The Koru ProjectIllustrative example. Not client data.
An example output from our starter engagement: one workflow map, three ranked AI opportunities, control checks and a proposed pilot. The institution and records are hypothetical.
Workflow map
This example uses PeopleSoft Campus Solutions. The actual sequence depends on the aid program, award year and campus procedures. A correction or exception can send a case back to an earlier step.
Ranked opportunities
The ranking weighs expected staff time saved against the risk of a wrong output. These are relative planning estimates, not measured results. Validate the workload and controls before choosing a pilot.
| Rank and opportunity | Expected time saved | Risk | Control and human review |
|---|---|---|---|
| 01. Draft an ISIR exception brief | Highest expected: reduce repeated lookup and summarizing in the daily review queue. | Moderate: a missing flag or wrong summary could send a case to the wrong queue. | Summarize approved exception codes and procedures. Staff compare the draft with the system record; the assistant cannot update records or clear flags. |
| 02. Summarize verification packet completeness | Medium expected: reduce time spent organizing documents and drafting missing-item lists. | Moderate: a summary could omit conflicting information or expose student data. | Use synthetic packets during evaluation. Staff review the original documents and decide whether verification or corrections are complete. No automatic student messages. |
| 03. Group COD rejection messages | Lower expected: reduce repetitive sorting of reporting errors by code and owner. | Lower for this limited task: incorrect grouping may delay a review, but it cannot change an award or reporting record. | Use synthetic error logs first. Show the original error beside each group, and require staff to approve routing. No automated corrections or resubmissions. |
Risk and regulatory checks
The Financial Aid owner, privacy office and information security team review these checks against current rules and institutional policy. This example is a planning aid, not a compliance determination.
Identify the education records involved, who needs access and the permitted basis for disclosure. If relying on the school-official exception, review institutional control, legitimate educational interest, permitted use and limits on redisclosure. Use synthetic records for this pilot.
Department of Education FERPA guidanceReview the vendor and contract, data retention, encryption, access controls, logging and incident procedures against the institution’s information security program. Assign an owner to review the provider’s safeguards over time.
Federal Student Aid service-provider guidanceLink each decision to the applicable award year, current guidance and campus procedure. Mark the reviews for verification, eligibility, SAP, packaging, disbursement and withdrawal/R2T4 exceptions. The assistant cannot determine eligibility, approve an appeal, calculate an award or release funds.
Federal Student Aid HandbookReview FAFSA data-use limits and federal tax information (FTI) restrictions separately. Removing a name does not establish that a record may be shared with an AI provider. This pilot uses synthetic data and excludes FTI; any later use of live data needs a separate review and approval.
Federal Student Aid FTI guidanceProposed pilot
After the control review, run a two-week staff exercise in an institution-approved tool. Use synthetic exception cases and approved procedures, with no production-system access, student messages or changes to records.
Proposed target: reduce median staff triage time per exception by 25%, including time spent checking and correcting the AI draft. This is an illustrative target, not a promised or achieved result.
The deliverable is the workflow map, ranked opportunities, control checklist and pilot plan. An implementation decision follows the review of those materials.
Back to Higher EdYour first Koru project
Tell us about one workflow, including the messy parts. We will tell you honestly where AI fits, where it doesn’t, and what a first pilot would look like.
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