Exploring Data-Driven and Human-Centered Methodologies in Engineering Education: A Preliminary Validation Framework

This paper presents an initial validation of a methodology that integrates data-driven methods and artificial intelligence tools into engineering education, aligned with the Industry 5.0 framework and the Sustainable Development Goals. The approach combines data analysis and human-centered methodologies, integrating Challenge Based Learning, User-Centered Design, and Data-Driven Decision Making to address complex real-world challenges. A collaborative digital platform was developed to collect and analyze experimental data from student projects in mortars with different additives, using tools such as Orange and Weka to introduce basic data mining concepts. Ten student projects generated a shared experimental database that supports predictive modeling of mechanical performance. Despite the limited dataset size, the experience promoted critical thinking, collaboration, and iterative improvement.