The purpose with this mixed-methods research was to examine students’ success in using artificial intelligence. The research sample consisted of 50 first-grade primary school students, 239 parents, and 25 primary school teachers studying in Almaty, Kazakhstan. A descriptive analysis technique was used to analyse the qualitative data. Findings are explained in themes. As a result of the research, it has been observed that parents’ attitudes towards their children’s use of technology were high. Most primary school teachers who participated in the research stated that students were interested in artificial intelligence, they supported the use of artificial intelligence technologies to some extent, and they found the students partially successful in this regard. Students participating in the research defined artificial intelligence as technology, computers that think like humans, smart machines, entertaining and educational computer content, robots that obey given commands, and technological devices that make life easier. Most students stated that they liked using artificial intelligence and that they found themselves somewhat successful in using artificial intelligence.

The relevance of the research under consideration is due to the need to improve the efficiency of the analysis of the quality, and completeness of the knowledge obtained by students when solving computational problems, the example problems in mathematics. The theoretical argumentation is proposed and the practical implementation of an intelligent automated analytical system for analyzing the quality and forecasting the content of educational material and the trajectories of the student's learning direction is described. The relevance of the research is the creation of neural network algorithms that allow analyzing the dynamics of changes in the student's level of formation of skills to solve arithmetic problems. The methods of analyzing the assimilation of educational information and methods of personalized construction of the curriculum for each student are substantiated.

The current study investigates teachers’ perceptions and acceptance of Artificial Emotional Intelligence (AEI) in teacher education. It particularly focuses on the role of demographic and professional patterns, drawing on data from a sample of 1.100 educators collected across Kazakhstan. The study employed a quantitative research design, including descriptive statistics, reliability analysis, group comparisons, and multiple regression analysis. The study results demonstrate a generally high level of AEI acceptance among teachers (M = 4.37), particularly regarding its potential to enhance emotional assessment and support in professional development. The AEI scale itself revealed a strong internal consistency (Cronbach’s α = 0.923), confirming the reliability of the measurement. However, comparatively lower levels of trust in AI-generated results suggest a gap between positive attitudes and full confidence in the technology. The findings suggest that teachers’ attitudes toward AEI are generally consistent across participating groups. In turn, it challenges traditional assumptions about the extent to which demographic attributes may influence technology adoption in education. In addition, the observed gap between acceptance and trust shows the need to address ethical issues. The current study contributes to research in an emerging field of AI in education by emphasizing the role of contextual and trust-related factors in shaping perceptions of emotionally intelligent technologies.

Background. The study focuses on the need for effective strategies to address global challenges, such as the COVID-19 pandemic, which requires not only medical but also technological responses. In this context, the research aims to analyze issues and assess the potential use of artificial intelligence (AI) in predicting the spread of the virus, taking into account current knowledge and identifying gaps in understanding this field. The aimof this study is to analyze scientific publications dedicated to the use of artificial intelligence in the field of forecasting and prevention of COVID-19 infection. Search strategy.The search for literary sources was conducted in the PubMed database, and the selection of scientific works was based on keywords related to the COVID-19 pandemic and forecasting using artificial intelligence technologies. The search yielded 3,894 publications, extracted on December 27, 2023. Bibliometric analysis was performed using VOSviewer software version 1.6.19, visualizing the interconnections between keywords, identifying clusters of similar terms, and facilitating a deeper understanding of the research topic, trends, and directions in the field of artificial intelligence for combating the COVID-19 pandemic. Exclusion of articles not meeting the keyword criteria was done manually. From the initial pool of 3,894 works, a final set of 23 most relevant publications was selected, reflecting the researched theme and meeting the established search criteria. Conclusions. Contemporary trends and prospects of utilizing models and AI for forecasting COVID-19 outbreaks demonstrate an interdisciplinary approach, encompassing statistical analysis, simulation models, machine learning, and intelligent data analysis. The study emphasizes the importance of data quality, the selection of appropriate algorithms based on country-specific data, and the potential of AI to make a significant contribution to decision-making in public health and pandemic management. Введение. Исследование фокусируется на необходимости эффективных стратегий борьбы с глобальными вызовами, такими как пандемия COVID-19, требующей не только медицинских, но и технологических ответов. В данном контексте, исследование направлено на анализ проблем и оценку потенциала использования искусственного интеллекта (ИИ) для прогнозирования распространения вируса, с учетом текущих знаний и выявления пробелов в понимании этой области. Целью настоящего исследования является анализ научных публикаций, посвященных использованию искусственного интеллекта в области прогнозирования и профилактики инфекции COVID-19. Стратегия поиска. Исследование литературных источников о пандемии COVID-19 и прогнозировании с применением технологий искусственного интеллекта проведено с использованием базы данных PubMed. Результаты поиска включают 3,894 публикации, выгруженные 27 декабря 2023 года. Библиометрический анализ с применением программного обеспечения VOSviewer 1.6.19 позволил визуализировать взаимосвязи ключевых слов, выявив кластеры схожих терминов и обеспечив глубокое понимание темы исследования в контексте использования искусственного интеллекта для противостояния пандемии COVID-19. Ручная фильтрация исключила статьи, не соответствующие критериям по ключевым словам, и сформировала окончательный набор из 23 наиболее релевантных работ, отражающих тематику и соответствующих установленным критериям. Выводы.Современные тенденции и перспективы применения моделей и ИИ для прогнозирования вспышек COVID-19 демонстрируют междисциплинарный подход, включающий статистический анализ, имитационные модели, машинное обучение и интеллектуальный анализ данных. В исследовании подчеркивается важность качества данных, выбора подходящих алгоритмов на основе данных по конкретной стране, а также потенциал ИИ для внесения значительного вклада в принятие решений в области общественного здравоохранения и управление пандемиями. Кіріспе. Зерттеу тек медициналық ғана емес, сонымен қатар технологиялық жауаптарды қажет ететін COVID-19 пандемиясы сияқты жаһандық сын-қатерлермен күресудің тиімді стратегияларының қажеттілігіне бағытталған. Осы тұрғыда зерттеу проблемаларды талдауға және...