Organizing and Planning My Studies with ChatGPT

4

October

2025

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First, when I started using ChatGPT, I mainly used it as a tool to answer questions or explain theory and concepts. But over time, I discovered another use of it: helping me plan the workload of my studies. This has made a big difference in how I organize my time and approach my studying.

The part I struggled with most was not knowing where to start before an exam. I often felt overwhelmed by all the different things I had to do. Here, I found a lot of assistance in ChatGPT. I would ask if it could create a study schedule by breaking down all the different subjects and how much time I should spend on each one. The schedule could get as personalized as I wanted, I could include how much time I had before exams, which subjects I struggled with more, how busy I was on particular days, and the tool would create a customized schedule. It helped me prioritize tasks as well.

Of course, not everything was perfect. I noticed ChatGPT sometimes created schedules that were too optimistic. When this happened, it suggested much more study material than I could actually manage in a day. Then I had to adjust the plan, but this could as well be easily done with the help of the tool.

In addition, ChatGPT was also useful for helping me practice the material. It would not only suggest studying a certain chapter, but it would as well help me practice the material by generating questions. This kept my studying more active and efficient.

Overall, ChatGPT really helped me with organizing and structuring my studies. It gave me a clear starting point and therefore made the whole process of studying feel less chaotic and more manageable. This kind of clarity allowed me to work towards exams with a more relaxed and confident feeling.

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Personalization through Algorithms: Inclusive or Limiting?

25

September

2025

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From TikTok’s “For You Page” to Spotify’s customized playlists, algorithms nowadays drive personalization on almost every major online platform. Such algorithms offer several advantages to users by helping them discover relevant content, reducing choice overload, and increasing engagement. It shapes what we listen to, watch, and shop. However, in reality, does it actually empower us or does it limit us in ways we are unaware of?

The advantages are quite obvious. Algorithms help people discover new and relevant content like movies, videos, songs, and more. On TikTok and Spotify, they give unknown creators and artists a chance to reach many people. Or on Netflix, due to the recommendations, consumers no longer have to scroll endlessly to find a movie they might not have found themselves. For users, this means greater convenience, less choice overload, and more time savings. And for companies, it increases customer engagement, loyalty and revenue.

However, these benefits also come with risks. On a long-term basis it can reduce users’ abilities in preference learning and independent decision-making (Rafieian & Zuo, 2024). In addition, even well-designed algorithms can unintentionally disadvantage specific groups, thereby reinforcing inequalities (Ascarza & Israeli, 2022). For example, in 2019 Apple’s algorithms used to set credit card limits offered women lower limits then men with similar profiles (BBC News, 2019).

Personally, I enjoy and benefit a lot from personalization through algorithms. I frequently discover new items that I would not have found on my own, such as music or clothing. However, I also notice that my choices are strongly influenced by these recommendations. Instead of searching for it myself, I often end up watching or purchasing something based on these suggestions. Therefore, I believe that personalization is most valuable when it is diverse. Occasionally, it doesn’t hurt to show things outside people’s usual preferences, it can create new interests and broaden perspectives. Algorithms should guide people in what they discover, not control it.

References

Ascarza, E., & Israeli, A. (2022). Eliminating unintended bias in personalized policies using bias-eliminating adapted trees (BEAT). Proceedings of the National Academy of Sciences, 119(11). https://doi.org/10.1073/pnas.2115293119

BBC News. (2019, November 11). Apple’s “sexist” credit card investigated by US regulator. https://www.bbc.com/news/business-50365609

Rafieian, O., & Zuo, S. (2024). Personalization, Algorithmic Dependence, and Learning. Cornell SC Johnson College of Business Research Paper (forthcoming). SSRN. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5037671

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