Integration of Artificial Intelligence Technologies into the Educational Process: Uzbekistan's Experience and Future Prospects

Abstract

This article reports a mixed-methods field study on how AI-based educational technologies affect learning in Uzbekistan. It was carried out in 15 schools and 3 higher education institutions across the Tashkent, Samarkand, and Fergana regions during the 2023–2024 academic year. An intervention group was compared with a comparison group over 16 weeks (920 students in total), and interviews were held with teachers and students. We looked at knowledge acquisition, learning motivation (ARCS), task-completion time, teachers’ administrative workload, and knowledge retention 12 weeks later. On the descriptive means, the intervention group showed about 27.5% higher knowledge acquisition than the comparison group, along with stronger motivation, better retention, and faster task completion. Teachers’ administrative workload was 34.6% lower, which reflects reduced workload rather than a general gain in teaching effectiveness. These differences still need to be confirmed with inferential analyses that account for clustering and baseline values. The article also considers the policy setting for AI in Uzbek education, keeping policy goals separate from the empirical findings. The results should be read as associations observed under the study conditions, not as proof of a universal causal effect.