This article examines the integration of artificial intelligence–based adaptive learning systems with traditional pedagogical approaches in middle-school informatics education. The study aims to develop a hybrid instructional framework for supporting personalized learning trajectories, with particular emphasis on low-achieving students. The research is based on a systematic synthesis of selected Scopus- and Web of Science-indexed studies published between 2017 and 2025.

Using conceptual modelling and thematic analysis, the study identifies key technological, pedagogical, cognitive, and motivational factors influencing personalized learning in informatics. On this basis, a three-layer hybrid framework is proposed, integrating AI-driven diagnostics and adaptivity, teacher-led instructional mediation, and learner-centered personalized trajectories. The model also incorporates a four-stage learning cycle consisting of diagnostic assessment, pathway design, hybrid instruction, and mastery validation.

The results demonstrate that hybrid human–AI instructional models enhance learning outcomes by reducing cognitive overload, providing immediate feedback, supporting self-regulation, and strengthening learner motivation. Low-achieving students benefit from individualized task progression and sustained pedagogical support, while teachers gain access to data-informed instructional tools that facilitate targeted intervention.

The study highlights the importance of ethical governance, teacher professional development, and infrastructural readiness for successful implementation of adaptive technologies. Although the proposed framework is conceptual, it provides a theoretically grounded foundation for future empirical research and practical innovation in informatics education.