Project Principles
Project Principles
As a noncommercial, university-based research team, we have the flexibility and opportunity to avoid some of the pitfalls associated with commercial R&D in the ed-tech/HR-tech/skills-tech space.
Our key motivating principles include
Cutting-Edge AI Creates New Opportunities
The LAiSER project has evolved with the rapid changes in AI and large language models over the past few years. While early iterations focused primarily on improving the accuracy of extraction from unstructured data, our development now leans towards agentic applications that can help individuals find the best matches in a sea of data – for instance, the job posting(s) in a large job bank whose skill requirements align best with the skills acquired in one’s degree program. Our team monitors the latest developments in AI – and in how real humans are using AI tools – and adapts our research and application projects to keep up with new developments. For example, we are now exploring opportunities to leverage LAiSER to identify areas of the higher education curriculum in which students may be most at risk of AI-related job displacement, and potential opportunities to revise those courses and credentials to focus on uniquely human skillsets.
Interoperability is Key
Occasionally, one will see references to efforts to create a global skills taxonomy – an approach favored by organizations such as the World Economic Forum. In our research – including our global landscape scan on skills taxonomies – we explore why we all benefit from the rich tapestry of skills taxonomies developed by governments and private organizations around the world, and why synchronization with a global taxonomy would be a “cat herding” exercise with a low probability of success. Therefore, we’ve designed LAiSER as a tool for achieving interoperability across different taxonomies. LAiSER uses several major global taxonomies, including O*Net, ESCO, Open Skills Network and the UK Standard Skills Classification, to identify skills, and gives end users the power to decide which skill taxonomies they want to use in their analysis.
Always Free and Open Source
While there may be computing and LLM token costs associated with implementing LAiSER and LAiSER may one day be integrated into commercial tools per our open-source license (indeed, we would view integration with a major HR information system or student information system a major sign of success), there will never be a charge for accessing our software for skill extraction and linkage and we seek to build free applications that will empower workers, educators, and employers to fully utilize their unstructured skills data. Moreover, we are committed to building a community of open source users and contributors. All are welcome to contribute to the project through our codebase, and we proudly create structured internship opportunities for GW students and recent graduates interested in building the future of workforce and higher education data.