Effective Education System for Athletes Utilising Big Data and AI Technology

Martin Mičiak (), Dominika Toman, Roman Adámik, Ema Kufová, Branislav Škulec, Nikola Mozolová and Aneta Hoferová
Additional contact information
Martin Mičiak: Department of Management Theories, Faculty of Management Science and Informatics, University of Žilina, 010 26 Žilina, Slovakia
Dominika Toman: Department of Management Theories, Faculty of Management Science and Informatics, University of Žilina, 010 26 Žilina, Slovakia
Roman Adámik: Department of Management Theories, Faculty of Management Science and Informatics, University of Žilina, 010 26 Žilina, Slovakia
Ema Kufová: Department of Management Theories, Faculty of Management Science and Informatics, University of Žilina, 010 26 Žilina, Slovakia
Branislav Škulec: Department of Management Theories, Faculty of Management Science and Informatics, University of Žilina, 010 26 Žilina, Slovakia
Nikola Mozolová: Department of Management Theories, Faculty of Management Science and Informatics, University of Žilina, 010 26 Žilina, Slovakia
Aneta Hoferová: Department of Management Theories, Faculty of Management Science and Informatics, University of Žilina, 010 26 Žilina, Slovakia

Data, 2025, vol. 10, issue 7, 1-24

Abstract: Education leads to building successful careers. However, different groups of students have different studying preferences. Our target group are athletes, combining their education and sports training. The main objective is to provide recommendations for an effective education system for athletes, improving their chances of finding new careers after leaving sports. Such a system must include Big Data and utilise AI possibilities currently available that support athletes’ career planning and development in a meaningful way. The main objective is specified by the following partial objectives: identifying what types of Big Data to analyse in connection with the athletes’ education; revealing what AI tools to include in the athletes’ education for their better preparation for a career after sports; determining what knowledge of AI and Big Data athletes need to stay relevant once they enter the labour market. Our study combines secondary and primary data sources. The secondary data (used in the orientation analysis) include case studies on AI and Big Data connected to education. The primary data were collected via a survey performed on over 200 Slovak junior athletes. The results show directions for the sports policymakers and sports organisations’ managers willing to improve their athletes’ career prospects.

Keywords: Big Data; AI tools; education; career planning; athletes; sports management (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2306-5729/10/7/102/pdf (application/pdf)
https://www.mdpi.com/2306-5729/10/7/102/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jdataj:v:10:y:2025:i:7:p:102-:d:1686381

Access Statistics for this article

Data is currently edited by Ms. Cecilia Yang

More articles in Data from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().