Week 5 Reflection Post

This week’s guest lecture with Dr. Irvine brought up topics I hadn’t really thought about together before. We discussed digital learning tools alongside the privacy and ethical questions that come with them, and it left me reflecting on what we’re actually accepting when we move education online.

Data privacy in digital education

In May 2026, students at UBC and SFU had their personal information compromised through a Canvas data breach. Canvas is deeply embedded in student life, It is where assignments are submitted, grades are tracked, and communication with professors happens. A breach like this affects students whose academic and personal data was exposed. Most of us accept these platforms without much thought about what we’re agreeing to. The convenience makes sense. But I think institutions and platforms carry more responsibility to be transparent about the risks that come with digital learning environments.

Copyright and the Ghibli trend

The Studio Ghibli AI trend from last year is something I keep coming back to. People were generating images in Ghibli’s art style by uploading their own photos, and it was largely treated as a “fun internet trend”. However, I think it raised two serious concerns.

The first is straightforward: users were sharing personal photos with an AI tool without much clarity on how that data would be stored or used. The second is about the artists whose work made those outputs possible. Studio Ghibli’s visual style came from years of skilled, deliberate creative work. The idea that this can be replicated at scale by a model trained on that work, without the artists’ knowledge or compensation, is something I find ethically troubling. It connects directly to the copyright discussions in this course, and I don’t think those conversations are happening loudly enough in the context of AI.

What actually happens when AI does the thinking

There is a broader concern I have about how AI is being used in learning. Students can upload their lecture slides and receive a clear, organized summary within seconds. Certain AI tools can even generate realistic handwriting from typed text. The academic integrity issues are obvious, but the subtler problem interests me more.

Working through course material yourself, including the parts that are slow or confusing, is part of how understanding actually develops. A summary produced by AI might be accurate, but accuracy and comprehension are not the same thing. When you skip the process of engaging with material directly, you miss the part where it becomes your own knowledge. Klimova and Pikhart’s 2025 review in Frontiers in Psychology found that AI use in higher education has been linked to diminished interpersonal skills, social isolation, and anxiety in students. The well-being side of this tends to get less attention than the academic integrity side, but it matters just as much.

Productivity and what we are optimizing for

AI is often framed as a tool for amplifying human potential and increasing productivity. I think that framing is worth questioning in an educational context. Productivity means more output in less time, and that is genuinely valuable in many professional settings. But education is not primarily about output. It is about building knowledge and developing the ability to think critically and solve problems. A student who gets through their coursework faster with AI is not necessarily learning faster. They are producing faster.

I don’t think AI is without value in education. Some uses genuinely support learning. But we are adopting these tools quickly, and the critical conversation about what we are giving up has not kept pace. That feels like something worth paying attention to.

References

CBC News. (2026, May). UBC and SFU Canvas cyber breach. https://www.cbc.ca/news/canada/british-columbia/ubc-sfu-canvas-cyber-breach-9.7191972

DiPlacido, D. (2025, March 27). The AI-generated Studio Ghibli trend explained. Forbes. https://www.forbes.com/sites/danidiplacido/2025/03/27/the-ai-generated-studio-ghibli-trend-explained

Google. (2026). Nano Banana Pro. https://blog.google/innovation-and-ai/products/nano-banana-pro

Klimova, B., & Pikhart, M. (2025). Exploring the effects of artificial intelligence on student and academic well-being in higher education: A mini-review. Frontiers in Psychology, 16, 1498132. https://doi.org/10.3389/fpsyg.2025.1498132

Stanford News. (2026, April). Digital chores and productivity research. https://news.stanford.edu/stories/2026/04/digital-chores-productivity-boost-research