“We need database literacies, algorithmic literacies, computational literacies, interface literacies. We need new hybrid practitioners: artist-theorists, programming humanists, activist-scholars; theoretical archivists, critical race coders.”
Emily Johnson and Anastasia Salter, “Introduction: Provocations for Making,” in Critical Making in the Age of AI (Amherst College Press, 2025), https://doi.org/10.3998/mpub.14510509.
When reading this essay, I was multitasking and a large language model is generating an analysis of an essay I am going to read. This makes me think about a problem: when artificial intelligence can provide “short cuts” generating texts and essays, what skills do humanities subjects require students to acquire. I originally believe that outputting a perfect essay is a mirror of having an excellent understanding of the course context. However, the essay slightly changed my thought: if I keep on viewing traditional academic essays as the only valid scholarly output, I may become a passive user of artificial intelligence, as they do well generating texts. This quote argues that we do need to follow the trend of large language models and understand how apps, databases and algorithms carry hidden assumptions and biases.
As a student majoring in Physics, the quote actually closely connects to my experience where data analyzation application is frequently used for works such as finding trends. However, I rarely think of how those datasets were built and who was excluded from them. In fact, I fully believe in the result given by the applications. This reading made me realize data work is never fully objective, which reshaped how I plan to approach my future research projects. Later on, I may stop and think for a while where the trend comes from rather than just accepting what the application provides me.
If I have to choose the most eager part to learn within the area of digital humanity, it can be data visualization. Unlike plain text, visualized data can provide a better picture for data analyzation, and even provides insight to hidden stories behind the data. In addition, as the way to visualize data varies, the data analysis progresses differently for researchers, contributing to a diverse conclusion based on a single set of data. This diversity in ideas and thoughts can’t be mimicked by artificial intelligence, and it can be one of the reasons why humanity still exists.
Hi Peter, I really like the fact that as a Physics major, you “realize data work is never fully objective.
Hi Peter, as I said, I am really surprised to see how you are bridging your understanding of physics with how you understand digital humanities. As a history major, I really need someone from other majors to help me imagine digital humanities from another perspective. I think your response shows a thoughtful understanding of how digital humanities changes the way we think about academic work. Your discussion of AI is especially interesting because you connect it to the question of what skills students still need to develop. I also like how you relate the reading to physics and data analysis. Your point about questioning how datasets are created shows a strong awareness that digital tools are never completely neutral. Also, your last sentence is truly inspiring: ‘This diversity in ideas and thoughts can’t be mimicked by artificial intelligence, and it can be one of the reasons why humanity still exists.’