Closing the gap between education and employment: TER-AI project complete
6 min · July 31, 2026
Learning fulfills its purpose only when it leads somewhere. Turing College has just completed TER-AI, an EU-co-funded project that built four AI capabilities into its learning platform, all of which are now live.
Key takeaways
TER-AI is a completed research and innovation project co-funded by the European Union under Horizon Europe, through the European Innovation Council Accelerator programme, a €2.5 million grant awarded from nearly 1,000 applicants (Grant Agreement No. 101161973).
The project delivered four AI capabilities: Competence Intelligence, Personalised Learning Material, Evidence-Based Tutor Matching, and Student-Company Matching.
The core idea: connect what a learner can do with what employers are hiring for, through real-time, competency-based matching.
Turing College will keep building on these foundations after the EU-co-funded project period ends.
What is TER-AI?
TER-AI (Tertiary Education Redefined: AI-Driven Upskilling & Reskilling Platform for Industry-Relevant Digital Skills) is a research and innovation project run by Turing College and funded by the European Union under the Horizon Europe framework, through the European Innovation Council Accelerator programme (Grant Agreement No. 101161973). Over the course of the project, Turing College designed, built, and deployed a suite of AI-powered capabilities integrated directly into its adaptive learning platform. The goal: make education continuously relevant to evolving industry needs, closing the gap between what education delivers and what the labour market requires, one of the defining challenges of modern workforce development.
From promise to platform
In November 2024, when Turing College announced the €2.5 million EIC Accelerator grant, selected among roughly 70 companies out of nearly 1,000 applicants, we set out what an AI-powered learning platform could look like after eight years of exploring AI in education. That announcement described four things we planned to build. Here's how they turned out.
What we said we'd build (Nov 2024) | Developed (Jul 2026) |
|---|---|
A system that identifies and fills skill gaps in real time | Competence Intelligence |
AI tools that adapt to each learner, personalising learning paths in real time | Personalised Learning Material |
AI that enhances mentorship by flagging the right moments for a discussion | AI that enhances mentorship and peer connections, without replacing the human relationship |
An AI-driven system to match graduates with job opportunities that fit their skills | Student-Company Matching |
“What genuinely surprised and delighted me is how little we had to deviate from our original application. The handful of adjustments we made came from staying open to better ways of solving the problem as the technology evolved, and every one of them made the outcome stronger. It says a lot about how our team thinks, not just about what we're building today, but about building it in a way that can adapt to what's coming next.”
Rūta Gumbelevičiūtė, Head of Business Operations
What we built
The project delivered four interconnected AI capabilities.
Capability | What it does |
|---|---|
Competence Intelligence | Tracks each learner's skill profile from learning activity, quiz performance, and project work, creating a continuously updated, evidence-based picture of what they know and can do |
Personalised Learning Material | Generates AI project briefs and content adaptations tailored to each learner's professional role, industry context, and current skill gaps |
Evidence-Based Tutor Matching | Matches learners with peers and mentors based on demonstrated subject expertise and feedback quality, not seniority or availability |
Student-Company Matching | Connects learners and employers through real-time, competency-based job matching, reducing guesswork in both hiring and job searching *Live in production. Rolling out to a growing share of learners as we scale access |
“The hardest part wasn't building the AI. It was making sure it understood skills the way educators and employers do. The same competency often gets described in completely different language across courses, projects, and job descriptions. Solving that meant learners get evaluated more fairly, and understand why the platform recommends a particular tutor, activity, or job.
Of the four capabilities, I think Competence Intelligence will matter most over time. Most learning platforms can tell you what content someone completed. They struggle to understand what that person actually learned, and as AI becomes more embedded in education, that distinction will only matter more.”
Dominika Szulc, AI Engineer
Why it matters
The ambition behind TER-AI reaches beyond new platform features. It's a different approach to online education. When a learning platform understands what a learner can do, adapts what they learn next, connects them with the right support, and ultimately links them with employers seeking those skills, education becomes more than a credential: a pathway to employment, for our learners and as a model other providers can build on.
TER-AI shows that this vision is technically achievable at scale, and offers a practical model for the future of online tertiary education across Europe.
“People tend to assume AI in education means a chatbot. Here, it operates largely behind the scenes instead of holding a conversation. It analyses learning evidence from quizzes and projects to estimate competencies, adapts project briefs to each learner's context, recommends tutors based on demonstrated expertise, and matches learners with employers based on their actual skills.”
Dominika Szulc, AI Engineer
What comes next
TER-AI shows that an online learning platform can do more than deliver content: it can actively close the distance between skill acquisition and employment. As adaptive learning, competency intelligence, and skills-based hiring converge, we see this as the direction the wider EdTech sector is heading: away from credentials as a proxy for ability, and toward direct, evidence-based signals of what people can actually do.
Turing College will continue building on these foundations, deepening the connection between learner competency data and employer needs so the transition from learning to employment becomes faster and more reliable. We're also exploring how this model, competency intelligence feeding directly into personalised development and real opportunity, can extend beyond our own platform to inform how the sector approaches skills-based education and hiring more broadly.
“Taking on a two-year project built around fast-moving AI technology is inherently risky. What makes sense at the application stage can look outdated twenty-four months later, because the field moves that fast. What changed for us wasn't the vision or the ambition, it was our confidence that, with the right team and the right support from the EIC and our National Contact Point, a project this ambitious is far more manageable than it looks from the outside.”
Rūta Gumbelevičiūtė, Head of Business Operations
TER-AI proved a point: AI can make education accountable to the outcomes that matter most to learners: a job, a career, a future. We're glad the European Innovation Council backed that vision, and we look forward to carrying it forward.
FAQ
What does TER-AI stand for?
Tertiary Education Redefined: AI-Driven Upskilling & Reskilling Platform for Industry-Relevant Digital Skills.
Who co-funded TER-AI?
The European Union, under the Horizon Europe framework, through the European Innovation Council Accelerator programme (Grant Agreement No. 101161973).
What did TER-AI build?
Four AI capabilities now live on Turing College's platform: Competence Intelligence, Personalised Learning Paths, Evidence-Based Tutor Matching, and Student-Company Matching.
What happens after the project ends?
Turing College continues building on these foundations, deepening the connection between learner competency data and employer needs.
When was TER-AI announced?
November 2024, when Turing College secured a €2.5 million EIC Accelerator grant, selected from nearly 1,000 applicants across Europe.
Sources: