Abstract:

Materials education in engineering degrees is often based on theoretical lectures in which concepts related to the classification, properties and applications of different materials are presented. However, achieving a deeper understanding of these topics requires teaching strategies that connect theoretical knowledge with direct observation and experimentation. In this context, active learning and gamification strategies can increase student motivation and foster a better understanding of material behaviour.

This paper presents a teaching experience developed in the course Design Workshop III of the Bachelor's Degree in Industrial Design Engineering and Product Development at the Escuela Politécnica Superior de Alcoy of the Universitat Politècnica de València. The activity is implemented within the metallic materials topic in the materials area of the course and aims to help students understand the properties, classification and applications of metals used in product design and development.

The proposed methodology combines collaborative learning, gamification and the use of artificial intelligence tools. The session begins with a brief theoretical introduction in which the instructor presents the fundamental concepts related to metallic materials and their classification into ferrous and non-ferrous metals. Afterwards, students, organised in small groups, are given time to review the remaining content using the course slides and artificial intelligence tools in order to identify strategies for recognising different metals based on their properties. After this preparation stage, a gamified activity is carried out in which each group receives several real metal samples that must be identified. The activity is organised into three progressive stages designed to gradually increase the amount of information available. In the first stage, students perform a visual identification based on observable properties such as colour, surface finish or apparent weight. If all metals are correctly identified, students obtain additional points in the theoretical exam. Otherwise, groups proceed to a second stage in which tools such as magnets and scales are introduced to analyse additional physical properties. If the identification is correct at this stage, students also receive additional points, although fewer than in the first stage. Finally, groups that still present errors move to a third stage in which the instructor indicates which identifications are incorrect, allowing students a final opportunity to revise their answers. This progressive structure introduces elements of challenge and reward that increase motivation and engagement.

The results obtained show high levels of participation and motivation, as well as improved understanding of metallic material properties. The activity also promotes key competencies such as materials-based reasoning, technical observation and teamwork. This experience demonstrates that integrating active learning, hands-on experimentation and artificial intelligence can significantly improve the teaching and learning process in materials-related courses.

Keywords:

Materials education, engineering education, active learning, gamification, artificial intelligence, metallic materials, collaborative learning, hands-on experimentation, student motivation, materials-based reasoning.