Report lag
By the time a pivot is ready, the attendance slide has already worsened.
Ask your campus data questions — get actionable answers
Scholar AI Assistant turns SIS, attendance, fees, and academics into conversational queries and risk signals so principals and counselors act before problems escalate.
Ask
plain-language campus questions
Risk
attendance & fee signals surfaced early
Assist
draft tests & intervention lists
The problem
Why leadership still waits on custom reports
The data exists across modules, but answering “who is at risk this week?” still means Excel exports.
Hidden defaulters
Fee risk is invisible until cashflow is already stressed.
Counselor overload
No ranked list of students needing outreach.
Tool hopping
Staff jump between modules to assemble one answer.
Capabilities
What this module actually does
Operational depth your team will use every cycle — not a shallow feature list.
Conversational queries
Ask questions like “Grade 9 with attendance under 80%” and get structured answers.
Attendance risk indices
Surface students trending toward chronic absence.
Fee default signals
Highlight accounts likely to slip based on payment patterns.
Intervention lists
Hand counselors prioritized outreach queues.
Draft assessment help
Assist teachers with outline/test drafting where enabled.
Role-aware answers
Respect SIS permissions — teachers see their classes, not the whole trust.
Explainable cues
Show which signals drove a risk flag.
Deep links
Jump into Attendance, Fees, or SIS records from an answer.
Operating loop
How it works end to end
A clear path your staff and parents can follow without training manuals.
-
01
Connect
AI reads live signals from academic and finance modules.
-
02
Ask
Leaders query in natural language or open risk boards.
-
03
Review
Inspect explainable flags and supporting evidence.
-
04
Act
Assign follow-ups, alerts, or counselor tasks.
Who uses it
Role-aware access for every stakeholder
The right people see the right slice of the pipeline.
Principal / Trust
Asks cross-campus questions within permission scope.
Counselor
Works prioritized student intervention lists.
Teacher
Queries own sections for academic risk.
Fee office
Uses default signals to target reminders.
Connected stack
Works with the modules you already need
Each module is an edge in the campus operating graph — not a silo.
Outcomes
What changes after go-live
Measurable ops wins institutions report once this module runs inside ScholarERP.
Earlier
warning on attendance and fee risk
Faster
answers without waiting on custom reports
Focused
counselor time on the right students
“We stopped exporting five CSVs to find at-risk Grade 8 students — one question gave the list with reasons.”
FAQ
Questions teams ask before buying
Straight answers for setup, integrations, and day-two operations.
Does ScholarAI train on our private student data externally?
Deployments are designed to keep institutional data within your governed environment; ask DIDC for your tenancy’s AI data-handling policy.
Can teachers see other teachers’ classes?
No. Answers respect role-based access from Staff and SIS permissions.
How is this different from Predictive Analytics?
Scholar AI is the conversational/action layer. Predictive Analytics focuses on model forecasts and curves; KPI Builder is the dashboard canvas.
Can AI send parent messages automatically?
It can recommend outreach; sending still goes through Multi-Channel Alerts with your approval rules.
Ask ScholarAI a sample campus question
Demo attendance risk, fee signals, and explainable answers on sample data.