UDC: 
004.8
Schreiner Boris Aleksandrovich
Кандидат психологических наук, Cand. Sci. (Psychol.), Head of Laboratory “Artificial Intelligence Technologies”, Assoc. Prof. of the Department of Information Systems and Digital Education, Novosibirsk State Pedagogical University, boris.shrayner@gmail.com, Novosibirsk
Schrayner Аleksandr А.
Кандидат педагогических наук, Cand. Sci. (Pedag.), Assoc. Prof. of the Department of Pedagogy and Methodology of Primary Education, Institute of Childhood, Novosibirsk State Pedagogical University, aschr@inbox.ru, Novosibirsk

Guidance for schoolchildren on artificial intelligence professions

Abstract: 
The article actualizes the problem of professional orientation of schoolchildren in professions related to artificial intelligence. At the present time there is a need of society for specialists who could solve problems both on development of artificial intelligence systems, and on operation of already existing ones. The shortage of such specialists is a big problem. The aim of the article is to justify the content of career guidance activities in secondary schools in information technology (IT) professions in general and artificial intelligence (AI) in particular. Methodologically, our study was based on a theoretical analysis of the literature on career guidance for students. In the course of the study, the results of surveys of students were analyzed, documents were analyzed, and observations were made. Based on the analysis of career guidance resources and comparison with real job vacancies the specifics, specification of professions related to AI were considered. Activity-based and project-based learning methods were used in the career guidance activities. Google Klass was used as a learning platform and Google Colab as a development environment. Results. The article presents the content and the results of the career guidance master classes carried out by the authors with the schoolchildren. The results of the input and final surveys (questionnaires) of the students are analysed. Three main professions from Artificial Intelligence and their competences for the initial introduction are highlighted. Analysis of key skills of AI professions is carried out, groups of skills for career guidance are highlighted. Some essential content aspects that can be used in designing different types of career guidance sessions are suggested. In conclusion, it is concluded that the developed and tested materials contribute to professional self-determination of schoolchildren. They can be used for different career guidance activities on artificial intelligence professions.
Keywords: 
career guidance; professions; artificial intelligence; AI; python; developer; data-science; machine learning; job interview; vacancy
References: 

1. Kuzmina, S. R., 2022. Professional Demand and Vocational Guidance for Modern Schoolchildren. Young Scientist, no. 5, pp. 35–38. (In Russ.)
2. Panina, S. V., Kvashina, S., 2019. Junior skills movement as an innovative form of early career guidance for schoolchildren. The junior movement skills as an innovative form of early vocational guidance of pupils, no. 64 (3), pp. 203–206.
(In Russ.)
3. Khenner, E. K., 2021. Pedagogical support for professional self-determination of high school students in IT professions. The Education and Science Journal, no. 23 (8), pp. 37–60. (In Russ.)
4. Semakin, I. G., Yasnitsky, L. N., 2022. Artificial Intelligence and School Computer Science Course. Informatics and Education, no. 37 (3), pp. 12–20. (In Russ.)
5. Polipovich, S. A., Shrajner, B. A., Chikova, O. A., 2021. Formation of motivation while teaching students the basics of artificial intelligence. Pedagogical Informatics, no. 3, pp. 25–33. (In Russ.)
6. Fominykh, I. A., Dovranov, A. R., 2022. About the development of the course of choice “Basics of programming in Python” for pre-profile training of schoolchildren. Informatics in school, no. 1, pp. 22–29. (In Russ.)
7. Bogdanova, A. N., 2021. The elective course “Basics of artificial intelligence” for high school students. Informatics in school, no. 7, pp. 27–33. (In Russ.)
8. Rozov, K. V., Shrayner, B. A., 2022. Distance learning of schoolchildren in artificial intelligence technologies. Informatics in school, no. 6, pp. 37–43. (In Russ.)
9. Tkach, T. V., 2020. Machine learning and big data processing in a modern school. Informatics in school, no. 1 (7), pp. 25–29. (In Russ.)
10. Zubrilin, A. A., Pronchatova, A. S., Zubrilin, M. S., 2020. Network technologies in the study of databases at school. Informatics and Education, no. 158 (5), pp. 32–39. (In Russ.)
11. Samylkina, N. N., 2020. The structure and the content of digital competencies formed in pre-vocational training. Informatics and Education, no. 157 (4), pp. 11–19. (In Russ.)
12. Levchenko, I. V., 2020. Content of teaching the elements of artificial intelligence in a school informatics course. Informatics and Education, no. 157 (4), pp. 3–10. (In Russ.)
13. Zubrilin, A. A., Pronchatova, A. S., Zubrilin, M. S., 2020. Career guidance competence of an informatics teacher in the conditions of professional self-determination of schoolchildren. Informatics in school, no. 156 (3), pp. 3–7. (In Russ.)
14. Bosova, L. L., Bosova, A. Y., 2021. About the professional activity of an informatics teacher in the context of digital transformation of education. Informatics in school, no. 7, pp. 10–14. (In Russ.)
15. Ilyukhina, N. A., 2016. Universities career-orientational work for secondary school students. New opportunities for traditional forms. RSUH/RGGU bulletin, no. 6 (4), pp. 83–88. (In Russ.)
16. Grigoriev, S. G., Kalinin, I. A., Samylkina, N. N., 2022. The task system for the first All-Russian Olympiad in artificial intelligence for schoolchildren. Informatics and Education, no. 37 (3), pp. 12–20. (In Russ.)
17. Zuyeva, T. V., Nyssanov, A. T., 2021. Career guidance of adolescents in their sociocultural development and modern technologies. Psychologie Francaise, no. 67 (1), pp. 31–47. (In Еng.)
18. Jae Young, C., Sunbok, L., 2019. Dropout early warning systems for high school students using machine learning. Children and Youth Services Review, no. 96, pp. 346–353. (In Еng.)
19. Kiselev, P., Kiselev, B., Matsuta, V., 2020. Career guidance based on machine learning: Social networks in professional identity construction. Procedia Computer Science, pp. 158–163. (In Еng.)
20. Fishman, E, Weisberg, E., Chu, L., Rowe, S., Young, J., 2020. Mapping Your Career in the Era of Artificial Intelligence: It’s Up to You, Not Google. Journal of the American College of Radiology, no. 17 (11), pp. 1537–1538. (In Еng.)