Methodology for improving students’ intellectual-communicative competence through artificial intelligence in inclusive education

Abstract

The article examines a methodology for enhancing students’ intellectual-communicative competence in inclusive higher education through the pedagogically grounded use of artificial intelligence. Drawing on approaches to communicative competence, competency-based learning, Universal Design for Learning, and AI-assisted language education, intellectual-communicative competence is defined as an integrative ability to analyze information, formulate and justify a position, interact effectively in a foreign language, and regulate learning through reflection. A four-component structure is proposed, comprising motivational-axiological, cognitive-intellectual, communicative-activity, and reflective-regulatory components. The study substantiates a methodological cycle of “diagnosis – intellectualization – communication – feedback – reflection” and identifies conditions for its implementation in an inclusive environment, including personalization, differentiation, accessibility, critical verification of AI-generated responses, and teacher mediation. The article argues that artificial intelligence should function as a cognitive-communicative tool rather than a source of ready-made answers and concludes that the proposed model requires experimental validation in authentic educational settings.