TRANSFORMING MEDICAL IT EDUCATION IN UZBEKISTAN: A COMPREHENSIVE COMPARATIVE ANALYSIS OF TRADITIONAL PEDAGOGY VERSUS AI-INTEGRATED APPROACHES IN VOCATIONAL INSTITUTIONS

Khodjaeva Nilufar,Ergasheva Ra’no

Authors

Khodjaeva Nilufar,Ergasheva Ra’no Affiliation: Department of Information Technologies, Abu Ali ibn Sino Named Public Health Technical School, Yunusabad District, Tashkent, Republic of Uzbekistan,Department of Information Technologies, Republican Public Health Technical School No. 2 named after Abu Ali ibn Sino, Shaykhantakhur District, Tashkent, Republic of Uzbekistan

Keywords:

artificial intelligence, medical education, vocational training, Uzbekistan, traditional pedagogy, comparative analysis, Medical Information Technologies, digital transformation, technicum.

Abstract

Background: The Republic of Uzbekistan is undergoing a rapid digital transformation in both healthcare and education sectors, driven by the strategic framework of "Digital Uzbekistan – 2030." Vocational medical institutions (technicums) are tasked with training mid-level personnel who are proficient in Medical Information Technologies (MIT). However, prevailing traditional pedagogical methods often fail to equip students with the adaptive, practical, and digital competencies required by modern healthcare facilities.

Objective: This study provides an extensive comparative analysis between traditional teaching methods and Artificial Intelligence (AI)-integrated pedagogical approaches in teaching MIT within Uzbek vocational education. It explicitly details the limitations of the "Old Teaching Style" and the specific achievements of the "AI-Integrated Style."

Methods: A mixed-methods comparative study was conducted over two academic semesters across three medical technicums in Tashkent, Samarkand, and Fergana regions. The study involved 120 second-year students divided into Control (Traditional) and Experimental (AI-Integrated) groups. Data were collected through performance metrics, classroom observations, time-motion studies, and stakeholder interviews.

Results: The analysis reveals profound disparities. Traditional methods exhibited significant limitations in personalization, immediate feedback, language accessibility, and realistic simulation. In contrast, the AI-enhanced model demonstrated a 35% improvement in practical skill acquisition, a 50% reduction in data entry errors, higher student engagement, and better adaptation to local linguistic contexts (Uzbek/Russian).

Conclusion: While traditional methods provide a foundational structure, AI-integrated approaches offer superior outcomes in competency development. For Uzbekistan, a hybrid model that leverages AI while addressing infrastructural constraints is recommended to modernize vocational medical education effectively.

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