Review Research Faster: A Demonstration of Sports Science AI

In this post, I demonstrate Sports Science AI, a platform that provides generative AI-powered literature reviews.

As detailed in our AI Dictionary, Artificial Intelligence (AI) is the ability of a digital computer (or computer-controlled robot) to perform tasks commonly associated with intelligent beings. The term is frequently applied to the project of developing systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalise, or learn from past experience.

I strongly believe AI provides sports science practitioners with the opportunity to work more efficiently and effectively. In a previous video, I demonstrated five AI platforms that every sports scientist should consider incorporating into their toolkit. In this post (and associated video) I dive deeper into one of those tools: Sports Science AI.

Developed by Kevin Yven, this AI-powered platform leverages a custom database of over 111,000 sports science publications (and counting) to support faster, more relevant literature reviews and seamless interaction with PDFs.

Whether you're writing a research proposal, planning athlete testing, or trying to stay up to date with best practice, this tool can help you get from question to evidence in minutes. Let's take a closer look at what Sports Science AI can do.

The Sports Science AI Platform

Once you log in to Sports Science AI, you’re greeted by a simple, intuitive dashboard. The updated interface offers two key features:

• Literature Review – an AI-powered chatbot that answers your questions with inline citations from its database of sports science papers

• Chat with PDF – a feature that allows you to upload papers and interact with them conversationally

Kevin has offered the Global Performance Insights community 20% off your first 3 months of Sports Science AI PRO with the code  JOCLUBB2025

This is available for both individual and team plans, and on top of current discounts also applied.

► Try Sports Science AI PRO

Let's take a look at both these features in action 👇

Literature Review in Action

To demonstrate how it works, I asked the chatbot a broad question: "What are the effects of cold water immersion on athletic recovery?"

Despite the scope of the question, the platform returned a coherent and balanced summary, highlighting effects on endurance, sprint and jump performance, protocol variation, and key temperature/time recommendations. The citations were included inline and listed below the answer, and crucially, each one was clickable—taking me directly to the source article.

🧠 No hallucinations: Because Sports Science AI draws only from verified papers in its database, you're not dealing with made-up references. You can check each source with a single click.

This conversational interface is ideal for refining your review. For example, I followed up by asking "What about in soccer?"—without re-explaining the topic. The AI understood the context and returned a soccer-specific summary with new references.

To dive deeper, you can also filter results by Publication year, Citation count, and Sports.

These filters help you prioritise highly cited or recent evidence for a more efficient review process.

Chat with PDF: From Summary to Insight

Next, I demonstrated the “Chat with PDF” feature using one of the papers surfaced in the literature review. Uploading was quick, and the tool immediately provided a high-level summary, in this case outlining the participant numbers and intervention groups, as well as the key results, namely reduced muscle damage and soreness with cold water immersion.

From there, I used both suggested prompts and free-text questions to dive deeper into the study’s methodology and limitations. But of course, this is just a quick demonstration and there's a multitude of other queries we could ask of the study.

This conversational approach makes it easier to understand a study's design and quality without rereading it multiple times.

🔄 Coming soon on Sports Science AI: A platform update will allow automatic fetching of open-access PDFs. For paywalled content, manual upload will remain an option.

Another feature on the horizon is shared team libraries, allowing organisations to build and collaborate on topic-specific evidence bases—a valuable addition for performance departments or research groups. You can find more info on the Team Plan here.

Final Thoughts

Sports Science AI helps you move beyond static search engines or manual research, and into dynamic, conversational exploration. Whether you're asking broad questions or reviewing specific study PDFs, it enables a fast, trustworthy, and interactive experience that fits into busy applied practice.

If you're interested in trying it out, Sports Science AI offers a 20% discount for Global Performance Insights viewers. Use the code JOCLUBB2025 for up to three months off on any plan, including team packages. Try it here.

Frequently Asked Questions (FAQs)

What is a large language model?

A type of artificial intelligence (AI) that can understand and generate human-like responses, often using a very large dataset of text for training. These models, typically built using machine learning techniques, are trained on vast amounts of data, enabling them to recognise patterns in language and produce coherent answers.

What is Sports Science AI?

Sports Science AI is a chatbot platform built specifically for the sports science field, offering evidence-based answers from a database of over 111,000 research papers and counting!

What should I be cautious of when using AI tools?

Always validate the references and information provided by these tools. Despite enhancements, AI can still produce errors or misinterpretations (known as hallucinations), especially with complex queries. Sports Science AI provides live link to the source page so you can verify the citations.

Why is data security important when using AI tools?

Data security is paramount in safeguarding sensitive research information. AI tools require careful handling of raw data input to ensure data privacy and integrity, as they are a prime target for cyberattacks and data breaches. Protecting this data ensures the integrity of AI models, prevents unauthorised access to sensitive information, and maintains compliance with data protection regulations.

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For more insights and explorations into these AI tools and more, make sure to follow me on YouTube and check out the Sports Science Tutorials playlist.