ADAPTIVE DISTANCE LEARNING FRAMEWORKS IN EMERGING MARKETS: LEVERAGING GENERATIVE AI AND INTERNATIONAL DIGITAL ECONOMY STANDARDS

Аннотация

The rapid expansion of higher education in emerging markets has outpaced the growth of teaching capacity, while generative artificial intelligence (GenAI) offers a potential way to scale personalised tutoring in distance learning. This article aims to design an adaptive distance learning framework for emerging markets that combines GenAI-based tutoring with international digital economy standards. The study used a design science approach that combined three methods: secondary statistical analysis of higher education in Uzbekistan for 2020/21–2024/25, a structured synthesis of randomised controlled trials and meta-analyses on AI tutoring, and a document analysis mapping eight international standards and regulatory instruments onto framework layers. The results show that student enrolment in Uzbekistan grew 2.5 times over four years, correspondence (parttime) enrolment grew 5.7 times and reached 47.0% of all students, whereas full-time teaching staff grew only 1.55 times; the student–teacher ratio rose from 17.8 to 28.9. Restoring the 2020/21 ratio would require about 30.9 thousand additional teachers. The evidence synthesis indicates that GenAI tutoring improves learning when it is pedagogically scaffolded and supervised, but unrestricted access can reduce independent performance. Based on these findings, the article proposes the ADAPT-EM framework, consisting of five layers (access, interoperable content, adaptive AI engine, assessment and learning analytics, governance) aligned with WCAG 2.2, LTI 1.3, xAPI, ISO/IEC 42001, the EU AI Act, UNESCO and OECD instruments and national data protection law. The framework includes a set of performance indicators and addresses data localisation requirements. It can support universities and policymakers in emerging markets in introducing GenAI into distance learning responsibly.