Periodic Reporting for period 1 - TER-AI (Tertiary Education Redefined: AI-Driven Upskilling & Reskilling Platform for Industry-Relevant Digital Skills)
Período documentado: 2024-08-01 hasta 2025-07-31
The global economy faces an unprecedented skills shortage. While 92 million jobs will be displaced by technological changes by 2030, 170 million new positions will emerge globally, resulting in a net increase of 78 million jobs. However, 63% of employers cite skills gaps as their primary barrier to business transformation. This challenge has been accelerated by rapid digitalization across industries and the growing demand for technology professionals. Traditional higher education systems struggle to keep pace with evolving industry requirements, creating a critical gap between available talent and market needs.
The TER-AI project addresses this crisis through Turing College's pioneering AI-driven online tech education platform, directly aligned with the EU’s Digital Decade objectives and European Skills Agenda. Our approach leverages the rapidly expanding AI in education market - growing from €5.99 billion in 2025 to €95.46 billion by 2034 - to deliver personalized, adaptive learning experiences bridging traditional higher education and industry needs.
The project's strategic objectives include: (1) building an advanced AI-driven adaptive learning platform supporting personalized educational pathways; (2) ensuring high standards of transparency, data privacy, and ethical AI deployment; and (3) establishing a scalable infrastructure capable of serving millions of learners globally.
The expected impact includes bridging the tech talent shortage, democratizing access to high-quality education, and fostering economic growth through enhanced digital skills development.
The TER-AI project addresses this crisis through Turing College's pioneering AI-driven online tech education platform, directly aligned with the EU’s Digital Decade objectives and European Skills Agenda. Our approach leverages the rapidly expanding AI in education market - growing from €5.99 billion in 2025 to €95.46 billion by 2034 - to deliver personalized, adaptive learning experiences bridging traditional higher education and industry needs.
The project's strategic objectives include: (1) building an advanced AI-driven adaptive learning platform supporting personalized educational pathways; (2) ensuring high standards of transparency, data privacy, and ethical AI deployment; and (3) establishing a scalable infrastructure capable of serving millions of learners globally.
The expected impact includes bridging the tech talent shortage, democratizing access to high-quality education, and fostering economic growth through enhanced digital skills development.
During the first reporting period, the TER-AI project has made significant progress in developing an innovative AI-driven adaptive learning platform designed to revolutionize education and address the global skills gap in technology sectors.
The project first delivered a wireframe-level adaptive learning system prototype illustrating the main components: competence prediction, learning path adjustment, tutor ranking, & student-company matching. User validation through structured interviews with learners and experts confirmed system alignment with educational needs and market demands, providing clear direction for technical implementation.
A robust hybrid data architecture was implemented combining relational and vector databases to support real-time adaptive learning features. The platform integrates advanced event-driven data collection mechanisms ensuring GDPR compliance while enabling comprehensive learning analytics and personalized educational experiences.
A breakthrough competence prediction system was developed using Large Language Models and advanced prompt engineering techniques. The system automatically assigns hierarchical competencies to multiple different learning events such as unstructured review feedback, review transcripts, quiz questions, then evaluates student proficiency with confidence scoring, and finally creates a dynamic knowledge mapping for each learner, identifying gaps, strengths and suggestions for learning progression.
Comprehensive ethical standards were established through appointment of an External Independent Ethics Advisor, ensuring all AI-driven educational tools meet European standards for responsible innovation with data protection and algorithmic transparency protocols.
The achieved infrastructure and AI modeling capabilities provide the foundation for continued development of adaptive learning path optimization, tutor evaluation systems, and industry-aligned talent matching mechanisms.
The project first delivered a wireframe-level adaptive learning system prototype illustrating the main components: competence prediction, learning path adjustment, tutor ranking, & student-company matching. User validation through structured interviews with learners and experts confirmed system alignment with educational needs and market demands, providing clear direction for technical implementation.
A robust hybrid data architecture was implemented combining relational and vector databases to support real-time adaptive learning features. The platform integrates advanced event-driven data collection mechanisms ensuring GDPR compliance while enabling comprehensive learning analytics and personalized educational experiences.
A breakthrough competence prediction system was developed using Large Language Models and advanced prompt engineering techniques. The system automatically assigns hierarchical competencies to multiple different learning events such as unstructured review feedback, review transcripts, quiz questions, then evaluates student proficiency with confidence scoring, and finally creates a dynamic knowledge mapping for each learner, identifying gaps, strengths and suggestions for learning progression.
Comprehensive ethical standards were established through appointment of an External Independent Ethics Advisor, ensuring all AI-driven educational tools meet European standards for responsible innovation with data protection and algorithmic transparency protocols.
The achieved infrastructure and AI modeling capabilities provide the foundation for continued development of adaptive learning path optimization, tutor evaluation systems, and industry-aligned talent matching mechanisms.
The TER-AI project has achieved significant breakthroughs in competency prediction through the development of an innovative AI-driven system that goes well beyond traditional assessment methods. The system successfully implemented a comprehensive hierarchical competency framework that automatically maps learning activities to structures hierarchy: category, skill and sub-skill, demonstrating superior performance compared to conventional human-based evaluation approaches.
The breakthrough lies in the system’s ability to process educational content at scale using large language models, achieving evaluation quality that matches or exceeds human expert assessments while operating at dramatically improved speed, efficiency and cost. Unlike traditional competency systems that rely on static skill inventories or manual classification, our AI-powered approach dynamically generates and maintains a comprehensive skill taxonomy that evolves with educational content and learning patterns, taking into account the learning events of all users within the system.
The system represents a significant advance over existing educational technology solutions by providing real-time, explainable competency tracking with recency mechanisms. This enables personalized learning pathways that adapt to individual student progress and knowledge gaps, moving beyond the one-size-fits-all approach of traditional educational platforms to deliver truly individualized learning experiences at scale.
The scalable architecture positions the platform to serve millions of learners while maintaining personalized experiences, representing a paradigm shift in online education delivery.
To scale and sustain the solution, further research into prompt robustness and evaluation metrics is needed, along with clearer processes for human-in-the loop quality control mechanisms for dynamic competency taxonomies. Demonstrating real-world impact through broader pilot studies, creating automated quality- and impact-tracking systems, and establishing a secure review mechanism for over-generation are critical. For commercialisation, alignment with educational standards, explainability requirements, and data privacy regulations (e.g. GDPR) must be maintained.
The breakthrough lies in the system’s ability to process educational content at scale using large language models, achieving evaluation quality that matches or exceeds human expert assessments while operating at dramatically improved speed, efficiency and cost. Unlike traditional competency systems that rely on static skill inventories or manual classification, our AI-powered approach dynamically generates and maintains a comprehensive skill taxonomy that evolves with educational content and learning patterns, taking into account the learning events of all users within the system.
The system represents a significant advance over existing educational technology solutions by providing real-time, explainable competency tracking with recency mechanisms. This enables personalized learning pathways that adapt to individual student progress and knowledge gaps, moving beyond the one-size-fits-all approach of traditional educational platforms to deliver truly individualized learning experiences at scale.
The scalable architecture positions the platform to serve millions of learners while maintaining personalized experiences, representing a paradigm shift in online education delivery.
To scale and sustain the solution, further research into prompt robustness and evaluation metrics is needed, along with clearer processes for human-in-the loop quality control mechanisms for dynamic competency taxonomies. Demonstrating real-world impact through broader pilot studies, creating automated quality- and impact-tracking systems, and establishing a secure review mechanism for over-generation are critical. For commercialisation, alignment with educational standards, explainability requirements, and data privacy regulations (e.g. GDPR) must be maintained.