Transforming UNESCO's AI Competency Framework into Machine-Readable Data
Today, Monday Sept. 2nd 2024, marks a significant milestone in the intersection of education and technology as UNESCO officially launches its groundbreaking AI Competency Framework for students and teachers. Designed to equip educators and learners with the essential skills and knowledge to navigate an AI-driven world, this framework is poised to have a transformative impact on global education.
However, its true potential lies in its ability to be converted into machine-readable linked open data, setting a new standard for educational frameworks worldwide.
The Need for Machine-Readable Data in Education
In the digital age, one of the most pressing challenges in education is the interoperability and mobility of recognized skills across different contexts and platforms. Static documents, such as PDF-based frameworks, while useful, are limited in their ability to be seamlessly integrated into modern educational technologies.
By converting UNESCO’s AI Competency Framework into machine-readable linked open data, we can unlock faster adoption, cross-platform integration, and global utility.
Converting the Framework: A Technological Leap
The process of converting the AI Competency Framework from a static document into a dynamic, machine-readable format involves several key steps. First, the competencies and learning outcomes outlined in the framework are dissected and leveled using AI tools like OpenSkillsGPT. This tool, part of the Open Skills Network, automates the conversion process, turning the PDF into a format compatible with various open standards such as Rich Skills Descriptors (RSDs), IEEE Shareable Competency Definitions (SCDs), and Credential Transparency Description Language (CTDL).
This conversion not only makes the framework accessible in a machine-readable format but also enables it to be linked with other educational data. This structured data can then be integrated into a variety of digital education platforms, significantly enhancing their ability to deliver personalized and relevant learning experiences.
A critical aspect of this initiative is ensuring that the AI Competency Framework is interoperable with existing global skills frameworks. By "crosswalking" UNESCO’s framework with well-established systems like the European Skills, Competences, Qualifications, and Occupations (ESCO) and the United States’ Occupational Information Network (O*NET), we ensure that the competencies recognized in one context are transferable and recognized in others. This interoperability is essential for fostering a global education system where skills and qualifications can be easily shared and recognized across borders.
Integration and Implementation in Digital Platforms
Once converted into machine-readable linked open data, the AI Competency Framework can be seamlessly integrated into a wide range of digital education platforms via APIs. Learning Management Systems (LMS), Credentialing Systems, and other digital learning platforms can more easily integrate competencies outlined in the framework, enabling greater alignment, more efficient skills-matching and wider shared understanding based on the framework.
This integration is not just about convenience; the frameworks adoption in a digital-native language first - structured, machine-readbale data - represents a potential acceleration in its adoption and positive impact of AI in education.
The Flywheel Effect: AI Enabling AI
One of the most exciting aspects of this conversion process is the potential for a flywheel effect, where AI tools are used to make AI competency frameworks machine-readable, which in turn enables platforms to better leverage AI in education. This creates a self-sustaining cycle of improvement and innovation, driving the continuous enhancement of educational tools and resources.
A Blueprint for the Future
The successful conversion of UNESCO’s AI Competency Framework into machine-readable linked open data provides a transformative blueprint for the future of educational frameworks. The methodologies and tools used in this process can be applied to any other framework, setting a new standard for how educational competencies are developed, shared, and utilized in the digital age.
This approach not only maximizes the impact of UNESCO’s great work but also serves as a virtuous model for a global digital learning ecosystem. By embracing the power of AI and open data, we can catalyze a global revolution in skills-based learning, ensuring that education keeps pace with the rapidly evolving demands of the modern world.
Conclusion
As UNESCO’s AI Competency Framework is officially launched today, we stand at the forefront of a new era in education. The conversion of this framework into machine-readable linked open data is not just a technical achievement; it is a significant step towards a more interconnected, interoperable, and dynamic global education system. This initiative underscores the potential of AI in supporting the future of learning and provides a clear path forward for integrating technology and education - a flywheel of opportunities.
Thank you Simone! One step closer to skills serendipity for all
May be of interest Carolyn Wilcockson?
Congratulations to the team who made the incredible progress in developing AI Competency Framework! Making the framework available under an open license and as as linked, open data structures will enable clear connections between it, other frameworks, rubrics, curriculum, courses, credentials, jobs and more. These are essential attributes to accomplish a broad range of goals.
we would not be taking about machine-readability if it wasn't for Kelly Shiohira Natalie Lao Shafika Isaacs (PhD) and team for developing the framework in the first place !
Jamie Alexandre Matthew Gee Margo Griffith Chris Purifoy Duncan Cox Taylor Kendal Don Presant Doug Belshaw Koen Nomden Celine Jambon Ildiko Mazar Phillip Long Taylor Hansen Nate Otto Kerri Lemoie, PhD Sean Murphy Jonathan Furr Julie Keane, PhD Dmitri Zagidulin 🇺🇦 Anthony Fisher Camilleri James Keevy Colin Reynolds Katie Sievers Dawn Karber Ryan Lufkin Jason Weaver Takis Diakoumis Melissa Loble Dóra Ida Jekkel