ARTIFICIAL INTELLIGENCE IN MODERN PEDAGOGY: A HUMAN-CENTERED FRAMEWORK FOR PERSONALIZED LEARNING AND TEACHING

Аннотация

Artificial intelligence (AI) can support pedagogical differentiation, but the production of individualized content does not by itself constitute personalized education. This conceptual review examines how teachers may integrate generative and predictive AI tools into classroom decision-making while retaining responsibility for learning goals, inclusion, and assessment. A structured thematic synthesis of established educational theory and selected scholarly and intergovernmental publications informs a proposed Human-Centered Personalized Pedagogy (HCPP) framework. The framework organizes implementation around learner diagnosis, co-designed goals, bounded AI scaffolding, dialogic classroom activity, evidence-based feedback, and teacher-led review. It also identifies safeguards addressing inaccurate output, language inequity, data minimization, and dependence on automated assistance. Rather than reporting experimental outcomes, the article supplies a theoretically grounded model and a set of indicators that future studies can evaluate. The analysis argues that meaningful personalization requires changes in instructional interaction and learner agency, not merely a higher volume of AI-generated exercises.