Critical Thinking and Equity in Digital Spaces
This week’s focus on critical thinking and equity in digital spaces made me take a step back from how I’ve been using technology, especially AI, and think more about what’s happening underneath it. Comparing this topic to my inquiry project, a lot of my attention has been on utilizing AI tools to improve resume structure, make resumes clearer, and using these tools and prompts more efficiently. This weeks sources on equity and advancements in technology have made me reflect less about the outputs these tools present, but more about how these tools work in education and what assumptions are built into them as these tools are becoming more and more public.
Maha Bali: The Invisible Inequities of Data
One of the biggest ideas through Maha Bali’s lecture that stood out to me was the concept that digital systems are not neutral. Algorithms and AI tools are trained on data, and that data doesn’t represent everyone equally. When systems are designed around an “average” user, they often end up excluding people who don’t fit that mold. That idea stuck with me because it challenges the opinion that optimizing for a particular system is always a good thing. In reality, it might mean adapting to a structure that already has biases built into it and has those assumptions already in place. This kind of misrepresentation in many cases can lead to “cultural injustice” and reproduces stereotypes in many social media circuits and can often underrepresent cultures that may not have the same data presence as others
This connects closely to how we’ve been using AI in our inquiry project. A big part of our work has been testing tools like ChatGPT, Gemini, and Copilot to improve resumes and portfolios. I started thinking more critically about what “improvement” actually means. If AI is trained on a certain type of resume style or language, then optimizing with it might push everyone toward the same standardized version of “professional”, and while that standard still holds for a particular resume, conversations I have had with my pod as well as my interview with our industry professional state that there is more to building a digital profile than what is said on a general resume.
Sarah Elaine Eaton: Future Learning and AI
Interested in AI and the future of education, I watched the start of Sarah Elaine Eaton’s lecture on Artificial Intelligence and Future Focused Learning. An important idea from this topic was around the shift toward what was described as a “post-plagiarism” world. AI is becoming a normal part of how people write, create, and communicate. This aligns with how we approached our inquiry, where we used AI as a tool to support and refine our work, not replace it. It also highlights the importance of responsibility. Even if AI helps generate better wording, the final product still represents you. If something is inaccurate or exaggerated, that responsibility doesn’t shift to the tool.
This is where critical thinking becomes essential. Throughout our testing, we saw how easy it was for AI to slightly change the meaning of experience by making it sound more technical or more impactful. Without careful review, it would be easy to accept those changes just because they sound better. But this module reinforced that digital literacy isn’t just about using the tools, but to also challenge, question, and reflect on them, and whether it reflects your actual experience and intent.
Equity also plays a role in how these tools are accessed and used. Not everyone has the same familiarity with AI tools, the same level of guidance, or the same ability to critically evaluate outputs. This creates differences in how effectively people can use these technologies, especially in high-stakes contexts like job applications. In that sense, digital literacy becomes a form of advantage, and without it, these tools can widen existing gaps rather than reduce them.
Reflecting Back on our Inquiry Topic
Relating this back to our inquiry, these sources have helped validate that our project is not just about improving resumes, but also understanding the systems we’re optimizing for. Using AI to refine a resume is useful, but it needs to be done with awareness. There’s a balance between adapting to digital systems and maintaining authenticity, and that balance requires both technical skill and critical judgment.
Overall, this reflection has made me realize that critical thinking and equity are core parts of digital literacy. It’s not enough to know how to use AI tools effectively. As a learner and inquirer, you also need to understand their limitations, question their outputs, and recognize the broader impact they have on how people are represented in digital spaces.
References
Bali, M. (2026, March 17). Week 11: Critical thinking & equity in digital spaces [Guest lecture]. Hosted by Valerie Irvine at the University of Victoria. https://edtechuvic.ca/edci136/2026/03/17/week-11-critical-thinking-equity-in-digital-spaces/
Siemens, A. (2026, March 27). Inquiry Post 3: Beyond the Resume. https://aidensiemens.opened.ca/inquiry-post-3-beyond-the-resume/
Eaton, S. E. (2025). Artificial intelligence and future-focused learning [Distinguished Research Lecture]. Werklund School of Education, University of Calgary. https://werklund.ucalgary.ca/about/recognition/distinguished-research-lecture-dr-sarah-eaton