An Ecosystem for Teacher Training Based on Artificial Intelligence Technologies

Introduction. Artificial intelligence (AI) technologies are becoming increasingly integrated into the training of future specialists,
including teachers. Currently, many AI tools are used by educators in teaching specialized subjects, teaching internship and their
research. However, despite their objective didactic potential, many AI-based technological solutions have significant limitations
and shortcomings, ranging from hallucinations to the inability to develop specific types of students’ professional competencies.
All of this underscores the relevance of a combination of traditional and innovative (AI) tools in student education and a broader
ecosystem perspective on all participants in the educational process, their interactions, and mutual influences, in order to
improve the quality of specialist training. The aim of the study was to develop an educational ecosystem for training future foreign
language teachers based on AI technologies and to identify the attitudes of modern teachers toward the inclusion of specific
components in this educational ecosystem.
Materials and Methods. The research methods included literature analysis, teacher surveys, educational ecosystem modeling,
experience summaries and observation. The analysis included research articles and reviews published in academic journals
indexed in the Web of Science (Core Collection) and Scopus (Q1, Q2). Participants in the online survey included 23 teachers
from Derzhavin Tambov State University (Russian Federation).
Research Results. An educational ecosystem for training future teachers based on AI technologies was developed, comprising
two blocks – core and peripheral – and corresponding components. A gap in teachers' perceptions of the importance of
ecosystem components was identified and potential risks to its sustainability were highlighted. The survey results revealed the
extent to which faculty understand the inclusion of a number of components in the educational ecosystem: core: students – 100%,
faculty – 100%, content-specific core – 100%, AI-based technological solutions – 100%, digital educational environment – 87%;
peripheral: employers – 65.3%, regulatory framework – 21.7%, AI-based software developers – 17.4%, student initiative group –
4.4%, AI Lab – 0%, faculty methodological association – 0%, scientific and methodological community – 0%. These results are
explained by respondents' primary focus on key components of the educational process and solving specific educational issues,
a lack of vision for the possibility of interacting with software developers and understanding the methodological value of such
collaboration, and a lack of ecosystem thinking.
Conclusion. The novelty of this study lies in the development of an educational ecosystem for training future foreign language
teachers based on AI technologies. This ecosystem can be used to develop private educational ecosystems for training
specialists in specific fields. Depending on the specialization, the ecosystem's components can be flexibly modified.