The Experience of Individualizing the Learning Process Using Artificial Intelligence–Based Platforms

This article examines the theoretical and practical aspects of individualizing the learning process through artificial intelligence–based digital platforms. The relevance of the study is determined by the need to organize effective learning in a modern digital educational environment while considering students’ individual needs, abilities, and learning pace. The aim of the research is to identify the mechanisms of personalized instruction through AI-driven adaptive platforms and to evaluate their pedagogical effectiveness experimentally. The study employed a quasi-experimental design and utilized pedagogical observation, diagnostic testing, questionnaires, learning analytics data analysis, and statistical methods, including the Student’s t-test and Cohen’s d [2]. The results demonstrate that AI-based platforms significantly enhance the formation of individual learning trajectories, real-time monitoring of academic progress, and immediate feedback. Students in the experimental group showed significantly higher academic performance than those in the control group (t = 2.31, p < 0.05; Cohen’s d = 0.74). The study also proposes an integrative pedagogical model for AI-supported learning individualization.

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