Application of generative AI technologies in adult foreign language learning: peer mentoring
Year: 2025
Type: article
Abstract: Importance. The shift towards a post-industrial society, coupled with the economy and social life digitalisation, has precipitated substantial changes in the labour market. This has engendered a crisis in higher education, marked by waning interest in higher education that does not align with the needs of employers. One potential solution to this predicament lies in the integration of knowledge-intensive technologies into the educational process. The aim of the research is to describe the experience of implementing the pedagogical technology ‘peer mentoring’ in the process of teaching English for Specific Purposes to humanities students with the use of AI-technologies and to analyze the results obtained. Materials and Methods. The following scientific methods are employed: experiment, observation, formalized questionnaire, comparative analysis, expert evaluation, self-assessment, statistical methods. Results and Discussion. The impact of peer mentoring in ESP training with the use of AI technologies on the communicative and digital competencies development has been demonstrated. At the conclusion of the experiment, a statistically significant increase in the communicative (Δ х̅ = +0.94 points, ΔМо = +1 point) and digital (Δ х̅ = +1 point, ΔМо = +1 point) competences formation level is revealed. Conclusion. Peer mentoring technology can to a certain extent mitigate the lack of preparedness among foreign language teachers to incorporate AI into the teaching process. There is an acknowledged necessity for two key areas of focus: the retraining of university teachers in the domain of AI technologies in education, and the development of concepts for a university of the future that is integrated with AI. This will facilitate the cultivation of a set of professional and universal competencies that are in demand by the rapidly evolving labour market.
Author: I. E. Abramova
Institution: Petrozavodsk State University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: diamond
OpenAlex ID: W4409679000