Edith Cowan University is building AI into its admissions process

You have probably sat in a room where someone argued that admissions is a checklist problem. Course entry requirements exist. Applicant records exist. Match them, send the offer, done. Edith Cowan University's director of student administration Peter Corbett and associate director of admissions Annette Thompson know that story does not hold. No two applicants take the same path to university. Some arrive with credit from prior study. Others upload documents that require interpretation. The decision is time-sensitive, revenue-critical and scrutinised by every stakeholder who cares about enrolment. Thompson told Databricks' Data+AI World Tour event in Sydney that turnaround time is king in admissions. Everyone expects a decision yesterday, and competitive advantage evaporates when service levels slip. The university is now building an AI system called AI Advantage, using Databricks technology, to pull together the evidence needed to assess eligibility and produce an explainable recommendation.

What the system does today

Corbett described the system's inputs. It ingests course entry requirements published on the university's website and future students site. It pulls structured application information from the student management system. It reads documents uploaded by applicants. It retrieves existing records from the university's database. The AI mashes those sources together in the background and makes a recommendation.

The recommendation must be explainable. The system shows how the evidence it collected relates to the course requirements for what the prospective student applied for. Uncertain or complex applications are directed to an experienced assessor. Thompson said the model delivered recommendations, goals and targets, plus a holistic view of an applicant and the admissions cycle, while preserving the capacity to use a human-in-the-loop to review the decision, use the recommendation in a meaningful way and finalise an outcome in lightning-quick time.

Automation goals

The university is pursuing the work through a student experience improvement lens. Thompson said students are at the heart of what the institution does, and student experience is high on the list of importance for Edith Cowan. The team now sees opportunities to build on AI Advantage's capabilities.

Corbett said the university wanted to see whether it needed a human-in-the-loop initially, and is now reasonably confident it does not need one every time. The stated goal is for the system to make automatic offers to applicants without human involvement, raising applicant satisfaction. Thompson clarified that some decisions will still be referred to human assessors. The intent is not to completely replace human judgement in the admissions process.

Beyond automated decisions, the university hopes the AI system will predict eligibility for courses and scholarships, though no timeline has been set for that capability.

What this means for L&D

Edith Cowan's approach offers a template for organisations building AI into high-stakes, time-sensitive processes. The university started with explainability and human review, then moved toward automation only after validating the model's recommendations. That sequence matters. Admissions is revenue-critical and closely scrutinised. Jumping straight to automated decisions without proving the system's logic would have invited risk and resistance.

L&D and innovation leaders can apply the same discipline when deploying AI in talent acquisition, performance review or skills assessment. Build the system to surface evidence and explain its reasoning first. Let experienced practitioners validate the recommendations. Only then remove the human-in-the-loop for straightforward cases, while preserving escalation paths for complexity. The goal is not to eliminate judgement. It is to free skilled people from repetitive evidence-gathering so they can focus on the decisions that require their expertise.

Sources:

• IT News, 20 October 2026