Keyword: «preservice teachers»

The purpose of this study was to assess teachers’ working conditions within schools to help identify potential problems and develop strategies and policies to effectively address them. The setting for this study is 50 Texas high schools where 385 Texas high school science teachers participated in this study. Schools were identified using a two-stage stratified random sampling plan. Two explicit stratification variables were used (school size and minority student enrollment proportion) along with an additional implicit stratification method to account for the schools’ location. All principals (n=50; 100% return rate) completed a field-based semi-structured interview. Science teachers within the schools completed a 22-question survey (n=385; 89.6% return rate). In addition, master schedules and Academic Excellence Indicator System reports were collected. In addition, a working conditions rubric was developed and then analyzed using factor analysis. The rubric was applied to all 385 science teachers to obtain a working conditions score. Descriptive statistics were used to describe the frequency and percentages of working conditions components. This study suggests that science teachers experience significantly more difficult working conditions that depend on the size of their school, the teacher’s experience level, and minority student enrollment proportion of the school. In short, working conditions are a significant component within the teacher professional continuum. Educational institutions should examine the working conditions of all teachers more closely, particularly in the light of establishing professional cultures that promote collegiality and interactions among teachers and providing materials for teaching and learning.

The relevance of this study stems from the rapid development of modern technologies and their expanding application within the educational sphere. The digital transformation of education, the active implementation of artificial intelligence (AI) tools, and the growing demand for educators' digital competence underscore the necessity to investigate the readiness of preservice teachers to work with new technologies. However, contemporary teacher training programs often prioritize traditional methods, neglecting the need to integrate AI into the teaching process and develop relevant skills. This creates a risk of professional obsolescence among graduates and a decline in the quality of educational services in the context of technological revolution. Consequently, the aim of this article is to identify the level of readiness among students in pedagogical specialties to apply artificial intelligence in educational activities. Within the research framework, a literature analysis was conducted to identify AI modules in the curricula of teacher training universities. Additionally, an analysis of the content of these curricula was made, revealing the relationship between module design and students' AI readiness. Based on the research findings, shortcomings in the current content of AI-related courses within pedagogical universities were identified; risks associated with students potentially neglecting personalized learning and AI utilization were presented. The following solutions were proposed: reforming teacher education curricula; focusing on addressing ethical and privacy challenges arising from the use of this technology; strengthening student teachers' AI preparation through empirical research and scientific methodology, including experimental studies; integrating AI via digital pedagogy modules. The theoretical significance of the study lies in defining the direction for future research on the specifics of AI application in education. The practical significance of this research is driven by the imperative to prepare students in pedagogical universities for implementing AI in the educational process.