OBJECTIVES & TARGET GROUPS

The project objectives are:
– To create and deploy a novel structured peer mentoring framework that utilizes AI tools to match Roma students as peer mentors with mentees effectively and track progress in real-time.
– To equip 30 mentors and 30 mentees with high-level AI and STEM competencies, making them more resilient and employable in the modern labor market.
– To develop and implement Microcredential training for teachers on STEM AI peer mentoring
– To realise one semester peer mentoring support at 94 SU.

The following target groups are direct participants in the project activities and they will take direct benefits from its realisation:

1. Disadvantaged and marginalised students (with Roma origin)
Target: 30 peer mentors (grades 11-12) and 30 mentees (grades 8-10).
Identified needs: Roma students often face a lack of visible success stories within the academic system (based on the The Education of Roma Children: Challenges and Promises (2024). They require “critical friends” who share their cultural background and have successfully navigated the same educational challenges.  We see there among conclusions that there is a low-pressure learning environment still in Bulgaria, where technology should be utilized as a collaborative tool rather than serving as an intimidating barrier. These students need high-level STEM and AI competencies to overcome the “double disadvantage” of socio-economic marginalization and the digital divide, ensuring they remain competitive in the modern labor market.

2. VET teachers
Target: 95 educators (aged 27–61) from which 15 will be trained as facilitators (“Operator in Clothing Production” and
“Courier/Logistics.”)
Identified needs: While we at 94 SU possess a new STEM centre, our teachers are declaring a lack the specific preparation to use its functionalities in a didactically efficient and pedagogically sound way. Our teachers need to transit from passive technology users to active facilitators of ethical and responsible AI usage within specific vocational disciplines.They require training (via internally conducted survey results) on how to design, manage, and monitor a peer-mentoring framework within a technical curriculum without increasing their administrative burden.

3. School leadership and pedagogical staff
Target: 4 school principals/vice-principals and pedagogical counsellors (aged 40–55).
Identified needs: AI-driven analytics into VET curricula of Clothing production and Courier wil shift from reactive to proactive support, identifying dropout “red flags” before students fully disengage.At the top they need expertise in evaluating the effectiveness of pilot programs to ensure they can be integrated into the school’s long-term academic improvement plan and long-term strategy.

4. AI/STEM experts (BIST & MENTRIS)
Target: AI/STEM specialists and didactic academicians (aged 27–65).
Identified needs: Experts from 2 partners require a real-world “laboratory” (such as 94 SU) to test and refine predictive AI models and inclusive mentoring code of conduct in a VET setting. There is a need for structured places to transfer high-level research from Greek and Bulgarian academic contexts into practical vocational field.