Pedagogical objectives

• Design AI-supported case-based and PBL pathways aligned with manufacturing realities.
• Embed ARCS/IMMS to monitor motivation and guide instructional decisions.
• Develop open learning resources for easy adoption across programmes.
• Strengthen systems thinking and ethical awareness in Industry 4.0/5.0 contexts.

Technical objectives

• Create a shared requirements & knowledge database (quality, safety, human factors, Lean).
• Integrate the database with a simulation model and AI assistants (scenario guidance).
• Enable scenario exploration and (future) digital-twin alignment for safe experimentation.
• Ensure interoperability and documentation for maintainability.

Validation & impact

• Run multi-site pilots across partner universities and courses.
• Measure engagement, motivation, and learning outcomes (quant + qual).
• Iteratively improve usability and instructional design.
• Report evidence-based guidelines for educators and trainers.

Sustainability & openness

• Publish results, tools, and guides under open access where possible.
• Plan post-project adoption in curricula and enterprise training.
• Ensure inclusive, green, and accessible practices throughout.
• Foster academia–industry co-creation communities.

Project management

• Use transparent governance, sprint reviews, and shared tooling for coordination.
• Track deliverables, budget, and risks; ensure compliance with Erasmus+ guidelines.
• Align dissemination and exploitation with stakeholder needs.
• Maintain quality assurance across all work packages.

Stakeholder engagement

• Co-design cases with industrial partners and educators.
• Offer workshops and webinars for capacity building.
• Gather feedback loops from students, teachers, and companies.
• Promote transferability to other domains of engineering education.

4SiM Knowledge Base

Structured requirements and case assets (quality, safety, human factors, Lean), with educator guides.

Simulation-driven learning

Integrated simulation with AI assistants and classroom-ready cases for scenario exploration.

Pilots & evidence

Multi-site pilots, ARCS/IMMS-based analytics, and a working paper with recommendations.