Infinity AI LabsxSMSB

Most student athletes have used an AI chat tool, but few understand what's actually happening when they type a prompt, or why the same question can get a different answer twice. This section covers what these tools are good at (drafting, summarizing, explaining, brainstorming) and where they fall apart (facts, math, anything that needs real judgment). Everything else in the seminar builds on getting this part right first.

AI is already showing up in medicine, construction, retail, design, and nearly everywhere else, not just in tech jobs. This section is less about coding and more about a mindset: whatever career a student athlete ends up in, knowing how to use AI well is going to be part of it.

AI tools remember what you type, and that data doesn't always stay private. This section covers a simple rule: if you wouldn't post it publicly, don't type it into an AI chat either, whether that's a password, an address, or something personal you're working through.

Deepfake video and AI-generated images are already showing up in student athletes' feeds, often with no label saying so. This section walks through the tells (odd hands, mismatched lighting, text that reads a little too smooth) using real examples student athletes can practice on. The goal is a habit of pausing before sharing or believing something, not paranoia about everything they see.

It's easy to let AI write the whole thing, or answer the whole question, without ever deciding whether that's actually what you wanted. This section builds the habit of asking first: am I using this to think better, or to avoid thinking at all. Small, practical examples, not a lecture.

Student athletes practice answering real interview questions with AI playing the other side, then get specific feedback on what landed and what didn't. The same approach works whether they're walking into a college interview, a first job interview, or pitching a coach or teacher on an idea. It's rehearsal, not a script to memorize.

The better AI gets at writing and answering questions, the more the skills it can't touch start to matter: leading a team, earning someone's trust, reading a room, making a hard call under pressure. For a group of student athletes, that's familiar ground, it's the same stuff that makes a good teammate or a good captain.

Before a tough conversation, AI can help a student athlete think through what they actually want to say and how it might land, without putting words in their mouth. That covers asking for a grade to be reconsidered, telling a coach about an injury, or bringing up something difficult at home. The conversation still happens in person, AI is just the rehearsal space.

A first paycheck is usually the first time gross pay, taxes, and deductions stop being abstract. This section uses AI to walk through an actual pay stub, line by line, so student athletes understand where the gap between the offer and the deposit actually goes. Practical, not a finance class, just enough to not be surprised.

Student athletes already rewrite the same idea for different audiences: a school paper, a presentation, a caption for a post. This section shows how AI can help adapt one idea across formats without losing what made it good in the first place, saving time on the rewrite instead of outsourcing the thinking.

Not every AI tool is built the same way, and not every answer it gives is right. This section covers a few quick questions to ask before trusting a tool or its answer: is this the right tool for the job, and can I verify this somewhere else before I act on it.

AI learns from existing data, and existing data carries existing unfairness with it, which is part of why facial recognition tools have misidentified people and why some hiring tools have screened out qualified candidates. This section is about recognizing that AI isn't neutral just because it's a computer, and asking who might get left out before trusting it with something that matters.

A lot of AI tools were built by training on other people's writing, art, and video, often without asking first. This section covers why that matters, especially for student athletes who make their own content, and what questions are worth asking about where a tool's answers actually come from.