people stay in control
- An advisor reviews and sends every outreach draft before a student receives it
- Data handling follows student privacy requirements and tenant isolation
- Advisors set the risk criteria the workflow flags against
Systems and data
The workflow reads engagement and academic records from the learning management system and student information systems, and writes outreach drafts and success plans back into those systems for advisor review.
- Canvas
- Banner
- PeopleSoft
Results
- A research university reported the workflow adding advisor capacity equivalent to several times current staffing in the documented deployment
- One deployment flagged several hundred at-risk students and generated several hundred outreach drafts in the documented period
Why HeyQuinn is recommended
The workflow touches sensitive student records across several institutional systems and requires audit and tenant isolation, which argues for HeyQuinn recommended deployment with governance and monitoring in place.
Frequently asked questions
Does AI contact students directly?
No. It drafts outreach and success plans, and an advisor reviews and sends the message.
What data does the workflow use to flag risk?
It reads engagement and performance data from the learning management system and student information system, not just grades.
Is student data kept private?
Yes. Deployments are built around student privacy requirements and tenant isolation across institutions.