The first time I interacted with Generative AI, I regarded it as merely a novelty, something that could summarize content or produce a rough draft of notes. However, during this course, my perspective shifted. I started considering AI as more than an automation tool, as a partner that augments our thinking, writing, and creation processes.
Employing ChatGPT made it clear how swiftly an outline could be constructed, a set of ideas be organized, a set of arguments be refined, and other viewpoints be proposed. ChatGPT did not replace my work; however, it shifted my focus from drafting to improving and evaluating the output more critically. This corresponds with new findings within the area, which state that generative AI does not remove the human element, but increases productivity by enhancing it. For instance, in controlled studies, access to AI results in a 40% time saving for writing tasks, while also improving the output, particularly for novice users (Noy & Zhang, 2023).
New technologies bring new responsibilities. Because AI models are still biased and limited, human supervision must prevail. In one work environment, AI boosted productivity, but the productivity gains driven by generative AI were most pronounced for novices learning from AI while expert users supplied essential context and corrections (Brynjolfsson, Li, and Raymond, 2023). This indicates that expert human input and AI use are not equivalent.
The most important lesson this course has taught me is that generative AI is more about reallocation than replacement, it reallocates creative resources toward analysis, emotion, and contextualization. This means the level of AI-assisted work will be determined more by the thoughtfulness of our partnership than the sophistication of the AI tools. One of the guest lecturers answered the question “Will AI replace humans?” very well by responding “No. AI won’t replace humans, but human who use AI will replace humans who don’t use AI.”
References
Brynjolfsson, E., Li, D., & Raymond, L. R. (2023). Generative AI at work (NBER Working Paper No. 31161). National Bureau of Economic Research. https://www.nber.org/papers/w31161
Noy, S., & Zhang, W. (2023). Experimental evidence on the productivity effects of generative artificial intelligence. Science, 381(6654), 187–192. https://doi.org/10.1126/science.adh2586
