Reducing competency generation time from (N items *N output) days manual labour to N days for supporting learning recommendation system

BINUS University initiate a personalized learning recommendation system that match students with courses based on their career goals.

This system aims to:

Connect job opportunities with relevant courses.

Guide students toward career-aligned education paths.

Provide data-driven course recommendations.

The competency generation process was too painful. e.g :

Each BCL&D need one full working days to complete 10 competency of each course/job position that consist level, definition and details.

The ability to update the competency based on the current need is limited.

Areas on improvement on competency generation :

Leverage current AI trends to generate competency rapidly.

Unlock new insight from existing data.

Implemented an asynchronous system to prevent users from being idle during the competency generation process

Allowed BCL&D to leave the platform and be notified upon completion

Designed four distinct states (Completed, In Progress, Without Competency, Failed) to help users efficiently manage actions

Allow verification and correction by stakeholder for the result, because the AI have room hallucination

Implemented a change history feature to track edits, promote transparency, and facilitate effective collaboration

The final result from these generation process is to allow the student getting recommendation for their courses. The result need to embed to the current portal and need to be measured to find the AI is really works or not.

Ensured the recommendation system aligned with the BINUS design system and portal

Conducted user surveys to measure the 80% recommendation criteria match

Collected feedback through unobtrusive modals to drive continuous system improvements

Incorporated design updates during the project to align with the evolving BINUS design standards

Reduced manual workload from *(N items N output) days manual labour to N days for supporting learning recommendation system.

Built stakeholder trust and positioned AI as tools to accelerate workflow and reduce manual effort.

Created a single source of truth.