Transforming Teacher Experience through AI: A Quantitative Study of MugalimAI’s Effects on Teachers’ Workload, Self-Efficacy, and Job Satisfaction

Abstract

This study examined the association between the use of MugalimAI, a locally developed AI-powered lesson-planning platform, and three dimensions of teachers’ professional experience, namely workload, self-efficacy, and job satisfaction, among in-service teachers in Kyrgyzstan. Guided by the Job Demands-Resources (JD-R) model, the study addressed two research questions: how the use of MugalimAI relates to teachers’ professional experience within the user group, and in what ways MugalimAI users differ from non-users on these dimensions. A quantitative, non-experimental, cross-sectional design was employed, combining correlational and group comparison analyses. Data were collected through a structured online questionnaire administered to a purposively selected sample of in-service teachers in Kyrgyzstan; of the 215 initial responses, the final analytical sample following data screening consisted of 154 teachers, including MugalimAI users (n = 59) and non-users (n = 95), with a further refined subsample of 47 users retained for correlational analyses. Spearman’s rank-order correlations were used to examine associations between the duration and frequency of platform use and teacher outcomes within the user subsample, while group differences between users and non-users were examined through inferential comparison analyses. Correlational analyses revealed no statistically significant associations between the intensity of MugalimAI use and workload, self-efficacy, or job satisfaction; however, a strong positive relationship between self-efficacy and job satisfaction was observed within the user group. Group comparison analyses indicated that MugalimAI users reported significantly lower workload across multiple indicators, including total working hours, time pressure, and work-life imbalance, as well as higher self-efficacy, particularly in classroom management and student engagement, and greater overall job satisfaction. These findings suggest that MugalimAI may function as a professional resource enabling more favorable teacher outcomes, consistent with a threshold-based rather than dose-dependent interpretation of AI tool use. The study extends the JD-R framework to AI-supported teaching in a low- and middle-income, post-Soviet context and offers empirical evidence relevant to educational policy, platform design, and teacher well-being.