Educational robotics as a strategy for social inclusion and pedagogical intervention in vulnerable youth communities - Acosta-Amaya, Gustavo A.; Peña-Palacio, Juan A.; Jiménez-Builes, Jovani A. (Frontiers in Robotics and AI, 2025)
IntroductionIn mining regions of Latin America, thousands of children and adolescents are deprived of formal education because of their participation in labor-intensive economic activities. This study addresses how educational robotics can serve as a strategy for both social inclusion and pedagogical intervention in communities with disrupted or nonexistent schooling.MethodsA multi-site intervention was implemented, directly benefiting 2,500 out-of-school or at-risk youth and 250 teachers in rural mining regions. The initiative encompasses the design and construction of educational robots and learning materials by university engineering students. Activities were conducted via project-based learning sessions and teacher training workshops. A mixed-methods approach was employed, integrating surveys, interviews, and participant observation to assess the impact on motivation, re-engagement with schooling, and pedagogical practices.ResultsThe findings indicated increased student engagement, enhanced collaborative learning, and a measurable rise in school re-enrollment within the participating communities. Educators reported enhanced confidence in utilizing technological tools and heightened motivation among students. The robots acted as mediating artifacts, facilitating dialogical, hands-on learning experiences and bridging gaps between formal education and local realities.DiscussionThe results underscore the potential of educational robotics to serve not just as a pedagogical instrument but also as a transformative vehicle for fostering inclusion, motivation, and equity in marginalized environments. The initiative also demonstrates the significance of university-community collaboration in addressing educational inequality through innovation. Challenges include maintaining long-term impact and scaling the model to other contexts with similar vulnerabilities.