Science for Education Today, 2024, vol. 14, no. 3, pp. 113–134
UDC: 
330.322 +004.81+378+371.263

Study of the future economists’ readiness to use artificial intelligence based on the hierarchy analysis method

Kormiltseva E. A. 1 (Moscow, Russian Federation), Baygusheva I. A. 2 (Astrakhan, Russian Federation), Varova N. L. 3 (Omsk, Russian Federation), Starikov V. I. 4 (Omsk, Russian Federation), Shmakova A. P. 1 (Moscow, Russian Federation), Burmistrova N. A. 1 (Moscow, Russian Federation)
1 Financial University under the Government of the Russian Federation
2 Astrakhan Tatishchev State University
3 Omsk State Pedagogical University
4 Omsk State Technical University
Abstract: 

Introduction. The article examines the problem of increasing the level of digital literacy of future economists in the context of interaction with artificial intelligence systems. The purpose of the study is to assess the readiness of future economists to use artificial intelligence in the context of digital prospects of the socio-economic space.
Materials and Methods. The methodological basis of the study is the sustainable development strategy, which identifies educational opportunities, resources and technologies as a global driving force for achieving the Sustainable Development Goals. The main research method is T. Saaty’s Analytical Hierarchical Process method, which enables to build a flexible hierarchical model for studying the dynamics of future economists’ readiness to use artificial intelligence in the context of developing digital literacy. The authors conducted an online survey of students from the Financial University under the Government of the Russian Federation, Astrakhan State University, Omsk State Technical University, and schoolchildren from economics classes (Gymnasium 19, Omsk) to assess the future economists’ readiness to use artificial intelligence in the context of the transition to a digital economy.
Results. The authors identified gender differences in respondents’ preferences in the use of artificial intelligence: female participants show higher results than the general sample, when choosing one answer about the purposes of personal use of artificial intelligence, and lower, when choosing a combination of answers, male participants show the opposite results, which is explained by the attitude towards risk in terms of gender stereotypes of behavior. When comparing the choice of respondents’ answers for different age groups (16-20 years old), no age peculiarities were discovered, as well as the ambiguous nature of the influence of artificial intelligence on the younger generation of future economists in terms of a decrease in the natural intelligence of young people against the backdrop of digitalization reaching all spheres of life. A high level of awareness of students and schoolchildren regarding the prospects and problem areas of using artificial intelligence in the field of economics and finance was also revealed.
Conclusions. The study concludes that future economists are actively interacting with artificial intelligence systems, promoting the formation of digital literacy in the interests of sustainable development.

Keywords: 

Sustainable development; Economic education; Future economists; Digital economy; Digital literacy; Artificial intelligence; Online survey; Saaty’s Analytical Hierarchical Process method.

For citation:
Kormiltseva E. A., Baygusheva I. A., Varova N. L., Starikov V. I., Shmakova A. P., Burmistrova N. A. Study of the future economists’ readiness to use artificial intelligence based on the hierarchy analysis method. Science for Education Today, 2024, vol. 14, no. 3, pp. 113–134. DOI: http://dx.doi.org/10.15293/2658-6762.2403.06
References: 
  1. Awad E., Dsouza S., Kim R., Schulz J., Henrich J., Shariff A., Bonnefon J.-F., Rahwan I. The moral machine experiment. Nature, 2018, vol. 563 (7729), рp. 59–64. DOI: https://doi.org/10.1038/s41586-018-0637-6
  2. Alekseev A. N., Lobova S. V., Bogoviz A. V. Digitalization and quality of labor: Contradictions in developing countries and the prospects of harmonization. International Journal of Quality and Reliability Management, 2021, vol. 15 (3), pp. 733–752. DOI: https://doi.org/10.24874/IJQR15.03-04  URL: https://www.elibrary.ru/item.asp?id=47041276  
  3. Avelar A. B. A., Oliveira K. D. D., Farina M. C. The integration of the sustainable development goals into curricula, research and partnerships in higher education. International Review of Education, 2023, vol. 69 (3), pp. 299–325. DOI: https://doi.org/10.1007/s11159-023-10013-1
  4. Bickley S. J., Macintyre A., Torgler B. Artificial intelligence and big data in sustainable entrepreneurship. SSRN Electronic Journal, 2024. DOI: https://doi.org/10.2139/ssrn.4686881
  5. Bickley S. J., Chan H. F., Torgler B. Artificial intelligence in the field of economics. Scientometrics, 2022, vol. 127 (4), pp. 2055–2084. DOI: https://doi.org/10.1007/s11192-022-04294-w
  6. Farias-Gaytan S., Aguaded I., Ramirez-Montoya M. S. Digital transformation and digital literacy in the context of complexity within higher education institutions: A systematic literature review. Humanities and Social Sciences Communications, 2023, vol. 10 (1), pp. 386. DOI: https://doi.org/10.1057/s41599-023-01875-9
  7. Frolova E. V., Rogach O. V., Kuleshov S. M., Shikhgafizov P. S. Digitalization of higher education: New trends and the factors that are associated students' grades. European Journal of Contemporary Education, 2022, vol. 11 (1), pp. 59–69. DOI: https://doi.org/10.13187/ejced.2022.59  URL: https://www.elibrary.ru/item.asp?id=49243983  
  8. Getenet S., Cantle R., Redmond P., Albion P. Students' digital technology attitude, literacy and self-efficacy and their effect on online learning engagement. International Journal of Educational Technology in Higher Education, 2024, vol. 21 (1). DOI: https://doi.org/10.1186/s41239-023-00437-y
  9. Grosu V., Kosmulets K. G., Sokolyuk M., Chubotariu M.-S., Mikhaila S. Testing accountants' ideas about profession digitalization and profiling the future professional. Technological Forecasting and Social Chang, 2023, vol. 193, pp. 122630. DOI: https://doi.org/10.1016/j.techfore.2023.122630

10.Kobrinskii B. A. Artificial intelligence: Problems, solutions, and prospects. Pattern Recognition and Image Analysis, 2023, vol. 33 (3), pp. 217–220. DOI: https://doi.org/10.1134/S1054661823030203  

11.Lau J., Bonilla J. L., Gárate A. Artificial intelligence and labor: Media and information competencies opportunities for higher education. Information Literacy in Everyday Life, 2019, pp. 619–628. DOI: https://doi.org//10.1007/978-3-030-13472-3_58  URL: https://www.webofscience.com/wos/woscc/full-record/WOS:000617912500058

12.Owan V. J., Abang K. B., Idika D. O., Etta E. O., Bassey B. A. Exploring the potential of artificial intelligence tools in educational measurement and assessment. Eurasia Journal of Mathematics, Science and Technology Education, 2023, vol. 19 (8), pp. em2307. DOI: https://doi.org/10.29333/ejmste/13428 

13.Ranjbar M., Effati S. Group decision making in the analytic hierarchy process by hesitant fuzzy numbers. Scientific Reports, 2023, vol. 13 (1), pp. 21864. DOI: https://doi.org/10.1038/s41598-023-49076-3

14.Ronzhina N., Kondyurina I., Voronina A., Igishev K., Loginova N. Digitalization of modern education: Problems and solutions. International Journal of Emerging Technologies in Learning, 2021, vol. 16 (4), pp. 122. DOI: https://doi.org/10.3991/ijet.v16i04.18203 URL: https://elibrary.ru/item.asp?id=46807178

15.Xu S., Yeyao T., Shabaz M. Multi-criteria decision making for determining best teaching method using fuzzy analytical hierarchy process. Soft Computing in Decision Making and in Modeling in Economics, 2023, vol. 27 (6), pp. 2795–2807. DOI: https://doi.org/10.1007/s00500-022-07554-2

16.Abramova I. E., Shishmolina E. P. Teaching a foreign language to the students of humanities: Academic and digital literacies. Bulletin of the Moscow State Regional University. Series: Pedagogy, 2022, no. 3, pp. 113–126. (In Russian) DOI: https://doi.org/10.18384/2310-7219-2022-3-113-126 URL: https://www.elibrary.ru/item.asp?id=49753787 

17.Vinichenko M. V., Nikiporets-Takigawa G. Yu., Ljapunova N. V., Chulanova O. L., Karacsony P. The nature of the influence of digitalization and artificial intelligence on the sociocultural environment and education in the conditions of the pandemic: Views of students of generation Z Russia and Slovakia. Perspectives of Science and Education, 2021, no. 3, pp. 26–42. (In Russian) DOI: https://doi.org/10.32744/pse.2021.3.2  URL: https://www.elibrary.ru/item.asp?id=46424166 

18.Dobrinskaya D. E. What is the digital society? Sociology of Science and Technology, 2021, vol. 12 (2), pp. 112–129. (In Russian) URL: https://www.elibrary.ru/item.asp?id=47110400 

19.Dukhanina L. N., Maximenko A. A. Problems of the implementation of artificial intelligence in education. Perspectives of Science and Education, 2020, no. 4, pp. 23–35. (In Russian) DOI: https://doi.org/10.32744/pse.2020.4.2  URL: https://www.elibrary.ru/item.asp?id=43917941

20.Ivanchenko I. S. Assessing the prospects for using artificial intelligence in higher education system. Science for Education Today, 2023, vol. 13 (4), pp. 170–194. (In Russian) DOI: https://doi.org/10.15293/2658-6762.2304.08  URL: https://www.elibrary.ru/item.asp?id=54390178

Date of the publication 30.06.2024