How One of the World's Largest Online Education Platforms Scaled Graduate Program Qualification With AI Voice

Leading EdTech Platform
Kuhnic
How One of the World's Largest Online Education Platforms Scaled Graduate Program Qualification With AI Voice

70%+

High-frequency scenario coverage

95%+

Expected ASR accuracy across English calls

≥10%

Contact-to-qualified rate

≥60

Post-call NPS

Director of Enrollment
Leading EdTech Platform

The AI agent performed exactly how our team would qualify applicants — but faster, more consistent, and always available. After the pilot success, we're now expanding this across more than 40 university programs.

Director of Enrollment

Enrollment Partnerships (Leading EdTech Platform)

IndustryOnline Education
Employee Range1,000+
LocationGlobal

This leading online education platform partners with top universities globally to deliver online degree programs at scale. Selective programs like Master of Computer Science receive high volumes of inquiries from prospects who must meet strict academic and English-proficiency requirements. We built an AI outbound voice agent that pre-qualifies applicants, handles objections, and books enrollment interviews automatically.

After a highly successful pilot with one program, the platform approved a full rollout across more than 40 different programs and university partners, each requiring its own tailored qualification logic and scheduling workflow.

The Challenge

The enrollment team needed a repeatable, scalable way to:

Handle high inquiry volume for selective master's programs.

Enforce rigid academic requirements (GPA thresholds, transcripted CS prerequisites, math foundations, English proficiency scores).

Prevent Enrollment Representatives from spending time on unqualified or low-fit prospects.

Maintain a warm, human-like interaction, rather than an abrupt, transactional screening.

Book interviews while preserving CRM syncing, reminders, and rescheduling flows.

Ensure consistent quality across dozens of different programs, each with its own admissions criteria.

Challenge visualization

The platform set three ambitious KPIs for the pilot:

≥10% contact-to-qualified rate

≥60 NPS after AI-handled calls

≥70% coverage of high-frequency outbound scenarios

The Solution

The platform deployed an AI outbound voice agent that:

Calls prospects who requested program information.

Introduces itself on behalf of the platform and the specific university/program.

Delivers the legally required call recording/monitoring disclosure.

Asks what prompted their interest, gathering motivation and context for the Enrollment Representative.

Runs a structured qualification check:

  • Degree and field
  • GPA thresholds (≥3.2 preferred; <3.0 not admissible)
  • CS prerequisites (Intro to Programming, OOP, Data Structures)
  • Math prerequisites (Discrete Math, Probability & Statistics, Linear Algebra)
  • Transcripted Data Structures & Algorithms coursework
  • English proficiency via TOEFL/IELTS/Duolingo or waiver

Handles a wide spectrum of objections including time constraints, cost concerns, academic insecurity, program fit, online learning skepticism, and more.

If qualified:

  • Schedules an appointment automatically.
  • Sends confirmation and context to Enrollment Representatives.

If not qualified:

  • Explains the reason clearly and empathetically.
  • Provides remediation paths (take grad-level courses, repeat prerequisites).

Sends full structured data + transcript into the CRM for Enrollment Representatives to review.

Supports A/B testing of different intros, tone variations, and qualification flows.

Pilot Outcome

The initial pilot was launched with a Master of Computer Science program.

AI qualification responses matched human expectations and followed the admissions logic precisely.

Call tone and pacing felt natural, not robotic — internal teams commented that it "sounded like a real advisor."

Enrollment teams saw the value of having clean, structured qualification data + transcript visible before every call.

Early internal NPS and agent-sentiment feedback surpassed expectations.

Leadership validated the model as scalable and repeatable across other programs.

Because the pilot was successful, the platform approved a full deployment across more than 40 additional programs and universities, each with:

Custom qualification rules

Unique prerequisites

Program-specific scripts

Independent agent instances

Separate booking groups

This marks one of the largest known AI-qualified enrollment rollouts in the online education sector.

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