INTRODUCTION

AI is becoming a big part of sports. Fans see it in things like instant replay, stats, and highlights, but it’s also used behind the scenes for scouting, training, preventing injuries, and recruiting. When people think about AI in sports, they usually think about big teams like major football programs or pro leagues that already have a lot of money.

But AI isn’t fair for everyone. Smaller schools, lower divisions, and women’s sports don’t have as much money, so they don’t get to use the same technology. At the same time, they could actually benefit a lot from AI by helping athletes train better, stay healthy, and make smarter decisions (Du & Bi, 2025; Antalamarad & Upadhye, 2024).

This chapter looks at how AI is used in sports and why not everyone benefits equally. It focuses on smaller schools and women’s sports instead of just big teams. It also asks a simple question: when AI is used in sports, who benefits and who gets left out?

CONNECTION TO STS

Science and Technology Studies (STS) shows that technology isn’t just tools that make things better. It’s shaped by society and also affects society. This matters for AI in sports because it’s built using data, created by companies, and used more by teams with money and power.

One key idea in STS is that technology can increase unfairness when not everyone has access. Big programs can afford better staff and tech, while smaller schools and many women’s teams cannot. STS also shows that data isn’t perfect (Ethical Implications of Artificial Intelligence in Sport [EIAIS], 2025). If AI is trained mostly on men’s sports or big programs, it may not work as well for others (EIAIS, 2025). This can lead to players being overlooked or misjudged.

Finally, STS brings up questions about privacy. AI tracks a lot of athlete data, so it’s important to ask who owns it, how it’s used, and if athletes can say no without consequences. Athletes may feel pressure to share their data to stay on the team, even if they are not fully comfortable with it.

INFORMATION ON THE TOPIC

1. AI for performance and training

AI is used a lot to help athletes train better (Du & Bi, 2025). Teams use wearables to track things like speed, movement, and how the body handles stress. AI can look at this data and suggest changes or warn when an athlete might be at risk of injury (Antalamarad & Upadhye, 2024).

This could really help smaller schools and women’s teams by making coaching decisions easier. But the problem is that these tools cost money, need subscriptions, and take time to understand. Because of this, bigger programs still get most of the benefits.

2. AI for injury prevention

AI is also used to help prevent injuries by spotting patterns that could lead to problems (Blondin et al., 2025). For example, AI can track how an athlete moves and notice small changes that might lead to injuries over time. This is very important in women’s sports, especially with injuries like ACL tears, which happen more often in female athletes.

However, if AI systems are mostly trained on men’s data, they might miss patterns that are more common in women athletes (EIAIS, 2025). Smaller schools also don’t always have enough trainers or medical staff, so even if they have the technology, they may not be able to use it in the best way. This means the athletes who could benefit the most may still be at risk.

3. AI in recruiting and scouting

AI is starting to change recruiting by helping coaches watch film, compare players, and rank athletes (Huempfner, 2025). It can quickly go through large amounts of game footage and data, which saves time and helps teams find talent more efficiently.

This can help smaller schools discover players they might not have seen before. At the same time, it can also be unfair. Athletes with better video, better stats, or from bigger programs may stand out more because they have more data available. In women’s sports, there is often less data and less media coverage, which makes the AI less accurate and can limit opportunities even more (Shrivastav, 2023).

CONCLUSION

AI is changing sports in a lot of ways, not just with stats and highlights. It helps with training, preventing injuries, recruiting players, and getting teams more media attention. But not everyone benefits from AI the same way. Smaller schools and women’s sports often don’t have as much access because they have less money, less data, and less representation.

From a Science, Technology, and Society (STS) point of view, AI isn’t just a neutral tool. It reflects what society values, like which sports and athletes get the most attention and resources. As AI keeps growing in sports, it’s important to make sure women athletes, smaller schools, and student-athletes are included in decisions about how it’s used.

A fairer future with AI in sports is possible, but only if it’s built and used in a way that focuses on fairness, representation, and protecting athletes.

REFERENCES

Antalamarad, N. M., & Upadhye, J. (2024). Role of artificial intelligence (AI) in sports. ITM Web of Conferences, 68, 01004. https://doi.org/10.1051/itmconf/20246801004

Blondin, M. J., Fister Jr., I., & Pardalos, P. M. (Eds.). (2025). Artificial intelligence, optimization, and data sciences in sports (1st ed.). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-76047-1

Du, T., & Bi, N. (2025). Application of artificial intelligence advances in athletics industry: A review. Concurrency and Computation: Practice and Experience, 37, e8372. https://doi.org/10.1002/cpe.8372

Ethical implications of artificial intelligence in sport: A systematic scoping review. (2025). Bioengineering, 12(8), 887. https://www.mdpi.com/2306-5354/12/8/887

Huempfner, J. (2025). College sports are embracing AI: The digital era of athletics. ASM Sports. https://arxiv.org/abs/2510.15487

Shrivastav, V. S. (2023, November 2). The AI advantage: Technology driving innovation in women’s sports. Forbes. https://www.forbes.com

AI ACKNOWLEDGMENT

I acknowledge the use of ChatGPT (https://chat.openai.com) to assist in editing, revising, and updating this chapter. The prompts used included “Create an outline of possible edits/ changes to improve the chapter,” “Fix areas in the chapter that could be extended upon,” and “Create proper APA citations for the following sources.” The output from these prompts was used to enhance clarity, suggest changes, and improve citation quality.