Education Sciences

Artificial intelligence (AI) is increasingly used in education, yet evidence regarding its adoption by kindergarten teachers remains limited, particularly in Indonesian early childhood settings. This study investigated the AI tools used by kindergarten teachers, their educational purposes, implementation challenges, and technology acceptance factors influencing AI integration in Malang, Indonesia. The design used was an exploratory sequential mixed-methods approach. Ten Guru Penggerak and five kindergarten administrators participated in interviews, observations, and document analysis to gather qualitative data. Findings informed a questionnaire administered to 250 kindergarten teachers selected through equal-allocation stratified random sampling across five districts of Malang City. Partial least squares structural equation modeling was used to analyze quantitative data. Qualitative findings showed that teachers used AI for lesson planning, play-based learning design, storytelling, media creation, assessment, documentation, parent communication, and professional learning. Teachers critically reviewed AI-generated content to ensure developmental, curricular, cultural, ethical, and safety appropriateness. Major challenges included limited AI literacy, inadequate infrastructure, restricted premium features, inaccurate outputs, insufficient institutional support, and privacy concerns. According to quantitative findings, behavioral intention was positively and substantially correlated with performance expectancy, effort expectancy, and enabling factors, but there was no statistically significant correlation with social influence. Behavioral intention influenced the effects of performance expectation, effort expectancy, and enabling environments and was substantially correlated with self-reported AI integration. These results imply that teachers’ perceptions of AI’s utility, usability, institutional support, and professional competence all play a role in its successful incorporation in kindergarten. Strengthening AI literacy, prompting skills, ethical awareness, and developmentally appropriate pedagogical practices is essential for sustainable AI implementation in early childhood education.

Systemic pedagogy was developed for Angelica Olvera at the CUDEC center and implemented in schools in Spain and Latin America; however, quantitative evidence remains absent. This exploratory pilot study examined whether students in classrooms implementing this approach showed higher observable academic performance in Spanish Language and Mathematics in Grades 1–2 of Primary Education, using a quasi-experimental pretest–posttest design with one classroom per condition per grade. Sixty-nine students participated (Grade 1: n = 33; Grade 2: n = 36); direct-observation rubric scores, operationalized as teacher-rated observable academic performance, served as dependent variables, analyzed via ANCOVA with pretest scores as a covariate. All four primary analyses showed significant group differences (p ≤ 0.003), with large-to-very-large effect sizes (ω²p = 0.225–0.540; d = 0.40–1.16). In Grade 2, the control group declined in Language (Δ = −0.044, p = 0.025) and showed no gains in Mathematics. Subscale results revealed a cross-grade descriptive pattern consistent with a hypothesized temporal progression: relational and procedural dimensions showed larger differences in Grade 1, whereas cognitive dimensions showed larger differences in Grade 2. A genuine test of this sequence would require longitudinal measurement of the same cohort. Aligned with Control-Value Theory and Self-Determination Theory, these preliminary, hypothesis-generating findings require replication with larger samples, multiple classrooms per condition, and blinded external assessors.

Italy remains well below the European average in tertiary attainment, a gap that has reinforced a policy narrative centered on the need to increase the number of graduates. Yet aggregate attainment indicators provide limited information on the nature of graduate expansion, particularly in terms of degree level, field of study, institutional type, and labor-market alignment. This paper examines the expansion of distance-learning universities in Italy as a specific case through which to assess the nature and implications of graduate growth. Using administrative data from the Italian Ministry of University and Research and AlmaLaurea survey data, the study compares graduates from state, non-state campus-based, and distance-learning universities across degree levels and fields of study. Results show that distance-learning universities have become a major contributor to graduate growth, accounting for 18% of graduates in 2024. However, their expansion is concentrated in fields such as sport science, law, education, psychology, and social sciences, while their presence remains limited in STEM and health-related areas. These findings suggest that distance-learning higher education may formally help Italy move closer to European tertiary attainment targets, while also highlighting the need to consider whether it addresses the disciplinary imbalances that shape graduate supply and labor-market alignment.