Training of future natural science teachers for developing students’ skills in applying artificial intelligence in the educational process

Abstract

The article substantiates the theoretical and methodological principles of training future teachers of natural sciences to form students' skills in applying artificial intelligence in the educational process. The relevance of the problem is determined by the contradiction between the intensive introduction of AI tools into educational activities and the insufficient theoretical and methodological certainty of the corresponding training of pedagogical personnel. The content of the training is structured in three interrelated blocks: cognitive, which involves the acquisition of knowledge about the functional capabilities and typical limitations of generative AI; operational and activity, aimed at forming the skills of designing educational situations using AI and organizing the verification of the obtained results; value-ethical, focused on ensuring academic integrity, transparency and the development of AI literacy of students. The pedagogical conditions for the effectiveness of such training are determined: the integration of AI in the context of STREAM education, the targeted development of the digital competence of the future teacher, and the normative and methodological regulation of AI application practices. The formation of AI application skills in students is conceptualized as a staged process that reproduces the logic of scientific knowledge and includes orientation-adaptive, verification-critical, research-project, and communicative-reflective stages. Methodological recommendations are formulated for the design of learning outcomes, the implementation of standardized procedures for verifying AI responses, the use of diagnostic and correction tasks, the construction of evaluation criteria for «AI-supported» educational products, and the introduction of AI use declaration practices. It is substantiated that the most productive model is one in which AI functions as a tool to support educational and research activities, while problem formulation, critical argumentation, and formulation of conclusions based on data remain the prerogative of the student.

Keywords: artificial intelligence; training of future teachers; natural sciences; skills in using AI; digital competence; AI literacy; academic integrity; STREAM education; educational process.