Relationship between integrating artificial intelligence into the work processes of early childhood leader-educators and their levels of productivity, professional self-efficacy, stress, and burnout (Hebrew)

Kindergarten teachers in Israel work in an environment characterized by a high workload, complex professional responsibilities, and lack of resources, and report work-related stress at levels above the international average (TALIS, 2018; Farewell et al., 2022; Ng et al., 2023). This situation places early childhood leader-educators at high risk of burnout, with direct implications for the quality of education provided to children and the turnover rates in the profession. In the current technological era, artificial intelligence (AI) tools are becoming more accessible and integrated into many fields of work, raising the question of whether they can also be harnessed to meet the needs of early childhood leader-educators. Few studies have examined their actual impact on this population, and in particular the question of how early childhood leader-educators themselves can use AI for their own professional work (Ljungcrantz, 2026). The aim of the present study was to examine the effect of a dedicated intervention program to integrate AI into the work of early childhood leader-educators on productivity (work performance), professional self-efficacy, stress and burnout.

The study included 73 early childhood leader-educators in the Israeli Ministry of Education, assigned via Matching by background variables to an experimental group (34 participants) and a control group (39 participants). The study period lasted three months, during which the experimental group participated in a structured training program for integrating AI into their work, while the control group received no training. Participants in both groups completed questionnaires before (Pre) and after (Post) the intervention. The questionnaires included the Individual Work Performance Questionnaire (IWPQ), a professional self-efficacy questionnaire, and a burnout questionnaire. Difference-in-Differences analyses were conducted using mixed linear models, hierarchical regression analyses, and mediation models.

Findings indicate that early childhood leader-educators who integrated AI into their work demonstrated significant improvement in work performance (p = .025, d = 0.76) and in overall professional self-efficacy (p = .036, d = 0.71). Examining self-efficacy sub-dimensions, a significant increase was found in the management dimension (p = .025, d = 0.76) and a near-significant trend in the relationships dimension (p = .077, d = 0.60), while the knowledge dimension showed no significant change. The extent of actual AI use was found to predict improvement in self-efficacy, and self-efficacy significantly mediated the relationship between extent of AI use and reduction in burnout. A direct and significant effect of AI use on burnout was not found within the timeframe of the current intervention.

The findings suggest that the more frequently and skillfully early childhood leader-educators use AI, the stronger their professional self-efficacy becomes, and this strengthening may lead to a reduction in burnout over the long term. This points to the importance of dedicated training that equips early childhood leader-educators with proficient and informed AI use skills, as adopting the tool alone is not enough. Integrating such training into preparation and professional development frameworks may contribute to strengthening professional self-efficacy and to early childhood leader-educators' overall wellbeing at work.