The integration of machine learning (ML) into agent-based systems has transformed the landscape of artificial intelligence (AI), especially in adaptive, dynamic, and autonomous decision-making. From intelligent tutoring systems to adaptive simulations and robotics, ML-augmented agents offer real-world applications across domains. Modern pedagogy demands that students not only understand classical AI but also develop competencies in programming learning agents capable of real-time adaptation. Research has shown that combining the instruction of agent-based modeling with machine learning enhances students’ grasp of concepts and improves their hands-on abilities in learning AI.
- Docente: Jesus Insuasti