Bridging Education and Industry for a More Circular Future

Hand of a businessman shaking hands with a Android robot.A new international study developed within the CircleREdu project highlights the growing importance of stronger cooperation between higher education and industry in preparing future professionals for sustainable manufacturing.

The study, entitled “Circular Economy, Reverse Engineering and Education: Course Design at the Intersection of Industry and Education for Systemic Thinking Pedagogy”, was written by representatives of partner universities: Kaisa Tsupari and Lili Aunimo (HHUAS), Tobias Müller (KIT), Vasile Cojocaru (UBB), Ewa Rollnik-Sadowska and Aleksandra Gulc (BUT).

Its main goal is to explore how universities and manufacturing companies can better align their efforts to support the implementation of reverse engineering (RE) within circular economy (CE) business models. In a rapidly changing economic and technological environment, this topic is becoming increasingly important for both education and industry.

The research was based on interviews with 30 academic staff members and 21 industry representatives from Poland, Romania, Germany, and Finland. Using the Ability–Motivation–Opportunity (AMO) framework, the authors examined similarities and differences between academia and industry in terms of skills, motivations, and opportunities related to circular economy and reverse engineering.

The findings reveal that there are still significant gaps between what higher education offers and what the labour market expects. At the same time, the study proposes a structured and practical approach to course design that can help close this gap. In particular, it emphasizes the value of integrative, practice-oriented curricula that foster systemic thinking and better prepare graduates for the challenges of sustainable and circular manufacturing.

This publication is an important step in strengthening the dialogue between education and industry and in supporting the development of future-oriented learning models.