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The use of artificial intelligence in the study process as a determinant of the education process in an agile university

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Purpose: The aim of the article is to examine the use of artificial intelligence (AI) in the study process and to assess how it affects the effectiveness of education in the context of agile universities. The key features of an agile university are presented and the ways in which AI technologies support the educational process are discussed. Design/methodology/approach: The research was conducted using a survey method on a sample of 450 students from various universities. Respondents answered questions about the use of AI tools in the learning process, such as translating complex issues, preparing reports, identifying errors, generating new ideas, and creating tests and questions. Findings: (mandatory) Studies have shown that students most often use AI to explain complex issues and generate new ideas. AI tools are also useful in identifying and correcting errors and creating abbreviations and summaries of long texts. AI tools have proven to be less effective in preparing reports and creating tests. Research limitations/implications: Research limitations include the use of the online survey method, which may have reduced the diversity of respondents and limited sample representativeness. Further research could extend the analysis to other forms of education and greater demographic diversity among students. Practical implications: The results of the research can be useful for universities that want to integrate AI technologies into their curricula. They can help identify the most effective applications of AI in education and develop strategies for implementing agile teaching methods. Social implications: The use of AI in education can contribute to improving the quality of education and better preparing students for the requirements of the modern labor market. AI technologies can also support the development of soft skills, such as creativity and problem-solving skills. Originality/value: The article provides a new perspective on the use of artificial intelligence in education, especially in the context of agile universities. It provides practical recommendations for educational institutions and emphasizes the importance of adaptive technologies in the education process.
Rocznik
Tom
Strony
333--347
Opis fizyczny
Bibliogr. 34 poz.
Twórcy
autor
  • University of Economics in Katowice
  • Lodz University of Technology, Faculty of Organization and Management
autor
  • University of Technology and Economics in Warsaw
Bibliografia
  • 1. Almahamid, S., Awwad, A., Adams, A. (2010). Effects of organizational agility knowledge sharing on competitive advantage: An empirical study in Jordan. International Journal of Management, 27(3).
  • 2. Anderson, S., Wilson, T. (2017). Decentralization and Organizational Agility: A Comparative Study. Journal of Strategic Management, (1)42.
  • 3. Bessant, J., Brown, S., Francis, D., Meredith, S., Kaplinsky, S. (1999). Developing manufacturing agility in SME’s. International Journal of Technology Management, 5.
  • 4. Bondos, I. (2014). Towards Marketing 3.0. Internal Trade, 14(3).
  • 5. Bray, D.A., Reeves, M., Levin, S., Harnoss, J.D., Ueda, D., Kane, G.C., Johnson, J.S., Billespie, D. (2019). Adapt and thrive: How can business leaders best understand the complex interplay between companies, economies, and societies? MIT Sloan Management Review, 4-5.
  • 6. Brown, K., Jones, L. (2018). The Impact of Decision-Making Speed on Organizational Agility. Journal of Applied Psychology, 2(123).
  • 7. Campbell, M., Hoane, A.J., Hsu, F.-H. (2002). Deep Blue. Artificial Intelligence, 134(1-2), 57-83. https://doi.org/10.1016/S0004-3702(01)00129-1
  • 8. Cappelli, P., Tavis, A. (2018). HR goes agile. Harvard Business Review, 3-4.
  • 9. Cegarra-Navarro, J.C., Sánchez-García, J.C., Marco-Lajara, B., García-Pérez, A. (2021). Building organizational resilience in the face of the COVID-19 pandemic: The role of strategic agility. Sustainability, 6(13). https://doi.org/10.3390/su13063054
  • 10. Chen, Y., Li, X. (2021). The Role of Organizational Agility in Managing the COVID-19 Pandemic: A Case Study of Two Chinese Hospitals. International Journal of Environmental Research and Public Health, 1(18). https://doi.org/10.3390/ijerph18010070
  • 11. Feigenbaum, E.A. (1981). The art of artificial intelligence: Themes and case studies of knowledge engineering. Proceedings of the 7th international joint conference on Artificial intelligence - Volume 1 (IJCAI’81), 1014-1029.
  • 12. Fosso Wamba, S. (2022). Impact of artificial intelligence assimilation on firm performance: The mediating effects of organizational agility and customer agility. International Journal of Information Management. https://doi.org/10.1016/j.ijinfomgt.2022.102544
  • 13. Goldman, S.L., Nagel, R.N., Preiss, K. (1995). Agile Competitors and Virtual Organizations: Strategies for Enriching the Customer. Van Nostrand Reinhold.
  • 14. He, H., Harris, L. (2021). The impact of organizational agility on crisis management and firm performance: A moderation analysis. Journal of Business Research, 122. https://doi.org/10.1016/j.jbusres.2020.11.026
  • 15. Kidd, P.T. (1994). Agile Manufacturing: Forging New Frontiers. Addison-Wesley.
  • 16. Kumkale, I. (2022). Organizational Agility. Springer Nature Singapore. http://dx.doi.org/10.1007/978-981-16-7582-9_3
  • 17. Kurnia, S., Chien, S.W. (2020). Building organizational agility through strategic management accounting: A case study of an Indonesian manufacturing company. Journal of Asia Business Studies, 4(14). https://doi.org/10.1108/JABS-09-2019-0253
  • 18. Leberecht, T. (2016). Strategy design. Bet on agility. Harvard Business Review Poland, 166-167.
  • 19. LeCun, Y., Bengio, Y., Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. https://doi.org/10.1038/nature14539
  • 20. Martucci, I., de Felice, A., Schirone, D. (2012). Knowledge Exchange between IKEA and Suppliers Through Social and Environmental Strategy. European Conference on Knowledge Management, Academic Conferences International Limited.
  • 21. McCarthy, J. (1960). Recursive functions of symbolic expressions and their computation by machine, Part I. Communications of the ACM, 3(4), 184-195. https://doi.org/10.1145/367177.367199
  • 22. McNamee, R.C., Schoch, N., Oelschlaeger, P., Huskey, L. (2012). Collaboration Continuum Cultural and Technological Enablers of Knowledge Exchange. Research, Technology Management, 53(6).
  • 23. Meredith, S., Francis, D. (2000). Journey towards agility: The agile wheel explored. The TQM Magazine, 12(2).
  • 24. Narasimhan, R., Talluri, S., Mahapatra, S. K. (2006). Multiproduct, multicriteria model for supplier selection with product life-cycle considerations. Decision Sciences, 37.
  • 25. Overby, E., Bharadwaj, V., Sambamurthy (2006). Enterprise agility and the enabling role of information technology. European Journal of Information Systems, 15(2), 120-131.
  • 26. Raišienė, A.G., Bilan, S., Smalskys, V., Gečienė, J. (2019). Emerging changes in attitudes to inter-institutional collaboration: the case of organizations providing social services in communities. Administratie si Management Public, 33.
  • 27. Russell, S., Dewey, D., Tegmark, M. (2015). Research priorities for robust and beneficial artificial intelligence. AI Magazine, 36(4), 105-114.
  • 28. Sahopta, M. (2012). An Agile Adoption and Transformation Survival Guide: Working with Organizational Culture. Nowy Jork.
  • 29. Sanchez, L.M., Nagi, R. (2001). A review of agile manufacturing systems. International Journal of Production Research, 16.
  • 30. Skyrius, R., Valentukevi, J. (2021). Business Intelligence Agility, Informing Agility and Organizational Agility. Research Agenda. Vilnius University Press. http://dx.doi.org/10.15388/im.2020.90.47
  • 31. Stverkova, H., Pohludka, M. (2018). Business Organizational Structures of Global Companies: Use of the Territorial Model to Ensure Long Term Growth. Social Science, 7(12).
  • 32. Teece, D.J. (2007). Explicating dynamics capabilities: the nature and microfoundation of sustainable enterprise performance. Strategic Management Journal, 13(28).
  • 33. Todorovich, K. (2020). Books: Becoming more agile. Strategic Finance, 10.
  • 34. Turing, A.M. (1950). Computing machinery and intelligence. Mind, 59(236), 433-460. https://doi.org/10.1093/mind/LIX.236.433
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-9f40bd92-08b0-420d-b92e-9e2ab7c0e748
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