IMPLEMENTING THE CDIO FRAMEWORK IN DATA-DRIVEN ENGINEERING CONTEXT: A COURSE-LEVEL CASE STUDY
Paper in proceeding, 2026

Engineering practice has been increasingly influenced and developed by digitalization, data availability, and advanced analytics using simulation, modelling, and Artificial Intelligence. As a result, modern engineers are expected not only to design and realize products, but also to use data to inform decisions across the entire product realization lifecycle from conception to operation and continuous improvement. This development creates new challenges and opportunities for CDIO-based engineering education. The CDIO framework aims to educate engineers capable of Conceiving, Designing, Implementing, and Operating complex systems in authentic contexts. CDIO Syllabus 3.0 reinforces this ambition by explicitly emphasizing digitalization, data-informed decision-making, sustainability, reflective learning, and systems thinking. However, many institutions still face difficulties in operationalizing these high-level learning outcomes into concrete, course-level implementations. Therefore, this paper aims to present a course-level case study of CDIO implementation through the course Data-Driven Product Realization, developed within the Chalmers initiative Tracks. The course is structured according to the Cross-Industry Standard for Process Mining model, which is an iterative and systematic approach for planning, executing, and deploying data analytics projects. It follows a project-based learning pedagogy, where diverse student teams engage in solving complex real-world industrial problems together with the companies. The paper contributes a detailed mapping between the course elements, CDIO Syllabus 3.0 outcomes, and CDIO Standards. Additionally, it proposes a reusable course design pattern that can support institutions seeking to implement CDIO Syllabus 3.0 in data-driven engineering contexts. Findings from course evaluation survey and student and teacher reflections demonstrate that the course effectively enhances integrated learning of technical and professional engineering competencies.

Professional Skills Development

Standards 1,2,7,8,and 11

Project-Based Learning

CDIO Syllabus 3.0

CDIO Implementation

Data-driven Engineering Course

Author

Ebru Turanoglu Bekar

Chalmers, Industrial and Materials Science, Production Systems

Ida Gremyr

Chalmers, Technology Management and Economics, Innovation and R&D Management

Kristina Henricson Briggs

Tracks/Fuse

Anders Skoogh

Chalmers, Industrial and Materials Science, Production Systems

Jon Bokrantz

Chalmers, Industrial and Materials Science, Production Systems

Proceedings of the International CDIO Conference

20021593 (eISSN)

431-444
9789188041654 (ISBN)

22nd International CDIO Conference, CDIO 2026
Liverpool, United Kingdom,

Subject Categories (SSIF 2025)

Production Engineering, Human Work Science and Ergonomics

Other Engineering and Technologies

Learning and teaching

Pedagogical work

More information

Latest update

7/20/2026