EFFECTIVENESS OF AI-BASED ADAPTIVE EDUCATIONAL TECHNOLOGIES IN DEVELOPING PROFESSIONAL COMPETENCIES OF ENERGY ENGINEERING STUDENTS

Abstract

The article evaluates the effectiveness of an artificial intelligence-based adaptive educational technology in the training of energy engineering students. The study aimed to identify changes in professional competency levels and determine whether the experimental and control groups differed statistically. The quasi-experimental study involved 370 students from the Kashkadarya, Jizzakh, Fergana, and Bukhara regions: 187 students in the experimental group and 183 in the control group. The technology combined initial diagnostics, adaptive delivery of theoretical and practical tasks, simulation of professional situations, individualized feedback, and learning-data analysis. Competencies were assessed on a three-level scale; distributions were compared using Pearson's chi-square test, while composite scores were compared using Welch's t-test. Before the intervention, the groups did not differ statistically (χ² = 0.347; p = 0.841). At the final stage, 56.7% of the experimental group and 36.1% of the control group reached the high level, while the low level was recorded for 13.9% and 31.1%, respectively. The final distributions differed significantly (χ² = 21.057; p < 0.001; V = 0.239). The normalized composite score reached 80.9% in the experimental group versus 68.3% in the control group (t = 4.700; p < 0.001; d = 0.489). These findings indicate the practical effectiveness of the technology while confirming the instructor's leading role and the need for further studies with stronger control of confounding factors.

Keywords

artificial intelligence, adaptive learning, professional competencies, engineering education, energy engineering education, learning analytics, pedagogical experiment, assessment, personalized learning, digital educational technology.