Erasmus+ • EU funded

Why 4SiM is relevant

Linking higher education and industry through AI-enhanced case-based learning and simulation for sustainable, human-centric manufacturing.

Handshake symbolizing academia–industry collaboration

Relevance at a glance

Educational • Industrial • Societal

4SiM addresses the growing need to teach and practice systems thinking in modern manufacturing. By combining real-world cases, simulation, and AI assistants, the platform raises learner motivation (ARCS/IMMS), improves decision-making skills, and accelerates transfer of knowledge from academia to the workplace.

Three pillars of relevance

What changes for learners, companies, and society

Educational relevance

4SiM strengthens curricula with experiential learning that mirrors complex socio-technical realities, improving engagement and outcomes.

  • AI-supported case-based and problem-based learning.
  • Simulation and (future) digital twins to explore scenarios safely.
  • Motivation and engagement tracked with ARCS/IMMS.
  • Open resources for scalable adoption across courses.

Industrial relevance

Companies benefit from graduates and trainees who think systemically and can act on data and human-factor insights.

  • Bridging quality, safety, human factors, and Lean practice.
  • Faster onboarding via realistic case scenarios.
  • Improved problem-solving for Industry 4.0/5.0 challenges.
  • Academia–industry co-creation of reusable training assets.

Societal relevance

Human-centric and sustainable production contributes to resilient European industry and better workplaces.

  • Green, digital, and accessible learning practices.
  • Ethical and inclusive design of socio-technical systems.
  • Safer, more sustainable operations and workforce well-being.
  • Open access results for broad community impact.

Evidence and early insights

Pilots and instruments
203
pilot responses
36
IMMS items
3
AI characters

Early pilots suggest measurable gains in learner engagement and clearer links between theoretical constructs and real manufacturing constraints.