Developing prompt engineering skills in the pre-service training of foreign language educator

Importance. The integration of artificial intelligence technologies into the educational process is one of the key areas of digital transformation of education in Russia. In the context of the rapid development of AI technologies, there is an urgent need for pre-service educators to develop prompt engineering skills that allow them to formulate requests for generative AI to solve specific methodological problems. The formulation of high-quality prompt makes it possible to improve the quality of feedback from generative AI and optimize the processes of pedagogical planning, the development of educational and control materials, the adaptation of the learning content to the individual characteristics of students, etc. Ignoring this aspect of pre-service educators training now can lead to a professional backlog of graduates and their lack of competitiveness in the labor market in the future. In this regard, prompt engineering training should be considered an imperative of modern education. The purpose of this study is to identify the effectiveness of the prompt engineering skills development methodology for pre-service foreign language educators.Research Methods. Theoretical methods were used such as the study of scientific and methodological literature on research issues, analysis, generalization and classification of information. In order to test the effectiveness of the proposed methodology, an experimental training was conducted aimed at developing the skills of prompt engineering among pre-service foreign language educators. 52 students of the 1st-4th courses of the Institute of Pedagogy, studying bachelor’s degrees in the fields of “Pedagogical Education (English Language profile)” and “Linguistics (Theory and Methodology of Teaching Foreign Languages and Cultures" profile)” at Derzhavin Tambov State University, took part in the pilot training. The object of control was the nomenclature of prompt engineering skills of a foreign language educator, represented by ten skills reflecting the specifics of teaching a foreign language.Definition of Concepts. The main concept in the study is prompt engineering or prompting. The paper describes in detail the basic and advanced prompting techniques aimed at obtaining highquality feedback from generative AI.Results and Discussion. The methodology for the developing prompt engineering skills in the pre-service training of foreign language educators has been tested during experimental training. Obvious progress is observed in the following controlled parameters: the skill to formulate prompt for organizing speech communication in a foreign language (t = 9.8 at p < 0.001), the skill to formulate prompt in order to find the necessary information, translate or explain complex educational material (t = 6.2 at p < 0.001), the skill to formulate prompt for developing a plan or lesson fragment (t = 10.1 at p < 0.001), the skill to formulate suggestions for developing training exercises for developing lexical and grammatical skills (t = 7.3 at p < 0.001), the skill to formulate prompt for text generation (t = 5.5 at p < 0.001), the skill to formulate prompt for text adaptation (t = 5.8 at p < 0.001). The following parameters remained without significant progress: the skill to formulate prompt for creating technological lesson maps (t = 7.3 at p > 0.05), the skill to formulate prompt for developing control and measuring materials (t = 1.1 at p > 0.05), the skill to formulate prompt for conducting a comparative analysis of two or more texts (t = 0.9 at p > 0.05), the skill to formulate suggestions for evaluating written creative work (t = 0.6 at p > 0.05).Conclusion. Prompt engineering plays a significant role in the system of linguistic and methodological training of pre-service foreign language educators based on AI technologies, as it allows them to master modern techniques of interaction with generative AI. For junior students, it is advisable to focus on the basic skills of interacting with generative AI, and for senior students – on solving specific methodological tasks. Prompt engineering training should be continuous and start from the first year, integrating into the learning process through individual disciplines, for example, through “Introduction to Artificial Intelligence” or minors, so that senior students can apply their knowledge to solve more complex cognitive tasks. The technique of autoprompting allows students to visually study the anatomy of high-quality prompt and at the same time develop critical thinking by analyzing and refining AI-generated prompt. The perspective of the study is to observe the effectiveness of the autoprompting technique in the pre-service training of foreign language educators. The results obtained can be used in further research on the study of prompt engineering techniques for educators or students of pedagogical training areas, for the development of author's methods of teaching prompt engineering to educators, as well as in the methodology of teaching a foreign language.

prompt engineering, prompting, AI technologies, AI competence, linguistic and methodological pre-service training of a foreign language educator