Case Study
Online education provider automates transfer-credit evaluation to 80% touchless
Results
80%
Faster admissions, 80% cleared without manual review
75K
Handles 75,000 transcripts a year automatically
14FTEs
On track to free up the work of 14 evaluators
Company Profile
An online career-focused education provider offering accredited high school, college, and skilled-trades programs to working adult learners at national scale. Its model depends on fast, accurate admissions and transfer-credit decisions, since every step a student clears without friction shapes both completion and revenue.
Challenge
Manual transcript review standing between application and class
Evaluators reviewed transfer credit one transcript at a time, and that manual review stood between a student’s application and their first day of class. Each transcript meant screening course eligibility, matching courses to programs, and applying credit rules that differed for high school and college transfers. Course names varied by institution, so the work resisted simple rules and leaned on evaluator judgment.
Nothing was reusable, so the same institution’s courses were evaluated from scratch every time. As application volume rose, review slowed enrollment and strained a team that could not scale one transcript at a time.
Solution
Route the routine, escalate only the ambiguous
Our client engaged WonderBotz, which designed and delivered an end-to-end evaluation solution from intake through credit posting to the student system, leveraging proprietary frameworks and accelerators. The solution automatically read course data from high school and college transcripts, then automated the routine majority of course matching, escalating only ambiguous courses to an Anthropic Claude reasoning step and only low-confidence results to a human evaluator. It encoded the client’s eligibility and credit-awarding policy for both transcript types, so decisions stayed consistent and auditable.
A feedback loop captured evaluator corrections and applied human-assisted reinforcement learning, so accuracy improved for each institution as volume grew. Routine work cleared automatically, evaluators focused on genuine judgment calls, and students moved toward enrollment without waiting on a manual queue.
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