EDUCATIONAL RECOMMENDATION SYSTEMS: PERSONALIZED PATHWAYS AND STUDENT AUTONOMY

Abstract

The study addressed AI-based educational recommendation systems in distance education, emphasizing personalized learning pathways and student autonomy. It examined how such systems, when structuring recommended learning routes, could contribute to learner autonomy without compromising pedagogical intentionality and formative principles. The overall objective was to systematize, through a bibliographic study, the relationship between content recommendation, pathway personalization in distance education, and student autonomy. The adopted methodology was bibliographic research, analyzing selected academic publications on artificial intelligence in education and distance learning. In the development, the foundations of recommendation systems, their use in organizing personalized pathways, and implications for autonomy were discussed, indicating that recommendations tended to support self-regulation when they functioned as decision support integrated into instructional design, but could weaken autonomy when operating opaquely and prescriptively. In the final considerations, it was concluded that contributions to autonomy depended on explicit pedagogical criteria, mediation, and transparency, and that empirical studies were needed to strengthen evidence on learning and self-regulation outcomes in real distance education settings.