PEDAGOGICAL MODEL AND EXPERIMENTAL STUDY OF DEVELOPING AI-BASED INDIVIDUAL LEARNING TRAJECTORIES IN PRIMARY EDUCATION

Abstract

The rapid advancement of Artificial Intelligence (AI) has introduced innovative opportunities for transforming educational practices, particularly in primary education where individualized instruction plays a critical role in children's cognitive and personal development. This study examines a pedagogical model for developing AI-based individual learning trajectories and evaluates its effectiveness through an experimental educational approach. AI technologies enable teachers to identify learners' strengths, weaknesses, interests, and learning styles while generating personalized instructional pathways that support continuous academic growth. The proposed pedagogical model combines adaptive learning technologies, formative assessment, teacher guidance, and student-centered methodologies to improve educational quality. The experimental findings demonstrate that AI-supported personalized learning increases students' motivation, academic achievement, classroom participation, and independent learning skills. Furthermore, the study highlights the importance of maintaining the teacher's central pedagogical role while integrating intelligent technologies into instructional practice. The research concludes that AI-based learning trajectories contribute to more effective, inclusive, and flexible primary education systems capable of addressing the diverse educational needs of every learner.