Introduction. The study addresses the challenge of integrating foundational artificial intelligence (AI) and
machine learning concepts into primary education to foster early algorithmic thinking and digital literacy:
methodology and Methods. The study used a mixed-methods design, combining document analysis of the
national draft curriculum with an experimental intervention involving 21 second-grade students using ML for Kids and Scratch. Additionally, the study administered a diagnostic survey to 44 in-service Informatics teachers. Results. A five-stage integration model was established. Practical acoustic model training improved students’ conceptual understanding of machine learning and increased their motivation to learn. Teachers showed strong interest in AI education (88.4%), and demand for specialized training courses was substantial (54.5%). Scientific novelty. The study validates a constructivist, block-based instructional pipeline linking supervised machine learning tools with visual block programming tailored to primary school standards - practical significance. The proposed framework and instructional scenarios offer direct guidelines for educators and curriculum designers implementing applied AI literacy in primary classrooms.