Integrating Open‐Source Image Analysis Into Problem‐Based Learning for Chemical and Materials Engineering Education

ABSTRACT

The ongoing Fourth Industrial Revolution, driven by digital transformation and collectively known as Industry 4.0, means that future materials and process engineering must develop advanced cross‐disciplinary competencies in areas such as data analytics, computational modeling and image‐based interpretation. This study presents and evaluates an educational innovation that integrates ImageJ into a problem‐based learning (PBL) framework to enhance analytical, digital, and reflective skills in engineering education. The approach was implemented across three international institutions in Spain and Mexico at both undergraduate and master's levels, combining instructor‐led workshops, directed academic activities (DAAs), and autonomous student projects. A total of 72 students used ImageJ to analyze real microstructural images and establish process–structure–property relationships. Mixed‐method evidence, derived from surveys and reflective reports, revealed substantial gains in analytical reasoning, digital literacy, and self‐directed learning. Specifically, 83% of participants rated ImageJ as highly useful for their current coursework, 89% for future professional practice, and over 90% recommended broader adoption of open‐source tools in engineering curricula. The findings demonstrate that integrating open scientific software into PBL environments promotes engagement, strengthens engineering reasoning, and supports the development of transferable digital competencies. The paper concludes by outlining pedagogical implications and practical guidelines for scaling this framework across materials and chemical engineering programs.