Coreable
A platform designed to answer the critical question: what skills will matter in the age of AI, and how can they be measured, managed, and improved?
The Question
As AI models become capable of performing an increasing range of cognitive tasks, a fundamental question emerges for education and workforce development: which human skills will remain valuable?
Coreable was born from this challenge. Developed in collaboration with academics, the project aimed to identify, measure, and cultivate the skills that will define human value in an AI-augmented workforce.
Origin Story
The idea emerged from the UTS Startups incubator, bringing together a cross-disciplinary team of technologists, educators, and researchers. The project was recognized with the Hatchable Award, validating its potential to address a real and growing need.
Core Concept
🔍 Skills Identification
Academic research-backed framework identifying the core competencies that machines struggle to replicate—creativity, emotional intelligence, complex problem-solving, and adaptive thinking.
📊 Measurement Tools
Validated assessment instruments to measure these future-proof skills, providing individuals and organizations with actionable insights into their capabilities.
📈 Development Pathways
Personalized recommendations for skill development based on assessment results, helping users focus their growth on areas that will remain relevant as automation advances.
🏢 Organizational Insights
Tools for managers and HR teams to understand team capabilities, identify skill gaps, and make informed decisions about training and development investments.
The Thesis
"In a world where AI can do anything routine, the most valuable human skills are those that are fundamentally about being human."
Rather than competing with AI on speed and accuracy, Coreable's framework focused on skills that complement automation—interpersonal dynamics, ethical reasoning, creative synthesis, and the ability to navigate ambiguity.
Technical Stack
- React: Component-based frontend for interactive assessments and dynamic skill visualizations.
- GraphQL: Flexible API layer enabling efficient data fetching for complex skill relationship queries.
- Bootstrap: Responsive UI framework ensuring accessibility across devices for diverse user groups.
- MySQL: Relational database for structured storage of assessment data, user profiles, and skill taxonomies.
- Google Cloud Platform: Scalable infrastructure for hosting and data processing.
Note: This project was discontinued due to lack of investor interest, despite strong academic validation and the Hatchable Award recognition from UTS Startups.