Enabling Federated Learning in the Classroom

Abstract

The recent challenges faced by universities in the realm of educational technology and human-robot interaction have been notable. This pertains not only to the utilization of cutting-edge digital devices but also encompasses shifts in perceptions and learning approaches. The evolution spans behaviorism to cognitive theories and eventually social constructivism. More precisely, it reflects a transition from transmitted knowledge to negotiated knowledge, culminating in harvested knowledge. Artificial intelligence learning environments have undergone significant changes in response to instructional, scholastic, and administrative needs. This paradigm shift in higher education is indicative of a move from traditional pedagogical approaches to contemporary methodologies in learning and educational management. Given the intricate relationship between classroom technology and learning outcomes, a critical examination of learning federation in the educational context becomes paramount. This chapter aims to explore this phenomenon from a sociological perspective, employing quantitative methods facilitated by research instruments such as observation and questionnaires. A total of 204 students from faculty teacher training and education faculty participated in this study, including a random sample of 43 students who were exposed to artificial intelligence in a federated learning environment. The research findings underscore positive responses in terms of understanding AI basics through the FL model, utilizing AI in learning, AI as a learning topic, and attitudes toward AI. These results shed light on the efficacy and benefits of implementing federated learning in educational settings, providing valuable insights for future advancements in the intersection of technology and education.