The augMENTOR project successfully created an AI-boosted educational ecosystem by combining advanced data science with robust pedagogical frameworks, following initial systematic literature reviews and user requirements elicitation.
Key technical achievements included the design and implementation of the augMENTOR reference architecture, a layered microservices architecture supported by a Knowledge Graph which seamlessly managed heterogeneous LMS data. Scientifically, the project advanced learner profiling using techniques like Gaussian Mixture Models to create dynamic, multidimensional profiles.
Pedagogically, the project delivered the validated augMENTOR Pedagogical Framework, comprising the macro-level Pedagogical Design Model with Emerging Technologies (PeDeMET) and the micro-level Technology-augmented Educational Scenarios and e-Activities (TESA), which incorporates Creative Pedagogy. This framework facilitated the creation of practical, technology-augmented scenarios, integrating an assessment strategy for 21st-century skills via explainable AI (XAI) recommendations.
Full-scale validation was achieved through diverse pilot implementations, confirming the versatility and effectiveness of the AI-boosted feedback mechanisms, with high educator utility and learner support. A "Human-in-the-Loop" philosophy ensured the AI complemented, rather than replaced, pedagogical judgment.
The project also established a rigorous legal and ethical framework, ensuring GDPR compliance and ethical oversight, supported by a dedicated Ethics Manager and an external Ethics Advisory Board, adhering to high European standards for trustworthy AI.
Finally, the project produced a set of policy briefs based on outcomes, lessons learned, and input from national and international policy-making events.
