Live like a jellyfish

The toolkit developed by Gulbahar et al. (2017) was really interesting. An instructor can just type their lesson goals into the toolkit. Then, it recommends the best social media tools and even helps design the lesson. While reading this paper, I thought that adding generative AI technology would make this toolkit much better. AI could better understand the instructor's goals and help design a much more engaging lesson. However, as this week’s reading pointed out, not every class needs to use social media. This is where the instructor's skill becomes very important. They must avoid relying too blindly on technology. Instructors should not casually use social media just because they think, "Young learners like social media, so this will keep them interested," or "Learning can happen through social media, and using it will make my class look cool." Instead, instructors need to think critically before bringing social media into the classroom. They must answer t...

Pischetola et al. (2022) used Bakhtin’s theory. They said that two forces create true connections and new knowledge. One is the centripetal force (pulling things to the center). The other is the centrifugal force (pushing things out). When I think about education in my home country, the pulling-in force was much stronger. We have a very strict national curriculum. Most schools use textbooks approved by the government. Teachers and schools can try different teaching methods. However, there is simply too much to cover in the curriculum. Because of this, it is hard for teachers to teach freely and branch out. Are you a teacher, or have you taught before? If you are reading this, please share your experience. How is the balance between these pulling and pushing forces in your area?

Salomon (2016) introduced a failed case of LA students using iPads in schools. The curriculum on iPads did not allow teachers to revise it, and students began bypassing safeguards to play games on them. Therefore, teachers stopped using it altogether. The project, with a tremendous amount of money, would have been better not to do. In my home country, a similar situation happened a few years ago: an AI Digital textbook (AIDT). The majority of schools in my home country use textbooks published by government-approved publishers following a national curriculum. When the Minister of Education announced the introduction of AIDT, most publishers began developing AIDT. However, the government approved only 1-2 AIDTs based on their strict criteria, and schools had to choose among limited options. Moreover, creating students’ accounts manually, obtaining parental consent and uploading the scanned version, a bunch of administrative tasks related to AIDT, inconsistent content with paper textbooks...

Today, I found a paper about networked information/knowledge in higher education settings. Kumar et al. (2024) analyzed how people in higher education shared knowledge using ICT after reviewing the literature. They explain it with three sections: - Teaching and learning: Students and educators used cloud-based platforms and social media to learn collaboratively and share knowledge. However, the more shared materials students gathered, the lower their performance was, and the more they tended to avoid challenging tasks, as they thought they had learned something even by downloading them.  - University governance: ICT did not really enhance shared knowledge. It depended on leadership and the organizational culture. - Research: ICT helped global collaboration. In this research, the takeaway is that the educational effects really depend on how learners use the materials or tools, not the materials themselves. It reminds me of my college days, when almost every student shared materials ...

Today, I read Chesterman (2025). I believe that AI-generated content should be copyright-free or freely allowed for commercial use as long as the source is credited. In the past, creating high-quality media like art, video, or music required professional skills, forcing regular people to either pay high prices or spend hours searching for rare copyright-free options. Generative AI changed this by allowing anyone to easily create media, which  improves the quality of information; for instance, writers who used to rely only on text can now use AI images to explain their ideas much better. While technical issues like model poisoning or training data ethics are valid concerns, they should be treated as separate problems for AI companies. Restricting commercial use only blocks highly motivated creators who want to put effort into making great content for a larger audience. Ultimately, allowing commercial use is a win-win, as it helps creators share more engaging work while promoting AI ...

What captures learners' interest more today: a 5-minute physiology lecture or a 1-minute animated video of talking body organs? Definitely the second one. I watch a lot of YouTube Shorts, and I hardly ever watch videos over 10 minutes unless I really want to. Recently, the algorithm showed me physiology Shorts, and I learned about the human body in less than a minute. The video linked above is also a great example of microlearning (you should read the title to understand exactly what the video wants to tell you). Microlearning is perfect for using short pockets of time, like waiting for a pedestrian light, the microwave, or an elevator. There are also many shorts about public speaking and psychology. If you know any good microlearning YouTube channels, please share your recommendations!