Features of the transformation of teaching methodology in the conditions of using artificial intelligence, digital platforms and adaptive educational resources

Abstract

In the conditions of intensive digitalization of the educational environment, the integration of artificial intelligence technologies and adaptive platforms determines the fundamental transformation of teaching methodology, manifested in the rethinking of all components of the didactic system, including goal-setting, content, forms of activity, and evaluation criteria. This transformation is characterized by the redistribution of the teacher’s professional functions, evolving from a central source of knowledge and process organizer into a data analyst, designer of individual educational scenarios, and facilitator of independent learning. This shift is accompanied by a significant increase in time costs for interpreting learning analytics by 12.7 percentage points and for individual consulting by 7.9 points, alongside a simultaneous reduction in effort for assignment preparation and checking by 9.3 points and for lecturing by 8.7 points. These changes promote the personalization of educational trajectories, enabling algorithms to adjust material complexity in real time, identify patterns of difficulties, and apply spaced repetition principles. This results in a statistically significant improvement in academic performance, with average score increases of 6.2-8.7 points on a 100-point scale in experimental groups versus control groups. The effect is particularly pronounced in delayed testing, indicating the formation of stable knowledge. Learner perceptions of these innovations vary by educational level: master’s students rate convenience, motivation, and control higher (mean scores 4.03-4.38), while secondary vocational education students show greater caution toward monitoring (2.89 points), prioritizing live pedagogical interaction over automated feedback. Teacher readiness for integration depends primarily on digital literacy and methodological support rather than age or experience, refuting common stereotypes and highlighting the need to invest in data interpretation competencies and the preservation of authorial autonomy in content design. The adoption of data-informed teaching and differentiated approaches based on platform analytics enriches methodological tools; however, the transformation is nonlinear, featuring periods of increased workload followed by a qualitative leap in professional effectiveness once new practices stabilize. Overall, the results illustrate how adaptive technologies act as a catalyst for meaningful changes in educational process organization. These changes require a thoughtful balance between algorithmic optimization, ethical dimensions of decision delegation, and the maintenance of subjectivity among all participants. This establishes the core content of the study, determines its relevance to digital didactics and pedagogical design, and supports an informed decision to consult the full text for detailed insight into the mechanisms, risks of reduced critical thinking and social interaction, and effective implementation strategies across diverse educational contexts.