ВИКОРИСТАННЯ ШТУЧНОГО ІНТЕЛЕКТУ ДЛЯ СТВОРЕННЯ ПЕРСОНАЛІЗОВАНИХ НАВЧАЛЬНИХ ТРАЄКТОРІЙ ФОРМУВАННЯ РАДІАЦІЙНОЇ ГРАМОТНОСТІ

Oleksandr Tymoshchuk

doi:10.14308/ite000804

Authors

Oleksandr Tymoshchuk Rivne State University of the Humanities, Rivne, Ukraine https://orcid.org/0000-0002-4367-4692

DOI:

https://doi.org/10.14308/ite000804

Keywords:

artificial intelligence, radiation education, radiation safety, radiation literacy, educational trajectory, future science teachers, life safety, labour protection, civil safety

Abstract

The article highlights the results of the study and substantiation of the possibilities of using artificial intelligence (AI) to create personalised learning trajectories for the formation of radiation literacy for different groups of the population. Attention is focused on the growing role of radiation literacy in the modern world, which is determined by the use of radiation technologies. The necessity of transition from traditional methods of forming the ability to think critically, analyse information and make informed decisions on radiation safety in different segments of the population is emphasised.

The article presents the results of the analysis and synthesis of scientific works on the use of AI in the educational process, in particular, for the personalisation of the educational process. The key trends in the development of digital educational environments and the possibilities of using AI to adapt educational material to the individual needs and level of training of students are identified.

The article presents the experimental and empirical stage of the study, which aimed to investigate the effectiveness of different concepts of using AI to create personalised learning paths for the formation of radiation literacy. The following population groups were involved as participants: specialists working with radiation-hazardous equipment (X-ray equipment), future science teachers, and people living near nuclear power plants. The effectiveness of different AI approaches was analysed and compared by the criteria of compliance with the control materials and the quality of wording and comprehensibility of the generated educational information for the participants of the experiment.

It is proved that modern AI tools are effective tools for improving radiation education and have significant potential for personalised radiation literacy. It is established that AI is able to adapt to the educational needs of different population groups by providing objective and scientifically based information. It is noted that different approaches to the use of AI, such as the ‘assistant’ model, adaptation of educational content and personalised recommendations, differ in their effectiveness. The results of the empirical data processing show that the approach of ‘personalised recommendations’ has demonstrated the highest efficiency.

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