Science for Education Today, 2023, vol. 13, no. 4, pp. 170–194
© Ivanchenko I. S., 2023
UDC: 
332.02+316.6+378

Assessing the prospects for using artificial intelligence in higher education system

Ivanchenko I. S. 1 (Rostov-on-Don, Russian Federation)
1 Rostov State University of Economics
Full text (Open Access) — Downloaded 590
Abstract: 

Introduction. The article deals with the problem of improving the quality of higher education in the context of its computerization. The purpose of the article is to describe the new structure of higher education, based on the principle of a neural network, as well as to identify the prospects of digital transformation for universities, when a wide range of administrative and educational functions might be performed by artificial intelligence.
Materials and Methods. The study uses structural modeling in order to build a higher education system that functions as a neural network based on theoretical analysis and reviewing of scholarly literature on the methodology of teaching in high-ranking foreign universities. The author also employs the UTAUT (Unified theory of acceptance and use of technology) model to identify students’ attitudes towards the prospects for the introduction of artificial intelligence in higher education.
Results. The paper proposes and describes a new intellectual structure of the higher education system. A distinctive feature of this structure is that employers should become the main evaluators of graduates’ education outcomes. Employers’ feedback is supposed to be provided for universities, adjusting the higher education system to continuously changing market requirements. The advantage of transforming the higher education system according to the principles of neural network functioning will bring a considerable increase in the quality of preparing top-level professionals, and therefore, real prospects for restructuring the national economy will be provided, when GDP growth is ensured not by increasing the amount of exporting raw materials, but by high-tech production. The results of students’ survey conducted and processed using the UTAUT model showed that the younger generation has a positive attitude towards the introduction of AI in the educational process: they are attracted by new prospects in obtaining knowledge and are not afraid of the risks associated with it.
Conclusions. The paper concludes that Russian universities, by switching to the new model of higher education, based on a neural network, will be able to dramatically improve the quality of education and become world leaders in the field of preparing top-level professionals, as currently in foreign universities, artificial intelligence manages only a limited range of functions. A distinctive feature of the proposed model is complete digitalization and automation of all routine work at universities, decreasing methodological and reporting load for academic staff, as well as transferring the main teaching load from classrooms to laboratories for a deeper students’ involvement in research activities.

Keywords: 

Neural networks; Personalization of learning; Universities of the future; Globalization of education; Quality of labor resources; Economic growth.

For citation:
Ivanchenko I. S. Assessing the prospects for using artificial intelligence in higher education system. Science for Education Today, 2023, vol. 13, no. 4, pp. 170–194. DOI: http://dx.doi.org/10.15293/2658-6762.2304.08
References: 
  1. Aldosari S. The future of higher education in the light of artificial intelligence transformations. International Journal of Higher Education, 2020, vol. 9 (3), pp. 145–151. DOI: https://doi.org/10.5430/ijhe.v9n3p145
  2. Aoun J. E. Robot-Proof: Higher Education in the Age of Artificial Intelligence. Massachusetts Institute of Technology. The MIT Press Cambridge, Massachusetts London, England, 2017, 210 p. ISBN: 978-0-262-03728-0 URL: http://repo.darmajaya.ac.id/5335/1/Robot-Proof_%20Higher%20Education%20in%20the%20Age%20of%20Artificial%20Intelligence%20%28%20PDFDrive%20%29.pdf
  3. Bates T., Cobo C., Marino O., Wheeler S. Can artificial intelligence transform higher education? International Journal of Educational Technology in Higher Education, 2020, vol. 17, pp. 42. DOI: https://doi.org/10.1186/s41239-020-00218-x
  4. Bhise A., Munsh A., Rodrigues A., Sawant V. Overview of AI in education. Artificial Intelligence in Higher Education. Taylor & Francis Group, LLC. London, 2023, 267 p. https://doi.org/10.1201/9781003184157 URL: https://www.taylorfrancis.com/chapters/edit/10.1201/9781003184157-2/overview-ai-education-archana-bhise-ami-munshi-anjana-rodrigues-vidya-sawant
  5. Chatterjee S., Bhattacharjee K. K. Adoption of artificial intelligence in higher education: A quantitative analysis using structural equation modeling. Education and Information Technologies, 2020, vol. 25, pp. 3443–3463. DOI:  https://doi.org/10.1007/s10639-020-10159-7
  6. Chrisinger D. The solution lies in education: Artificial intelligence & the skills gap. On the Horizon, 2019, vol. 27 (1), pp. 1–4. DOI: https://doi.org/10.1108/OTH-03-2019-096 URL: https://www.emerald.com/insight/content/doi/10.1108/OTH-03-2019-096/full/html
  7. Christie M., de Graaff E. The philosophical and pedagogical underpinnings of active learning in engineering education. European Journal of Engineering Education, 2017, vol. 42 (1), pp. 5–16. DOI: https://doi.org/10.1080/03043797.2016.1254160
  8. Cox A., Pinfield S., Rutter S. The intelligent library: Thought leaders’ views on the likely impact of artificial intelligence on academic libraries. Library Hi Tech, 2019, vol. 37 (3), pp. 418–435. DOI: https://doi.org/10.1108/LHT-08-2018-0105 URL: https://www.emerald.com/insight/content/doi/10.1108/LHT-08-2018-0105/full/html
  9. Croxford L., Raffe D. The iron law of hierarchy? Institutional differentiation in UK higher education. Studies in Higher Education, 2015, vol. 40 (9), pp. 1625–1640. DOI: https://doi.org/10.1080/03075079.2014.899342

10. Dhrifi A., Alnahdi S., Jaziri R. The causal links among economic growth, education and health: Evidence from developed and developing countries. Journal of the Knowledge Economy, 2021, vol. 12, pp. 1477–1493. DOI: https://doi.org/10.1007/s13132-020-00678-6

11. Lee J.-M., Lee B., Rha J.-Y. Determinants of mobile payment usage and the moderating effect of gender: Extending the UTAUT model with privacy risk. International Journal of Electronic Commerce Studies, 2019, vol. 10 (1), pp. 43–64. DOI: http://dx.doi.org/10.7903/ijecs.1644

12. Lemaignan S., Warnier M., Sisbot E. A., Clodic A., Alami R. Artificial cognition for social human-robot interaction: An implementation. Artificial Intelligence, 2017, vol. 247, pp. 45–69. DOI: https://doi.org/10.1016/j.artint.2016.07.002

13. Liarokapis F., Mourkoussis N., White M., Darcy J., Sidniotis M., Petridis P., Basu A., Lister P. F. Web3D and augmented reality to support engineering education. World Transactions on Engineering and Technology Education, 2004, vol. 3 (1), pp. 11–14. DOI: http://www.wiete.com.au/journals/WTE&TE/Pages/Vol.3,%20No.1%20(2004)/04_Liarokapis.pdf

14. Maneejuk P., Yamaka W. The impact of higher education on economic growth in ASEAN-5 countries. Sustainability, 2021, vol. 13 (2), pp. 520. DOI:  https://doi.org/10.3390/su13020520

15. Mehlig B. Machine learning with neural networks. Göteborg, Sweden, 2021, 240 p. URL:  https://arxiv.org/pdf/1901.05639.pdf

16. Narai Y. N. 21 Lessons for the 21st century. New York, NY: Random House, 2018, 318 p. URL: https://amauroboliveira.files.wordpress.com/2020/10/21-lessons-for-the-21st-century-1.pdf

17. Pastor J. M., Peraita C., Serranoa L., Soler A. Higher education institutions, economic growth and GDP per capita in European Union countries. European Planning Studies, 2018, vol. 26 (8), pp. 1616–1637. DOI: https://doi.org/10.1080/09654313.2018.1480707

18. Pence H. Artificial intelligence in higher education: New wine in old wineskins? Journal of Educational Technology Systems, 2019, vol. 48 (1), pp. 5–13. DOI: https://doi.org/10.1177/0047239519865577

19. Popenici S. Artificial intelligence and learning futures: Critical Narratives of Technology and Imagination in Higher Education. New York, 2022, 228 p. DOI: https://doi.org/10.4324/9781003266563

20. Popenici S., Kerr S. Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning, 2017, vol. 12, pp. 22. DOI: https://doi.org/10.1186/s41039-017-0062-8

21. Rybinski K., Kopciuszewska E. Will artificial intelligence revolutionise the student evaluation of teaching? A big data study of 1.6 million student reviews. Assessment and Evaluation in Higher Education, 2021, vol. 46 (7), pp. 1127–1139. DOI: https://doi.org/10.1080/02602938.2020.1844866 

22. Schubert T., Kroll H. Universities’ effects on regional GDP and unemployment: The case of Germany. Papers in Regional Science, 2016, vol. 95 (3), pp. 467–489. DOI: https://doi.org/10.1111/pirs.12150

23. Sethi K., Jaiswal V., MohdDilshad A. Machine learning based support system for students to select stream (subject). Recent Advances in Computer Science and Communications, 2020, vol. 13 (3), pp. 336–344. DOI: https://doi.org/10.2174/2213275912666181128120527 

24. Sfakianakis G., Magoutas A. I., Georgopoulos D. An empirical analysis of differences in GDP per capita and the role of human capital. Industry & Higher Education, 2010, vol. 24 (2), pp. 101–107. DOI: https://doi.org/10.5367/000000010791191047

25. Smunty P., Schreiberova P. Chatbots for learning: A review of educational chatbots for the Facebook Messenger. Computers and Education, 2020, vol. 151, pp. 103862. DOI: https://doi.org/10.1016/j.compedu.2020.103862

26. Takii K., Tanaka R. Does the diversity of human capital increase GDP? A comparison of education systems. Journal of Public Economics, 2009, vol. 93 (7–8), pp. 998–1007. DOI: https://doi.org/10.1016/j.jpubeco.2009.04.007 

27. Venkatesh V., Morris M., Davis G., Davis F. User acceptance of information technology: Toward a unified view. MIS Quarterly, 2003,vol. 27 (3), pp. 425–478. DOI: https://doi.org/10.2307/30036540

28. Venkatesh V., Thong J. Y., Xu X. Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 2012, vol. 36 (1), pp. 157–178. DOI: https://doi.org/10.2307/41410412

 

29. Venkatesh V., Thong J. Y. L., Xu X. Unified theory of acceptance and use of technology: A synthesis and the road ahead. Journal of the Association for Information Systems, 2016, vol. 17 (5), pp. 328–376. DOI: https://doi.org/10.17705/1jais.00428

30. Wei M.-F., Luh Y.-H., Huang Y.-H, Chang Y.-C. Young generation’s mobile payment adoption behavior: Analysis based on an extended UTAUT model. Journal of Theoretical and Applied Electronic Commerce Research, 2021, vol. 16 (4), pp. 618–637. DOI: https://doi.org/10.3390/jtaer16040037

31. Wheeler S. Digital learning in organizations. London: Kogan Page, 2019, 272 p. URL: https://www.goodreads.com/book/show/41110265-digital-learning-in-organizations

32. Zawacki-Richter O., Marín V., Bond M., Gouverneur F. Systematic review of research on artificial intelligence applications in higher education – where are the educators? International Journal of Educational Technology in Higher Education, 2019, vol. 16, pp. 39. DOI: https://doi.org/10.1186/s41239-019-0171-0

33. Ivanchenko I. S. The impact of the money market on economic growth. Finance and Credit, 2012, no. 15, pp. 11–19. (In Russian) URL: https://elibrary.ru/item.asp?id=17663256  

34. Kolosnicyna M. G., Ermolina U. E. Public spending on education and economic growth: Cross-country analysis. Questions of Statistics, 2021, vol. 28 (3), pp. 70–85. (In Russian) DOI: https://doi.org/10.34023/2313-6383-2021-28-3-70-85 URL: https://elibrary.ru/item.asp?id=46227157

35. Radugin A. A., Radugina O. A. Application of artificial intelligence in the educational process of the university: Technology, potential and problems. Bulletin of the Voronezh State University, Problems of Higher Education, 2021, no. 4, pp. 84–87. (In Russian) URL: https://elibrary.ru/item.asp?id=47436781

Date of the publication 31.08.2023