Reach struggling students while there is still time
For student affairs deanships, advising units and department chairs who learn a student was struggling only when final grades arrive. We bring attendance, marks and missed work into one advisor view, flag who needs a conversation and draft the follow-up, while advisors decide every step.
- Timeline
- 3 to 4 weeks to first version, including the privacy review
- Best for
- Advising units and colleges supporting first-year students and students on probation
- Usually starts with
- A weekly advisor list built from attendance and missed LMS work
Agent flow
What is AI student academic follow-up?
It is a system that reads signals your university already collects, such as class attendance, marks on early assessments and assignments not submitted in the LMS, and flags students who match criteria your institution sets. For each flagged student, the advisor sees a short summary of what changed and a draft note or meeting invitation, then decides what happens next. The AI never issues academic warnings, contacts parents or changes a student's record on its own.
- Reads attendance, marks and submissions from Banner, PeopleSoft, Moodle, Blackboard or Canvas
- Flag criteria are set and reviewed by your academic advising committee
- Advisors approve every message before it reaches a student
- Each advisor sees only their own advisees, and every view is logged
Typical Student academic follow-up we ship
Each one scoped and quoted at a fixed price after a free review of your process.
Early attendance alerts
When a student misses more sessions than your college allows before a check-in, such as three in the first month, the case reaches the advisor's list with courses and dates, well before the course absence limit.
Low marks on early assessments
First quiz, midterm and lab report results are compared with each department's threshold. Students below it are flagged with any other signals, so one weak quiz alone does not open a case unless your rules say so.
Missed coursework across the LMS
Missing submissions in Moodle, Blackboard, Canvas or D2L Brightspace are gathered across all of a student's courses, so an advisor sees a pattern in four subjects that no single instructor would notice.
Advisor case summaries
Before a meeting, the advisor gets one page: current courses, attendance, recent marks, missed work, earlier advising notes and support already offered. It opens a conversation and makes no judgment about the student.
Drafted check-in messages
The system drafts a supportive check-in or meeting invitation in English or Arabic, in the tone your team sets. The advisor edits, approves and sends it, or decides a phone call, or no contact yet, is better.
Intervention records and reports
Meetings, referrals and outcomes that advisors record are kept in one place, so the deanship sees how many students were reached, which services were used and how flagged students finished the semester.
What changes for your team
Advisors find out a student was failing when final grades are posted.
Advisors see a short list of students showing early signs each week.
Attendance, marks and missed work sit in three different systems.
Each flagged student has one summary with all three signals and past notes.
Follow-up depends on which advisor remembers to check.
Every case has an owner, a status and a record of what the advisor decided.
Four steps, one fixed price
- 01
Agree rules and safeguards
With your advising committee and data protection officer we choose the signals, flag thresholds, who sees what and how long data is kept.
- 02
Connect the sources
We connect read-only to your student information system and LMS, map courses to advisors and test on past semesters with identities masked.
- 03
Build the advisor view
Advisors get a weekly list, case summaries and draft messages in Teams or SharePoint, with approval required before anything is sent.
- 04
Review, train and hand over
We train advisors, compare flags with their judgment for a few weeks, tune the rules and hand over documentation and admin access.
Why early academic follow-up is hard to do by hand
Most universities already hold the information that shows a student is slipping. Attendance sits in the student information system, quiz and midterm marks in the gradebook, and missed assignments in the LMS. Each piece lives in a different system and is seen by a different person. One instructor notices an absence in one course. Nobody sees that the same student has stopped attending three courses and missed every submission for two weeks, until the final grades make it obvious.
An academic follow-up system joins those pieces for the people responsible for advising. Each week it applies criteria your institution has agreed, such as absences above a set number, a mark below the pass line on an early assessment or several missed submissions, and produces a short list for each advisor. The value is timing. A conversation in week four, when a student can still catch up or discuss a lighter load, is very different from one after the final exam.
Advisors decide, the AI flags and drafts
This service is built around one rule: people make every decision about a student. The system flags, summarizes and drafts. It does not mark students as at risk in their record, issue academic warnings, place anyone on probation, contact parents or guardians or share anything with a sponsor. Those actions follow your regulations and are taken by authorized staff. If a draft has the wrong tone or a flag is simply wrong, the advisor dismisses it, and the rules are adjusted.
We are just as careful about what a flag is based on. Criteria use academic signals a student would recognize, such as attendance, marks and submissions, written in plain language your committee approves. We avoid opaque risk scores and never use nationality, gender, disability or family background to decide who is flagged. Advisors can see why each student is on the list, so they can explain it to the student or to any reviewer who asks.
Student data under FERPA, GDPR, PDPL and the DPDP Act
Academic records are among the most sensitive data an institution holds, so privacy design comes before the build. Under FERPA, US institutions give access to school officials with a legitimate educational interest, which here means advisors see only their own advisees. In the UK and EU, GDPR requires a lawful basis, a clear purpose and transparency with students, and we support your data protection impact assessment. Saudi Arabia's Personal Data Protection Law, the UAE PDPL and India's DPDP Act set similar expectations on purpose, access and retention.
In practice, data stays in your own Microsoft 365 or Google Workspace tenant or a cloud region you choose. Connections to the student information system are read-only, access follows advisor assignments, and every view and action is logged. When an AI model drafts a summary, it receives only the fields it needs, with student IDs in place of names where possible, through services that do not train on your data. Nothing goes live until your data protection officer signs off.
AI Student Academic Follow-Up questions
Does the AI decide which students get an academic warning?
No. Academic warnings, probation, course load changes and any notice to a student or parent follow your regulations and are decided by authorized staff. The system flags students who match criteria your committee approved and drafts a message the advisor can edit or discard. Nothing reaches a student, parent, guardian or sponsor without a named person approving it.
How is student data protected?
Data stays in your Microsoft 365 or Google Workspace tenant or a region you choose. Advisors see only their own students, every view is logged, and records are kept only as long as your retention policy allows. We design to FERPA, GDPR, the Saudi and UAE PDPL and India's DPDP Act as they apply to you, and your data protection officer approves the setup before launch.
Which signals can the system use?
Usually attendance, quiz, midterm and assignment marks, missed LMS submissions and long gaps without logging in to the LMS. Some institutions add advisor notes or tutoring center visits. Your advising committee chooses the signals and thresholds, and personal characteristics such as nationality, gender or disability are never used to decide who is flagged.
Will students know they can be flagged?
That is your institution's decision, and we recommend being open about it. Many advising teams tell students at the start of the semester that attendance and coursework are reviewed so help can come early, and the privacy notice says the same. A flag is an internal prompt for an advisor, not a status on the student's record, and instructors and classmates cannot see it.
What does it cost, and who owns the system?
After a free 30-minute audit of your advising process, we send a fixed-price quote in writing before any work starts. A first version, usually the weekly advisor list and case summaries, typically takes 3 to 4 weeks including the privacy review. Your institution owns every workflow, rule, prompt and report, with documentation and training at handover.
Often built together
AI Exam Preparation CoordinatorRosters, rooms, papers and special arrangements ready and checked before the first exam begins.
AI Unit Performance DashboardOne live view of unit progress, overdue activities and KPIs, ready before the dean asks for it.
AI Faculty Compliance and Follow-UpSee which course files are in and which are missing, without chasing every faculty member yourself.
Get a fixed quote for your Student academic follow-up
Send two lines about the process you want to hand over. We reply within one business day with what we would build, the tools involved and what it would take.
- 30-minute call, no slides, no pressure
- One fixed price before any work starts
- You own every workflow, account and line of code