ARTIFICIAL INTELLIGENCE AS A DRIVER OF DIGITAL TRANSFORMATION IN HIGHER EDUCATION: OPPORTUNITIES, CHALLENGES, AND INSTITUTIONAL CONDITIONS FOR EFFECTIVE IMPLEMENTATION
Keywords:
artificial intelligence, digital transformation, higher education, academic integrity, personalized learning, learning analytics, generative AI, institutional policy, digital divide, Uzbekistan.Abstract
The article presents a comprehensive analysis of artificial intelligence (AI) as a
factor driving the digital transformation of higher education. Based on a systematic review
of contemporary scientific literature published between 2023 and 2026, the study
identifies key opportunities for AI applications in university environments, including
personalized learning through adaptive systems, learning analytics as a tool for the early
identification of academic risks, automation of administrative processes, support for
research activities, and transformation of knowledge assessment methods. At the same
time, the main challenges and limitations are systematically examined, including threats
to academic integrity, ethical risks associated with data use, algorithmic bias, uneven
levels of faculty readiness, the digital divide, and the immaturity of regulatory frameworks.
The analysis concludes that the effectiveness of AI implementation is determined not by
the technology itself but by the maturity of institutional conditions, including the
availability of operational policies, systematic staff training, redesigned assessment
methods, and clearly defined responsibilities for all participants in the educational
process. The study demonstrates that Uzbekistan’s higher education system, which is
implementing a large-scale national digitalization strategy (the Concept for the
Development of Artificial Intelligence until 2030 and the Digital Uzbekistan – 2030
Program), continues to face a significant shortage of empirical research, thereby defining
a priority direction for further scientific investigation. Practical recommendations for
universities are formulated, and a framework for subsequent empirical studies is
proposed.
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