MECHANISMS FOR INTEGRATING ARTIFICIAL INTELLIGENCE INTO DISTANCE LEARNING PLATFORMS AND THEIR PROSPECTIVE PEDAGOGICAL IMPACTS

Abstract

Distance learning platforms are increasingly becoming an essential part of higher education, yet their pedagogical effectiveness depends on the extent to which they support personalization, timely feedback, learning motivation and self-regulated learning. This study examines the mechanisms for integrating artificial intelligence into distance learning platforms and evaluates their prospective pedagogical effects. The research was conducted using a mixed-method design involving 400 undergraduate students and 15 experts in digital education. Student responses were analyzed using the Modified Priority Needs Index (PNI modified), while expert judgments were used to calculate the Composite Priority Index (CPI), which combined perceived need, pedagogical significance and feasibility of implementation. The results showed that the highest priority areas for AI integration were adaptive learning, intelligent educational data analytics, personalized recommendation systems and automated assessment. Among the expected pedagogical effects, the most significant were personalization of learning, improvement of feedback quality and enhancement of learning motivation. Expert assessment confirmed the high pedagogical significance and practical feasibility of the proposed AI integration model. The study contributes to the development of a structured framework for planning AI implementation in distance learning platforms and provides practical guidance for educational institutions seeking to improve the quality of digital learning environments.