AI Use Case - Automated Application Scoring
AI Application Scoring
Table of Contents
Overview
The automated application scoring feature that analyzes student's submission (e.g., transcripts, English level, and working experience) to rank their application according to how closely they fulfil the admission criteria. This feature aims to reduce the university admission team's review time, and allow them to focus on high-touch recruiment.
Use Case
Most institutions have limited Admission resources to manage incoming applicants. Especially during every term, there is a new wave of students sending in hundreds and thousands of application. This can create an issue for the team as they have limited man power as the team has to calculate the application score manually which takes up a lot of time and man power.
The AI Application Score Calculation helps to solve this issue by calculating the application score automatically
The Problem
A faculty member is trying to sort out the top 300 students out of 2000 applications, but then he realise that he have to calculate the application score before he can sort out the score with order. Even with his max team of 20 people it will still take few days to get it all calculated.
Sometimes even after calculation, the results undergo double check and still go wrong. This not only waste time, but it also wastes a lot of energy.
To put it in a word, inefficient. Instead of spending time to calculate and look at each individual application, the Admission team should focus on those applications with higher chances of success.
The solution
In this case, AI is introduced to deal with the issue. AI will be able to calculate the result up to an accuracy of 99.99% in a matter of seconds with the click of a button.
The process is easy, simple, time saving and cost saving as man power can be used elsewhere compared to calculating score.
For example, applicants can be evaluated based on the weightage defined in these 4 criterias, AI will then follow the weightage to calculate the formula/logic given. It can also be customized to fit different fields and data for different requirements.
| Previous CGPA | 30% |
| English Level | 50% |
| Local/International Student | 10% |
| Working Experience | 10% |
How and What Data will be sent to AI
Data will be sent to AI via a data query.
Data → AI → Return Response
In this Case, These 4 fields will be sent to AI via a query.
After, User can click the AI Operation button at the top of the Individual Program Application Record.
Results
The result will be a summary of the applicant's total application score in a few seconds. Along with the formulas, how it is calculated, and a short sentence on the student's performance.
Accuracy
Accuracy: 99.99%
Model Used: OpenAI gpt-4.1
Test data can be found here.
Estimated Cost - Per Request
For the latest pricing, please refer to GPT-4.1 API pricing (official)
Token calculation is based on OpenAI Tokenizer.
Request (Input)
Price (as of Jan 2026): $2.00 per 1,000,000 tokens
Calculated based on 2783 characters. 771 tokens
Input Cost = 771 x $2.00 / 1,000,000 = $0.001542
Response (Output)
Price (as of Jan 2026): $8.00 per 1,000,000 tokens
Calculated based on 484 characters, 178 tokens
Output Cost = 178 x $8.00 / 1,000,000 = $0.001424
Total Cost: <$0.003