Educational and Professional Perspectives on AI
Over the past few weeks, I’ve been thinking a lot more about what it actually means to use AI “professionally,” and how different that feels compared to how I’ve used it as a student. Before this course and our inquiry project, AI tools like ChatGPT were mostly just something I’d use to explain concepts, help debug, or speed up smaller tasks. It was useful, but pretty informal. Through our learning pod discussions and the direction of our inquiry, I started to see that this same tool exists in a completely different context in the workplace—where the expectations, risks, and outcomes are much more real.
Part of this shift came from reflecting on my own experience during my co-op. While learning on the job, there was some Microsoft Copilot capabilities that were adapted in the workplace, but even without heavily relying on AI at the time, there was already a strong expectation that work was accurately and clearly communicated, and aligned with business needs. Looking back now, it’s clear that if generative AI were introduced into that workflow like it is being utilized today, it would require a much higher level of awareness around how the tool is being used, what data is being handled, and how reliable the outputs are. Much of these conversations continue to grow as companies shift toward AI-driven solutions.
This is something we have touched on in our learning pod as well. One of the biggest differences between student and professional AI use is accountability. As a student, if I use AI and something is slightly off, the impact is usually limited to my own learning or a grade. In a professional setting, that same mistake could affect a report, a decision, or how information is communicated across a team. There’s a shift from low-stakes experimentation to high-stakes responsibility. AI becomes less of a convenience tool and more of something that has to be used carefully and intentionally.
Another layer that stood out to me is how AI is actually being integrated into workflows, not just used on the side. Tools like Microsoft Copilot are embedded into meetings, documents, and communication platforms. These can typically generate automatic summaries, transcripts, and insights at ease. During my internship, I saw early versions of this in action with tools that generated meeting notes or helped navigate large amounts of information. At the time, I saw it mainly as a productivity boost, but now I also see the accessibility side of it. Features like captions, transcripts, and summaries can make information more available to more people, reducing barriers in ways that aren’t always obvious.
At the same time, these tools aren’t perfect. Summaries can miss context, transcripts can be inaccurate, and automated outputs can oversimplify complex information. This reinforces something that keeps coming up in both our course and inquiry: digital literacy isn’t just about using tools, it’s about understanding their limitations. In a professional environment, that means verifying outputs, being aware of data sensitivity, and recognizing when AI should or shouldn’t be used.
What I find most interesting is how this all connects back to being a student right now. Through our inquiry project, we’ve been focused on using AI to improve resumes and portfolios through translating our work into something more professional. But this reflection made me realize that learning how to use AI responsibly is just as important as learning how to use it effectively.
Overall, this has given me a clearer picture of what that transition looks like. As a student, AI is a tool for learning and exploration. In the professional world, it becomes a tool that directly impacts quality, communication, and decision-making. Being able to navigate that shift, and understanding both the benefits and the responsibility that comes with it, is something I see as a key part of digital literacy moving forward.