Host: Welcome to A.I.S. Insights — powered by Living Knowledge. In a world where artificial intelligence is reshaping every industry, how do we ensure our teams are truly ready? Today, we're diving into a fascinating new study titled "Measuring AI Literacy of Future Knowledge Workers: A Mediated Model of AI Experience and AI Knowledge."

Host: It explores how we, as professionals, develop the crucial skill of AI literacy. And to help us unpack it, we have our expert analyst, Alex Ian Sutherland. Alex, welcome.

Expert: Thanks for having me, Anna. This is a topic that's incredibly relevant right now.

Host: Absolutely. Let's start with the big picture. What's the real-world problem this study is trying to solve? It seems like everyone is using AI, so isn't that enough?

Expert: That's the exact question the study addresses. The problem is that as AI becomes a standard tool, like email or spreadsheets, simply knowing how to prompt a chatbot isn't enough. Professionals, especially knowledge workers who deal with complex, creative, and analytical tasks, need a deeper understanding.

Expert: Without this deeper AI literacy, they risk misinterpreting AI-generated outputs, being blind to potential biases, or missing opportunities for real innovation. The study points out there’s a major gap in how we train people, making it hard for companies and universities to build effective programs for the future workforce.

Host: So there's a difference between using AI and truly understanding it. How did the researchers go about measuring that gap? What was their approach?

Expert: They took a very practical approach. They surveyed 352 business school master's students—essentially, the next generation of knowledge workers who are already using these tools in their studies and internships.

Expert: They didn't just ask, "Do you know AI?" They measured three distinct things: their hands-on experience using AI tools, their experience trying to design or configure AI systems, and their structured, theoretical knowledge about how AI works. Then, they used statistical analysis to understand how these pieces fit together to build true proficiency.

Host: And that brings us to the findings. What did they discover?

Expert: This is where it gets really interesting, Anna. The first key finding challenges a common assumption. Hands-on experience is vital, but it doesn't directly translate into AI proficiency.

Host: Wait, so just using AI tools more and more doesn't automatically make you better at leveraging them strategically?

Expert: Exactly. The study found that experience acts as a raw ingredient. Its main role is to build a foundation of actual AI knowledge—understanding the concepts, the limitations, the "why" behind the "what." It's that structured knowledge that acts as the critical bridge, turning raw experience into true AI literacy.

Host: So, experience builds knowledge, and knowledge builds literacy. It’s a multi-step process.

Expert: Precisely. And the second major finding is about the *type* of experience that matters most. The study revealed that experience in designing or configuring an AI system—even in a small way—has a significantly stronger impact on developing literacy than just passively using a tool.

Host: That makes a lot of sense. Getting under the hood is more powerful than just driving the car.

Expert: That's a perfect analogy.

Host: This is the most important question for our listeners, Alex. What are the key business takeaways? How can a manager or a company leader apply these insights?

Expert: The implications are very clear. First, companies need to rethink their AI training. Simply handing out a license for an AI tool and a one-page user guide is not going to create an AI-literate workforce. Training must combine practical, hands-on projects with structured learning about how AI actually works, its ethical implications, and its strategic potential.

Host: So it's about blending the practical with the theoretical.

Expert: Yes. Second, for leaders, it's about fostering a culture of active experimentation. The study showed that "design experience" is a powerful accelerator. This doesn't mean every employee needs to become a coder. It could mean encouraging teams to use no-code platforms to build simple AI models, to customize workflows, or to engage in sophisticated prompt engineering. Empowering them to be creators, not just consumers of AI, will pay huge dividends.

Expert: And finally, for any professional listening, the message is to be proactive. Don't just use AI to complete a task. Ask why it gave you a certain output. Tinker with the settings. Try to build something small. That active engagement is your fastest path to becoming truly AI-literate and, ultimately, more valuable in your career.

Host: Fantastic insights, Alex. So, to recap for our audience: true AI literacy is more than just usage; it requires deep knowledge. Practical experience is the fuel, but structured knowledge is the engine that creates proficiency. And encouraging your teams to not just use, but to actively build and experiment with AI, is the key to unlocking its true potential.

Host: Alex, thank you so much for breaking this down for us.

Expert: My pleasure, Anna.

Host: And a big thank you to our listeners for tuning into A.I.S. Insights — powered by Living Knowledge. We'll see you next time.