Effective Education System for Athletes Utilising Big Data and AI Technology

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.

1. Introduction

In recent decades, sports have become one of the areas significantly benefiting from modern technologies, especially from the possibilities offered by Big Data and artificial intelligence (AI), [1]. The development and utilisation of these technologies creates new opportunities not only for improving sports performance but also for effective education of athletes. They face the challenge of combining their professional careers with preparation for life after sports [2]. It is therefore essential that they not only work on their physical preparation but also acquire knowledge allowing them to succeed outside the world of sports. Up to 65% of global organisations expect a positive impact from using Big Data analytics at work (period from 2023 to 2027). In connection with AI, a similar impact is expected by 49% of organisations [3]. Both these concepts thus need to be connected to the education system for current athletes. The concept of dual education, which supports a balance between sports and academic or professional preparation, is crucial for this. Such education not only allows athletes to better manage the transition to new roles but also contributes to their long-term integration into society after their sports careers [4].

The stepping stone for making athletes’ education better and more effective is understanding the shortcomings of the classic approach to teaching this particular group of students (athletes). Traditional models of athlete education based on classic learning theories have several limitations. Since athletes primarily focus on training and sports performance, they do not have much space and time for studying. Therefore, they are often considered “stupid” and uneducated by their peers. Athletes also feel isolated and are the target of negative criticism. They must deal with increasing pressure from family, coaches, and fans [5]. China tried to use the “education plus sports” model in the education of athletes. In 2015, this form of education served only as a supplement to the common social education system. The limitations of this system were the low diversification of athlete education, the lack of rules and regulations, and the absence of supervision over this education [6]. Traditional theories and approaches to education also include cooperative forms. The cooperation among the members of this specific group should be supported so that athletes are responsible, able to respect each other, and use the synergistic effect of cooperation. However, since the sports environment is highly competitive, cooperative learning is often not sufficient or effective [7].

The issues present in education efforts focused on athletes can be at least partially solved by the inclusion of new technologies such as AI and Big Data. AI’s estimated impact on global education revenue by 2023 was 4%, so it is crucial to understand this area and take advantage of its benefits [8]. Therefore, the current era brings new opportunities for athletes’ education via advanced technologies. AI provides tools for performance analysis, optimisation of training plans, and injury prevention, essential for maintaining competitiveness at the top level [9]. At the same time, it offers various consulting tools that can help athletes prepare for further life challenges after the end of their active sports careers. Supporting educational programs via AI allows not only the development of sports skills but also the acquisition of knowledge necessary for success in various other professions. The knowledge and skills that can be enhanced among groups of athletes via the application of AI include language skills, financial management skills, analytical skills, and others, which are described in more depth in the secondary data analysis of our study.

With the broader application of Big Data technologies and the Internet of Things (IoT), athletes and their coaches gain access to a huge amount of data, enabling precise analysis of physical parameters, performance, and tactical decisions during matches [10,11]. These data can be used not only to refine training processes but also for long-term career planning and continuous improvement of the athletes’ personality traits. Thus, athletes’ education supported by Big Data and AI represents a significant step towards athletes reaching high performance but also becoming adaptive professionals prepared for a future outside of sports [12]. This is confirmed by the estimated global increase in demand for IT skills due to the use of AI. These are mainly skills connected to AI and machine learning (80%); and data analytics and Big Data (74%) [13]. This brings an important perspective that needs to be considered in athletes’ education. They need to master at least the basics of using AI and Big Data technology to become relevant in the labour market. This is a relatively novel topic, and the connection of sports with AI and Big Data still has not been extensively researched. Thus, Big Data needs to be collected on the specifics and preferences of athletes in connection with their learning so that the whole educational system for them can be set in a more effective way. Simultaneously, athletes should understand at least the basics of the Big Data concept and its application in business settings because this makes them more relevant in the labour market outside of sports.

The novelty of our study is the uniqueness of connecting sports with a focus on careers after sports, using AI and Big Data technology tools for athletes’ preparation. This is an important social issue because numerous athletes have difficulties integrating into society and finding other sources of income after finishing their sports careers. This is only exacerbated by the current application of classic education theories in athletes’ education. Therefore, the identification of ways how to include AI and Big Data concepts in this educational system is so critical. Athletes often lack financial literacy, as well as the hard and soft skills necessary for the professions of the future. These are the ones that will be relevant at a time when current athletes end their active professional sports careers. Our study focuses on solving this problem in an original way.

Figure 1 illustrates the concept of effective education of athletes for their future careers after sports, supported by modern technologies, specifically by Big Data and AI. The focus is on the idea of dual education, allowing athletes to smoothly transition from top sports to other professions. This transition is supported by various education programs that enable athletes to prepare for their future careers outside of sports. The figure therefore represents the connections between the key research elements included in this study.

Collecting Big Data on athletes’ specifics, preferences, and lifestyle and analysing these data properly can enhance athletes’ sports performance, mitigate mental health risks emerging from the pressure of performing sports and studying at the same time, and can also help this group better manage their finances. These are all significant benefits that are introduced by Big Data technology. However, in our study, we particularly focus on the potential that Big Data has for improving athletes’ learning and development. AI tools bring another important aspect to this issue. They provide significant education support for the learners (athletes) while also providing experience with this technology, which is necessary for numerous career options in the future. Therefore, a sustainable and future-proof educational system for athletes needs to reflect on all these partial issues while continuously monitoring newly emerging trends (technological, as well as educational and social ones).

Our research questions are included directly in the scheme presented above. Each is related to a specific subchapter within the analysis of the theoretical background presented in the following chapter. The answers to these questions stem from a combination of theoretical background, orientation analysis, and the questionnaire survey that we conducted. The research questions are stated as follows: RQ1: Is education outside of sports important for the athletes themselves? RQ2: Can AI help athletes with their learning? RQ3: How can Big Data help athletes with their learning and preparation for future careers?

After the introduction of the area of sports management and the role of education processes in it, the next chapter focuses on the analysis of the theoretical background. Here, dual education, AI, and Big Data in the education of athletes are described in more detail. This is followed by a description of the methodology where we focus on the whole research process, the data obtained, and the methods applied. The following chapter presents the research results. Specific examples of the utilisation of AI and Big Data technologies are described (orientation analysis). The results related to our own questionnaire survey are also presented. We work further with the obtained primary data and analyse selected dependencies using corresponding statistical tests. Finally, we summarise the results, provide recommendations for relevant stakeholder groups, identify the limitations of this study, and propose directions for future research.

2. Theoretical Background

Following the concepts outlined in the introduction, our theoretical background focuses on the analysis of the current state of athletes’ education using selected educational programs. It describes the factors that may influence the decision of athletes to engage (or not) in studying more. Finally, it describes the application of AI and Big Data in education.

2.1. Dual Education

There are several reasons why athletes need to prepare for life after sports. These include the planned or unplanned end of one’s sports career. In these two areas, we can further divide the athletes’ sports career ends according to their readiness for life after sports, focusing on prepared or unprepared athletes. The reasons for ending a sports career have a significant impact on the former athletes’ psychological well-being and social integration. Post-career adaptation is a complex process requiring targeted support and understanding of individual athletes’ needs [14]. Career support programs were established to help athletes prepare for a career after leaving professional sports. Stambulova and Harwood [15] define the goal of such programs as providing active current athletes with assistance and tools to prepare for life after leaving sports. The programs also provide support to athletes during the transition to sports retirement, as they may encounter potential difficulties, such as stress, loss of identity, anxiety, depression, or fear of rejection [16].

Examples of such educational programs include Athlete365 Career; Olympic Educational Programs; PROAD; Moving on, Game Plan, ICL, or UTR [17,18,19]. Many of these programs are aimed at career support while providing other services to improve overall athlete performance, workshops, nutritional advice, or advice on early retirement planning. The non-profit organisation UCL developed an award-winning leadership and character education program in collaboration with the Alcoa Foundation, Citibank, and Verizon [16,20].

Athletes engaging in dual education during their sports careers have better coping skills and face fewer obstacles in finding their places in society [15]. Dual education has been identified as the most suitable form of learning and skill acquisition for athletes. It enables them to pursue their professional sports careers while developing knowledge and skills applicable after sports. However, there are multiple challenges connected to this form of education. For the athletes, this is about finding a balance between sports trainings and educational tasks. Athletes may lack the necessary personal management skills to make important decisions about their future careers. By recognising their strengths, they can better cope with the challenges associated with the transition to new roles and environments [21]. It is in these cases that educational programs using modern technologies such as AI and Big Data support would be appropriate, which supports the overall focus of our study. This aspect is covered by the research questions set for this study.

Solutions to manage the described athletes’ life stages are influenced by psychosocial variables, which often decide whether the end of a sports career can be managed smoothly. It is necessary to continue in-depth research into the life stories of athletes within the generations already retired from sports, as well as those currently heading towards this stage. Then it will be possible to react responsibly to the athletes’ current needs, e.g., by educational programs designed not only for athletes but also for coaches and club managers. Their education can be supported via applied content, workshops, and lectures offered within educational programs and personal counselling [22].

2.2. Artificial Intelligence

AI represents computer science that creates machines imitating human intelligence. It is about simulating human behaviour and decision making. This field therefore includes technologies aimed at automating cognitive tasks commonly performed by humans [23]. When working with AI, an essential aspect is engineering, which is used to create a relationship between objects and their properties [24]. AI is an interdisciplinary field that includes logic, mathematics, computer science, psychology, management, and ethics [25]. Figure 2 represents the individual layers of technologies leading to AI, which are used to process Big Data (large amounts of differently structured data).

Modern technologies, including AI, are increasingly being used to support learning processes. In our study, we focus on the application of AI in athletes’ education. The development and progress associated with AI is already changing the way athletes achieve their career goals. AI is a powerful tool athletes, coaches, and trainers can use to achieve better performance. Machine learning and computer vision provide useful tools in sports for analysing performance, adapting training, and preventing injuries, which are a common reason for athletes to leave sports [27]. However, it is of vital importance for athletes to be aware of the need for dual education at the beginning of their careers. An integral part of education and training in various directions are AI algorithms and educational robots, which support learning activities and the overall ability to learn [28].

There are several reasons why athletes may end their sports careers. However, many of them make this decision based on their own free will, not because of an injury but because they are unable to meet the high demands of a given team or individual sport in the long run. That is also why it is necessary to consider what knowledge athletes should have about AI to support their sustainable performance. AI and machine learning can be of help not only for career advancement but can also be used to process an athlete’s failure or to prepare for life after sports. AI can provide athletes with valuable advice. It can be used, e.g., in preparing for job interviews. Athletes should know specific AI tools that will be helpful in the future. It is advisable for them to also know how individual tools work and when they can be effectively used. These tools include language tools and the tools to support financial literacy or financial management [29].

2.3. Big Data

Data are currently an important source of organisations’ successes. It is essential that managers know how to use its potential. The term Big Data includes datasets containing such a large number of records that the processing exceeds humans’ capabilities. Big Data simultaneously contains unstructured, semi-structured, and structured data. It is characterised by constant growth. Working with Big Data requires the use of special software tools [30]. These tools are used for data collection, storage, analysis, search, sharing, transfer, visualisation, and data security [31]. Data are collected from multiple sources, increasing the complexity of working with it. Processing Big Data requires considerable expertise [32].

Industry is increasingly focused on data, analytics, IoT, sensors, and integrated processes. Because of this, organisations face challenges in collecting and storing large amounts of data. However, transforming the enormous flow of Big Data into actionable knowledge is a highly complex task. Big Data can also be described as a set of dynamic properties of data, which are voluminous, fast, diverse, trustworthy, and valuable [33,34]. It is important to create a secure infrastructure supporting effective data collection and analysis so that organisations can innovate or optimise their processes faster and easier. A key element are qualified employees able of utilising various analytical tools. Qualified personnel should have expertise and skills related to data science or machine learning. Such employees can increase the efficiency of data management and improve the organisation’s overall efficiency [35].

Technology has taken sports management to the next level, from player performance evaluation to fan engagement. Sports organisations have access to invaluable insights due to Big Data technology [36]. Analysing athletes’ performance allows coaches to uncover patterns in movements, physiological responses, and tactical decisions. This knowledge provides the opportunity to adapt training, improve tactics during matches, and streamline recovery. Sensors worn by athletes, such as IoT devices, monitor individuals and provide them with feedback to improve their performance. A major benefit of sensors is their ability to alert athletes to potential injuries [37,38].

Big Data sets are a great tool for analysing players’ performance or identifying new game strategies. A sports organisation’s productivity is increasing based on using chatbots, which are powered by AI and virtual assistants. These technologies are used in ticket sales or customer services [39]. Due to Big Data analysis, sports management, training, and overall care for athletes have already significantly improved. However, there is still significant room for this technology to improve the preparation of athletes for their future careers outside of sports. Education has undergone a significant transformation in recent years. This is due to strong technological advances in the field of AI and Big Data [40]. Machine learning algorithms and Big Data analysis can identify potential students among athletes, based on performance indicators, injury history, and their potential to develop [38,41]. AI, combined with Big Data, brings fundamental innovations to the sports industry, and thus advanced predictive analytics capable of helping with strategic planning and decision making directly during sports events. Virtual and augmented reality tools are streamlining training and the overall spectators’ experience via simulations and clear statistical displays. AI is included in the development of sports equipment where it allows athletes to monitor and collect key data using smart sensors. This Big Data can be analysed and used to improve athletes’ performance [42]. Once again, the utilisation of these modern technologies in sports still improves other aspects, not athletes’ education. This calls for more research focusing on this issue.

Following the theoretical background and research questions established, we further set the hypotheses and indicators necessary for their verification. The connection between the hypotheses and examined indicators is captured in

Table 1. Research questions, hypotheses, and indicators.

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

.

The indicators applied in our research approach were linked to the results of our questionnaire survey and selected statistical tests. More details about the entire applied methodology are presented in the following part. Later, in the chapter focused on presenting the results achieved, the individual hypotheses are evaluated in connection with the relevant findings and tests’ results.

3. Methodology

Following the research questions of our study presented in the introduction and the hypotheses based on the theoretical background analysed, this part describes the overall objective and focus of our study, as well as the methods applied. Attention is also paid to the description of our questionnaire survey.

3.1. Objective and Focus of the Study

The main objective of our study is to identify effective elements of the athletes’ education process based on the analyses performed, specifically using AI and Big Data tools to support their later careers outside of sports. Subsequently, a part of the objective is to propose recommendations regarding personalised education systems for sports associations and clubs. The object of this study is represented by athletes from the Slovak Republic aged 14 to 19. Based on the available information, the population was rounded to 55,000 individuals. This population was derived from the number of students enrolled in lower secondary education from official Eurostat data [43] and additional information regarding the formula for the distribution of funds for sports from the state budget. This formula includes the number of young athletes in the given age category. The basic characteristics of the collected sample from the perspective of its internal structure are presented directly in the relevant results section (Section 4.2.1 Descriptive statistics). The source of primary data was our questionnaire survey distributed online. The sources of secondary data were used in the orientation analysis. This includes selected examples from practice related to the specific utilisation of AI and Big Data tools in education. The examples represent the connection of theory with practice and the basis for the comparison of secondary data with our primary research results. The application of AI and Big Data in educational cases creates a broader context for our survey results. This part is connected to the research questions presented in the introduction. However, due to the nature of the secondary data, this part is not connected with the verification of our hypotheses.

The purpose of the questionnaire survey was to find out how young athletes perceive the importance of education for their future and what their preferences are concerning different forms of education (including AI possibilities and the identification of possible implementation of Big Data support). At the same time, we try to identify the motivational factors affecting athletes when choosing sports and examine the relationship between success in sports, gender, and attitude towards education and learning. This survey also leads to a comprehensive picture of the factors that influence the attitude of young athletes towards education. It includes the identification of the most attractive forms of education for athletes, the perception of AI-supported learning methods, and the balance between sports activities and athletes’ development outside of sports. The data collected via the questionnaire survey were used to verify our hypotheses presented at the end of the theoretical background.

3.2. Applied Methods and Techniques

The questionnaire survey was conducted on a sample of 237 respondents (n = 237). Given the size of the relevant population, which was approximated and referenced above, the collected sample led to a margin of error at 6.35%. This sample allowed us to obtain a representative overview of the attitudes and preferences of Slovak athletes aged 14 to 19 towards learning and education. The error level provided an adequate accuracy of the results within the set objectives. In the implementation phase, we focused on contacting young athletes in order to reach a representative sample for the data collection. Respondents were approached by distributing a link containing an accompanying text explaining the purpose of the survey, the anonymity of the participating respondents, and the importance of their answers for the analysis of attitudes towards education among athletes. The questionnaire itself was created via the Google Forms platform, which enabled simple and clear collection of responses. This method of distributing the questionnaire ensured quick and effective feedback from respondents. The entire process was conducted online, which reduced the time and other requirements. The questionnaire was accessible for two months, from April 2024 until the end of May 2024. It contained 24 questions in total. The questionnaire included interlinked questions aimed at determining the attractiveness of various forms of education among respondents. One of these forms was the use of AI as a tool to facilitate education and provide specific benefits in this process. The answers were collected in the form of a set Likert scale.

To define key variables used in our study, specifically designed questions were included in the survey to identify the respondents’ perceived AI attractiveness for learning and educational importance. Except for the option when the respondent did not want to express his/her opinion (these answers were filtered out), the corresponding questions had the choices defined by the following scales. AI attractiveness for learning: 1 = not attractive; 2 = partially attractive; 3 = neutral; 4 = attractive; 5 = very attractive. Educational importance: 1 = not important; 2 = partially important; 3 = neutral; 4 = important; 5 = very important.

In connection with the young group of respondents, the area of Big Data was deliberately not included as a separate question. This is based on the premise that acquiring skills in the use and analysis of Big Data needs to be included in the education of athletes later. Therefore, in our article, Big Data currently represents the context of the use of Big Data in education for a more effective setting of this system (provided by the theoretical background and the orientation analysis). It is not yet a matter of including this topic directly in the education of the surveyed group of respondents. For the purposes of this article and the defined topic, we worked with a selected set of questions (presented in Section 4.2.2).

The validity of our questionnaire was tested by calculating Cronbach’s Alpha. For the calculation, we included relevant questions whose answers were represented by a Likert scale. The result of calculating this indicator (0.722, for N = 11 items) confirmed the suitability of the questionnaire for examining the selected elements. This was aligned with the original research requirements because the value exceeded the usual accepted threshold, even though a higher value would be better. For this study, the Cronbach’s Alpha represents a justification of our questionnaire and, for our future surveys focused on other relevant groups of respondents in the whole project, it represents room for improvement with how we set the questions.

Several methods and techniques of data collection and analysis were used in the creation of this study. As part of the theoretical background analysis, we used content analysis of documents, as it was necessary to process information from multiple relevant sources. We applied sociological inquiring in the form of a questionnaire survey. Based on this, we were able to analyse the attitude of young athletes towards learning and education. The choice of this method was supported, e.g., by a study dealing with the motivation of Latvian athletes to build a dual career. This also used a questionnaire survey as the main source of data [44]. The induction method was used to draw conclusions and summaries from individual parts of this study. Deduction was used to design recommendations based on the knowledge acquired. The comparison and synthesis were also applied here. We analysed the data obtained via the questionnaire survey using the Chi-square test, T-test, and Cramer’s V, which were also used in other relevant studies focused on sports, e.g., [45,46,47]. The Chi-square test is regularly used in its various applications in the current research [48]. One of its important applications is the detection of dependence among the studied categorical variables obtained from questionnaires surveys [49,50,51]. This is fully aligned with the main focus and aim of our study.

4. Results

In the analysis of secondary data (orientation analysis), we focused on selected examples from practice using AI and Big Data in the educational process. These were individually described and compared using a table at the end of Section 4.1. In the analysis of primary data, we include descriptive statistics, summary results, and identification of significant dependencies among the variables studied (Section 4.2).

4.1. Orientation Analysis—Selected Examples from the Practice Focused on the Application of AI and Big Data

The use of AI by educational institutions is widespread and is applied in various forms. It started with computer-related technologies and online intelligent learning systems, which were later accompanied by web chatbots. Due to these technologies, lecturers were able to increase the efficiency of students’ assessment and grading. It was also possible to modify the curriculum and teaching content. These were adapted and personalised to the students’ needs. Based on these changes, students’ retention of knowledge was supported and the experience with the teaching process and the overall quality of learning were improved [52].

According to current statistics and predictions, investments in AI in education are expected to grow by 36% by 2030, reaching USD 19.2 billion [42]. AI in education has shown its effectiveness in learning in schools, where, according to BusinesSolution [53], it increased students’ performance by 30% and reduced their anxiety by 20% [28,54]. Studies also indicate that the ChatGPT (version 3.5) AI tool can be used in creating personalised training in the field of sports, analysing athletes’ performance or reporting on sports events [55]. Integrating AI into the educational process provides personalised recommendations, adaptive learning experiences, or data-driven feedback [56]. AI may not only help in providing knowledge, but third-year pharmacy students also tested its use in preparing for a job interview. In this experiment, they worked with Big Interview (used on samples of students in 2023 and 2024), a software based on AI technology. This was tested on a sample of 234 respondents and the researchers examined subsequent behavioural changes. Students involved in the study were allowed to use Big Interview as part of lectures on best practices for conducting a job interview. Their task was to reflect on their experiences with this tool. The study showed that, after the Big Interview experience, 55% of students felt increased self-confidence; 79.9% confirmed increased interview readiness; and 38.9% expressed a belief that they would be invited to a second round of job interviews [29].

One of the possible risks after the end of a sports career is the lack of knowledge of personal financial management. This issue affects former and current active athletes. Bad financial decisions can put athletes in adverse life situations, the solution to which is lengthy and difficult. However, athletes can also use the help of AI here. One example is the Wally application. This can offer individuals help with managing personal finances by categorising them according to expenses and income and predicting when a person should save. The application helps predict the impacts of financial decisions and provides recommendations aimed at debt management. There are also other similar applications, and these can be helpful to athletes during their sports careers and after their end [57]. To increase the chances of an athlete in the labour market, applications supporting the development of language skills are useful too. An athlete can learn foreign languages continuously at a chosen time without having to attend a language school. The education process can be combined with an active sports career. Mastering a foreign language helps the applicant significantly increase the chance of advancing to the next round of a job interview and becoming employed after finishing an active sports career. Such support tools are used around the world, including the popular application Duolingo. Its design motivates people to use it daily, supporting continuous education. Knowledge of a second language can ensure a monthly salary higher by 11% in a job after a sports career [28,58]. Several specific ways of applying AI in the education of athletes were identified. These led to the fulfilment of different educational goals. AI was applied in personalising education and preparing for a job interview, processing personal finances, or acquiring language skills. Therefore, the relevance of this technology for an effective setting of the athletes’ education system was clearly supported.

Smart technologies, such as wearable devices, represent significant potential for improving various areas of life. IoT devices can collect data on the physical, mental, and behavioural health of individuals. This data is analysed in real-time. This approach allows not only to obtain valuable information but also to create predictions of future developments [59,60,61]. Thus, Big Data opens a wide range of possibilities for current and future education. Various studies focused on analysing the benefits and challenges stemming from the implementation of Big Data in the educational process [62].

In recent years, online courses have gradually become the mainstream of education. User comments are key data reflecting the quality of online courses and are very important for improving the quality even further. A new two-channel deep network sentiment analysis model based on the MOGWO ensemble method was proposed for sentiment analysis. The model uses Word2Vec and Glove in the vector representation of words. The results show that the sentiment recognition accuracy of the proposed model is higher than that of other comparative models [63].

The utilisation of Big Data analytics in education is expected to reach USD 47.82 billion by 2027, growing at a compound annual growth rate of 20.79% [64]. Big Data plays a pivotal role in education as it enables personalised learning and increases student engagement. This technology contributes to higher success in the labour market. It is expected that Big Data will improve data security measures and positively influence changes in career guidance even further [65,66]. This future development can significantly affect athletes’ careers after leaving professional sports. The utilisation of Big Data analysis can provide recommendations for athletes’ personal development, tailored to individual abilities. Big Data gives athletes personalised and digital learning, which is easier to combine with their demanding sports careers. In this case, athletes can learn anywhere and anytime. They can also obtain real-time feedback based on this technology [55].

Big Data technologies enable the creation of intelligent and interactive training systems adapting educational experiences to the specific needs, preferences, and learning patterns of individual students. Due to them, it is possible to focus on the personal needs and weaknesses of each student. An example of such an application of this technology is the Khan Academy platform, using data analysis to track students’ progress, identify problem areas, and offer personalised learning paths [55]. The identified examples of the application of Big Data technology in education are summarised in

Table 2. Application of AI and Big Data in diverse areas (comparison based on the sources listed).

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

. If sports clubs were to introduce similar intelligent educational systems for their athletes, they could achieve a significant improvement in their knowledge and skills. Based on this, athletes would have the opportunity to work more effectively on improving their skills. Specifically, this concerns improving performance, preventing injuries, or practising strategic thinking. Such an approach would help athletes in the field of education but would also provide clubs with a competitive advantage and support the long-term development of talents. Sports organisations should therefore identify areas in which athletes excel, as well as those that need to be improved. Furthermore, they should create unique educational programs for each athlete individually so that they can improve based on their current level of abilities [67].

The identified benefits include an increased level of engagement of education participants, personalisation of the educational process, focus on the learners’ individual abilities, the possibility of learning digitally regardless of location, and the creation of interactive educational elements. In practice, it is possible to see various examples of the use of AI and Big Data technologies, which could also be transformed into methods and tools for training and educating athletes. This would make them better prepared for their careers after sports.

4.2. Results of the Questionnaire Survey

This part focuses on a questionnaire we targeted at young athletes. In the survey, we collected 237 responses. The following sections present summary results of key questions from the questionnaire, as well as specific dependencies resulting from the statistical tests performed.

4.2.1. Descriptive Statistics

More men than women participated in the survey (164 to 72), with one respondent choosing the option “other”. Concerning age, there was a significant representation of respondents aged 17, 18, and 19. The majority of respondents came from Žilina (27%) and the next most represented group were respondents from Trenčín (11%). Within the education category, respondents mostly attended grammar schools (34%), secondary vocational schools (34%), or secondary sports schools (28%). We classified respondents into two groups according to whether they were involved in individual or team sports. The results show that most respondents (up to 82%) were involved in team sports and the remaining 18% performed individual sports. The absolute and relative values for the basic characteristics of the respondents are displayed in

Table 3. Descriptive statistics/basic characteristics (own elaboration).

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

.

Based on the results of descriptive statistics, it can be concluded that most respondents were men aged 17 to 19, coming mainly from central Slovakia, involved in team sports.

4.2.2. Summary Results of Selected Questions

As part of the questionnaire survey, we analysed the sports success of respondents (n = 237), where they had the opportunity to rate themselves on a scale from very successful to unsuccessful. Almost half of the respondents (46.4%) answered that they were averagely successful in the sport that they perform (Figure 3); 26.6% of respondents indicated that they considered themselves above averagely successful and 13.1% as very successful; 5.9% of respondents chose the option of below averagely successful, 5.5% of respondents were not able to assess their success, and 2.5% considered themselves unsuccessful in their sport. This shows that most respondents considered themselves successful and only a smaller group saw themselves as unsuccessful or below averagely successful.

For the questions covering our key variables, we then worked with a filtered dataset where the answers showing that the respondents did not want to express their opinions were not included (n = 168). With this dataset, the basic statistical indicators were calculated for the perceived success in the sport as follows: mean = 3.369, standard deviation = 0.849.

The question of “How important is education for your future?” was answered by 236 out of 237 respondents. Figure 4 shows the distribution of individual responses. Up to 69% of respondents considered education important or very important for their future. For 14% of respondents, education was partially important, and for 17% it was neutral or unimportant. The results indicate that young athletes are aware of the need for education for their future and perceive it as important. With the filtered dataset (n = 168), the statistical indicators were as follows: mean = 3.708, standard deviation = 1.146.

Based on these results, it can be concluded that the validity of hypothesis H1 is confirmed. Overall, education and preparation for a future career after sports is seen as crucial by Slovak young athletes. This is also supported by the high calculated value of the mean for this variable presented above.

Respondents were then given the opportunity to express their opinions on the attractiveness of individual forms of education. They were asked to rate the presented form of learning on a scale from “unattractive” to “very attractive”. From the answers obtained (minus those showing that respondents did not want to express their opinions; n = 225), we created summaries captured via a radar chart shown in Figure 5. The higher the value, the higher the attractiveness of the given form of learning and education. The most attractive form of education was video content (791) such as YouTube, TikTok, Instagram, etc. This was followed by forms such as podcasts (774), lectures (756), workshops (721), and coaching/mentoring (706). Values exceeding the total number of respondents were achieved because this was a question with the possibility of choosing multiple options. There were no weights assigned to the options or values themselves. The exact scale was clearly described in this study’s methodology. The radar chart was selected based on the possibility to see individual summarised scores for the options and fast and convenient comparison of the results.

The least attractive learning and education forms were books (636), popular articles (570), and educational online portals (500). AI (asking questions ChatGPT) was partially attractive to respondents, as this form reached a total value of 612. AI was currently still in the development phase, and the students, athletes, and scientists were still getting to know it in more detail. Nevertheless, it achieved higher preference than other forms of education, so we assume that, over time, AI will become one of the most attractive forms of education and will significantly help young athletes with their development.

The basic statistical indicators on the filtered dataset for the option we focused on in this study (AI) were as follows: mean = 2.982, standard deviation = 1.147. This calculation supports the conclusion that the attractiveness of AI in learning among young athletes is not that high yet.

The next question was aimed at finding out the reason why respondents started participating in their chosen sports. Respondents had the opportunity to select several from the options available or write their own reason (Figure 6). The most frequent reason was “own interest”, with a total of 149 answers. This indicates that young people engage in sports that they enjoy and that make them feel good. Slightly fewer respondents selected the option “fun” (141). Respondents therefore perceive sports as a pleasant and enriching way to spend their time. The reason “exercise” was ranked third, with 111 responses. This value shows that young people are also aware of the importance of physical activity for their health. The next places were taken by the options “sports role model” (94) and “family” (87). The family environment can play an important role in motivating young people to participate in sports. The rest of the reasons were “current friends” (48), “new friends” (21), and “trend, fashion” (3).

Based on the results, we conclude that most young athletes start participating in sports on their initiative, which is very positive. If a child starts participating in sports on their initiative, they likely enjoy it and find it fulfilling, and such an athlete will stay in the sport for longer. All summary results from our survey create an essential context for setting the educational process for active athletes with the application of AI and Big Data tools for their better preparation for future careers. The next part of our results follows on from these summary results, adding an analytical perspective via the statistical tests performed.

4.2.3. Identification of Significant Dependencies Between the Variables Under Study

Potential dependence was determined by the Chi-square test. The dependence between the success of individual respondents in sports (from failure to high success) and the importance of education for the future was examined first. Respondents expressed their opinion on the extent to which education is important to them (from unimportant to high importance). The individual values related to the above variables are shown in

Table 4. Dependence of perception of the importance of education on success in sports (own elaboration).

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

.

The calculation of the Chi-square test did not confirm a statistically significant relationship between perceived success in sports and the importance of education. The calculated value for the test was 24.692, which does not exceed the critical value of 26.296 with 16 degrees of freedom. This finding confirms that there is no statistically significant relationship between the success of individuals in sports and their attitude towards the importance of education.

The results thus indicate that the validity of hypothesis H2 could not be confirmed. This is supported also by the fact that the data did not meet the criteria to run Fisher’s exact test in SPSS (version 29), which was used as another tool for this hypothesis verification. This means that, when setting an athlete education system with the application of AI and Big Data, it is necessary to focus on both more and less successful athletes.

Another relationship we focused on examined whether the attitude and perception towards education depended on gender. The total number of respondents within the selected two questions was 235 (responses that could not be objectively included in the comparison were excluded). The results of the Chi-square test confirmed that the calculated value of 27.335 was higher than the critical value (9.488). The calculation was performed with four degrees of freedom. The values indicate a confirmation that the variables were statistically significantly dependent, and therefore the perception of education was significantly influenced by the respondent’s gender. The same result was achieved by another relevant test, in this case the T-test (

Table 5. Dependence of the perception of the importance of education on gender (own elaboration).

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

). The significance of the result was corroborated by the p-value (<0.001). This dependence was further examined to reveal its direction.

The total number of women analysed within the dependence was 72 and the total number of men was 163. To identify the direction of dependence, the following chart was created (Figure 7). Based on the ratio of responses divided by gender, it can be stated that female athletes attach significantly greater importance to education than men. This results from the fact that female respondents generally indicated options expressing a high perceived importance of education and none of the female respondents chose an option expressing the unimportance of education during an active sports career.

These results demonstrate that the validity of hypothesis H3 was confirmed. The future setting of an effective educational system for the education of active athletes with the application of AI and Big Data technologies needs to specifically focus on increasing the perception of the importance of education itself among male athletes.

Another dependence focused on whether the importance of education is influenced by the athlete’s engagement in individual or team sports. The calculated value for the Chi-square test (18.756) exceeded the critical value (9.488) at four degrees of freedom. The same positive result was confirmed even by the T-test (

Table 6. Dependence of the perception of the importance of education on the type of sport (own elaboration).

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

). This finding confirms that there is a statistically significant dependence between the type of sport (individual or team) and the attitude towards the importance of education.

The total number of respondents was 236, of which 43 (18.1%) were engaged in individual sports and 193 (81.9%) in team sports. To identify the direction of dependence, a chart was created, as shown in Figure 8. The results confirm that individual athletes attach greater importance to education than team athletes. Up to 93% of individual athletes considered education important or very important. Among team athletes, only 58.6% considered education important or very important, which was significantly less than in the case of individual athletes.

Hypothesis H4 was therefore confirmed, as individual athletes perceived education as more important. From this perspective, it is necessary to focus on supporting the perception of importance, especially among team athletes, when setting athlete education processes with the application of AI and Big Data.

The last relationship studied was the one between the importance of education and the attractiveness of AI for individual respondents. A total of 224 responses were analysed here, since the answer “cannot say” (selected by 12 athletes) was excluded due to irrelevance to the results studied. The calculated value for the Chi-square test (26.660) slightly exceeded the critical value (26.296) with 16 degrees of freedom. This finding confirms that there was a statistically significant dependence between the attractiveness of AI as a form of education and the attitude towards the importance of education. However, the strength of the dependence was not that high. Nonetheless, the attractiveness of AI may influence how people perceive the importance of education in the future. Or, conversely, the perception of the importance of education may be related to how attractive AI appears to respondents. This was also confirmed by the result of Cramer’s V (0.172; significance confirmed by the p-value of 0.045). The fact that the tests revealed only a weak existing dependence between the investigated variables indicates the possible influence of other variables that were not included in our research. It is possible to build on the results of the widely respected self-determination theory [68] and, in the future, include the intrinsic motivation of students in the investigation of the relationship between the attractiveness of specific educational methods and the perceived importance of education as such. We can use this theory similarly to what was considered in the case of leadership development in 2015 [69]. However, the significance value of Cramer’s V test still indicates confirmation of the stated hypothesis. The addition of other statistical tests to support our findings in the whole analysis of the questionnaire data was deliberate. It aimed at reaching a sufficient level of sensitivity.

To identify the direction of the dependence, a chart was created, captured in Figure 9. AI is considered an attractive form of education by those respondents who attach higher importance to their learning and education so that they are better prepared for a future career after sports. However, it is not a strong dependence, so the individual results differ only minimally.

Based on all the findings presented above, we assume that, since AI has been present in the field of education for only a relatively short time, respondents are not yet able to fully assess its importance and impact in relation to this process. We further assume that, over time, the attractiveness of AI will increase in all relevant areas. The presented results therefore confirm the validity of hypothesis H5.

5. Discussion

In our study we identified that the application of Big Data and AI in athletes’ education can bring considerable improvements in personalised learning and performance monitoring. Our research showed that the attractiveness of AI is now only partially perceived among young athletes in Slovakia. However, Slovakia is not yet using the full potential of this form of education, still being in the initial phase of adoption of this technology. We see significant room for improvement here. Research by Wang et al. [28] can be an inspiration, confirming that approximately 43% of university students in the United States use AI tools such as ChatGPT for their studies. This is a significant use of AI technologies among students. Also, approximately 50% of educators incorporate AI into the development of their educational process, thus demonstrating the growing acceptance of AI tools in teaching practices. Adaptive learning technologies powered by AI have improved students’ test scores by up to 62%. The general use of AI in educational contexts is associated with a 30% improvement in students’ performance and a 20% reduction in students’ anxiety levels. Given the low awareness and partial attractiveness of AI among athletes, it is necessary to increase education in this area, which confirms the need for targeted educational programs and workshops. In the future, specific methods need to be designed that facilitate the use of AI for educational purposes and increase awareness of its benefits. This way, the new system of athletes’ education will be compatible with current developments and tools used in employee development in organisations in general [70,71,72,73,74].

The results also show that most athletes consider education important and are fully aware of the benefits of education even after their sports careers. This finding is supported by a Spanish study stating that an academic career can help athletes relieve tension. This restlessness is a natural part of athletic life and the search for personal balance [75]. According to Devaney [76], education plays a key role for elite and professional athletes as a backup plan for life after their sports careers. On the other hand, it also provides them with a way to improve their performance and cope with stress during their active sports careers. Education broadens athletes’ horizons, develops their self-identity outside sports, and promotes transferable skills (applicable in other fields). With this form of preparation, athletes will be suitable candidates for the labour market and will be able to find employment in modern organisations, for which continuous development following multiple aspects of overall sustainability is a respected value [77].

In relation to the gender difference of athletes and their attitude towards education, it can be concluded that education is currently considered more important by women. This finding is reflected in the statistics of educational level. In many industrialised countries, women have surpassed men in achieving higher education [78]. Another study also confirmed that women usually achieve better academic results and have higher motivation in the field of education [79]. These statements are supported by our results too. These show that women consider education to be more important than men and attach greater importance to it for their future career growth. A valuable finding is the dependence between the type of sport and the perception of the importance of education, where individual athletes perceive education as more important than collective athletes. This may be a consequence of the greater pressure on personal responsibility and self-education, which is more common in individual sports. Our study revealed differences between different groups of respondents. It thus follows the need to identify significant differences between target groups that occur in various domains and environments [80,81,82]. All the knowledge gained from this study, after confrontation with the results of other relevant research projects, should enter the development of a systematic approach to the education of active athletes for their better preparation for a career after sports. However, this approach must be appropriately set and supported strategically within the national and international sports management [83,84,85].

6. Conclusions

Our article focused on identifying the possibilities for effective education of athletes during their current sports career, which would prepare them for life outside of sports. To set an effective way of educating athletes, it is appropriate to use various educational programs. Based on the currently used AI tools and the use of Big Data, we evaluated the possible applications in the field of athletes’ education. The connection of AI and Big Data technology with the setting of effective education of athletes is multifaceted. Appropriate utilisation of AI is one of the possible educational methods. Collection and analysis of Big Data related to the education of athletes represent a way to evaluate the achieved efficiency and set steps for its increase. Finally, to support the employment of former athletes in the labour market outside of sports, skills associated with the use of AI and Big Data represent a significant competitive advantage. We analysed the state of perception of education’s importance using a questionnaire survey conducted on a sample of young athletes aged 14 to 19. The results were evaluated for a sample of 237 respondents. The findings indicate a certain interest in education during athletes’ sports career to prepare for life after it. Via a content analysis of selected available sources, we found insufficient utilisation of AI and Big Data in the current education of athletes in Slovakia and around the world.

The use of personalised learning systems, self-confidence tools, and financial planning can significantly help athletes with their future career transitions. Based on the findings presented, we propose several recommendations for sports associations and clubs as follows:

• 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.

One of the limitations of our study is the geographical distribution of respondents within Slovakia, as most of them came from central part of the country. Another limitation is the focus mostly on quantitative data. Focusing specifically on youth athletes and not on current professionals can be considered limiting as well. From the methodological perspective, the application of specific statistical tests can also be seen as a limitation. This directly follows the capability and explanatory power of the selected tests. Since the questionnaire survey was designed to map the preferences of the specific group of learners towards forms of learning, more attention could not be paid strictly to just the concepts of big data and AI in learning.

In the framework of further research, wider regional coverage would be appropriate, which would more relevantly evaluate the perception of individual factors in the questionnaire survey. This would include, e.g., expansion within the regions of Slovakia, but also expansion of the research to other countries around the world. In the future, the research should also focus more on current professional athletes. It would be beneficial to also include qualitative data obtained via in-depth interviews with relevant respondents. A suitable step in future research would also be the creation of focus groups that would specialise directly in the application of AI and Big Data in the athletes’ education. These could provide more detailed data on the educational needs of athletes and possible barriers of introducing new technologies into this domain. Due to the identified contradictions in our research results, e.g., the detected high educational importance in general but low interest of athlete students in using AI in their learning, future research can also be focused on interconnections of this topic with the results from the career transition theory (based on research outside of sports). Another important perspective to be investigated more thoroughly in the future is the combination of our results with the gender research literature. Based on future research, it will be possible to support the specific creation of effective methods of education for athletes throughout their careers.