Effective Education System for Athletes Utilising Big Data and AI Technology
Abstract
1. Introduction
2. Theoretical Background
2.1. Dual Education
2.2. Artificial Intelligence
2.3. Big Data
3. Methodology
3.1. Objective and Focus of the Study
3.2. Applied Methods and Techniques
4. Results
4.1. Orientation Analysis—Selected Examples from the Practice Focused on the Application of AI and Big Data
4.2. Results of the Questionnaire Survey
4.2.1. Descriptive Statistics
4.2.2. Summary Results of Selected Questions
4.2.3. Identification of Significant Dependencies Between the Variables Under Study
5. Discussion
6. Conclusions
- Create educational modules that will help athletes develop skills for life after sports;
- Implement programs using AI applications, e.g., in providing coaching or feedback;
- Provide practical training on the use of AI given the low awareness of AI among athletes;
- Raise awareness of AI tools and their use for athletes via appropriately targeted promotional campaigns and changes in the settings of current educational processes directly in sports organisations and in connection with educational institutions;
- Focus on both groups of potential students—athletes achieving significant sporting achievements and those less successful in sports. Both groups perceived the importance of education for a future career after sports at a comparably high level;
- Place specific emphasis on education for men, so that they perceive the importance of education itself more in preparation for a future career after sports;
- Focus on supporting the perception of importance, especially among team sports athletes, by conducting joint educational workshops linked to team training;
- Support motivation to learn via the inclusion of attractive educational forms—video content, podcasts, virtual reality, AI tools;
- Personalise educational activities for athletes to the extent possible based on the application of Big Data technology and appropriate analytics.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Research Hypotheses | Indicators |
---|---|
H1: Active young athletes perceive education as important in terms of preparing for a future career after sports. | Perceived importance of learning during a sports career |
H2: For young athletes who perceive their success in their sport as lower, education as preparation for a career after sports is more important. | Perceived success in the sport performed; perceived importance of learning during a sports career |
H3: The perceived importance of education during an active sports career to prepare for a future career after sports differs depending on the young athlete’s gender. | Perceived importance of learning; gender |
H4: There is a difference in the perception of the importance of education during sports careers among young athletes depending on the type of sport performed. | Perceived importance of learning; type of sport |
H5: There is a relationship between the perceived importance of young athletes’ education during their sports careers and the perceived attractiveness of AI as a form of learning. | Perceived importance of learning; perceived attractiveness of AI as a form of learning |
Technology | Type | Area of application | Results |
---|---|---|---|
EBSCOhost, ProQuest, WOS [52] | AI | administration, instructions for students, and learning | grading students’ assignments more effectively; customised curriculum and content |
Big Interview [29] | AI | universities and schools | improved students’ job interview readiness: 79.9% of respondents felt more prepared, and 55% felt more confident |
Wally [57] | AI | financial services | managing finances, categorising expenses and predicting costs; increasing financial literacy |
Duolingo [28] | AI | educational applications | interactive foreign language learning; motivating users to learn regularly and improving language skills |
Word2Vec, Glove [64] | Big Data | online learning | recognising the sentiment of users’ reviews to increase online courses’ quality |
IoT Wearables [61] | Big Data | healthcare | monitoring the physical, psychological, and behavioural state of individuals; analysing data in real-time |
Khan Academy [56] | Big Data | schools and online learning platforms | tracking students’ progress and identifying problem areas; providing personalised learning plans |
Basic Information | Absolute Value | Relative Value (%) | |
---|---|---|---|
Gender | Female | 72 | 30 |
Male | 164 | 69 | |
Other | 1 | 1 | |
Age | 14 | 30 | 13 |
15 | 18 | 7 | |
16 | 38 | 16 | |
17 | 59 | 25 | |
18 | 49 | 21 | |
19 | 43 | 18 | |
Location | Žilina | 63 | 27 |
Trenčín | 27 | 11 | |
Martin | 21 | 9 | |
Považská Bystrica | 19 | 8 | |
Bánovce and Bebravou | 15 | 6 | |
Myjava | 12 | 5 | |
Námestovo | 10 | 4 | |
Other | 70 | 30 | |
High school type | Grammar school | 81 | 34 |
Secondary vocational school | 81 | 34 | |
Secondary sports school | 66 | 28 | |
Other | 9 | 4 | |
Sport type | Individual | 43 | 18 |
Team | 194 | 82 |
Success in Sports | Importance of Education for the Future | ||||
---|---|---|---|---|---|
Not Important (%) | Partially Important (%) | Neutral (%) | Important (%) | Very Important (%) | |
Unsuccessful | 0 | 0 | 0 | 0 | 1 |
Below averagely successful | 0 | 0 | 1 | 2 | 3 |
Averagely successful | 0 | 12 | 6 | 18 | 17 |
Above averagely successful | 1 | 9 | 3 | 7 | 4 |
Very successful | 0 | 1 | 2 | 7 | 3 |
Gender | N | Mean | Standard Deviation | F | F Significance | t | t Significance |
---|---|---|---|---|---|---|---|
Male | 163 | 3.454 | 1.172 | 35.902 | <0.001 | −4.968 | <0.001 |
Female | 72 | 4.208 | 0.804 |
Type of Sport | N | Mean | Standard Deviation | F | F Significance | t | t Significance |
---|---|---|---|---|---|---|---|
Individual | 42 | 4.310 | 0.680 | 28.761 | <0.001 | 4.097 | <0.001 |
Team | 193 | 3.549 | 1.159 |
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Mičiak, M.; Toman, D.; Adámik, R.; Kufová, E.; Škulec, B.; Mozolová, N.; Hoferová, A. Effective Education System for Athletes Utilising Big Data and AI Technology. Data 2025, 10, 102. https://doi.org/10.3390/data10070102
Mičiak M, Toman D, Adámik R, Kufová E, Škulec B, Mozolová N, Hoferová A. Effective Education System for Athletes Utilising Big Data and AI Technology. Data. 2025; 10(7):102. https://doi.org/10.3390/data10070102
Chicago/Turabian StyleMičiak, Martin, Dominika Toman, Roman Adámik, Ema Kufová, Branislav Škulec, Nikola Mozolová, and Aneta Hoferová. 2025. "Effective Education System for Athletes Utilising Big Data and AI Technology" Data 10, no. 7: 102. https://doi.org/10.3390/data10070102
APA StyleMičiak, M., Toman, D., Adámik, R., Kufová, E., Škulec, B., Mozolová, N., & Hoferová, A. (2025). Effective Education System for Athletes Utilising Big Data and AI Technology. Data, 10(7), 102. https://doi.org/10.3390/data10070102