When it comes to using generative AI tools such as ChatGPT, it is commonly assumed that its main purpose is for mundane tasks, like writing reports, emails or cost-benefit analyses. Research supports this, showing huge gains in productivity within such tasks (Winn, 2023). In the corporate space this is reflected by strategies like IBM’s, that aim to outsource 90% of their so-called ‘mundane tasks’ to generative AI (Rao, 2023). Even in everyday life it seems that ChatGPT is mainly used for routine activities that require little creativity, such as helping children with homework or making grocery lists (Chatterji et al., 2025). This goes to show that the intent of GenAI, the resulting productivity gains and its use in everyday life lie mostly in one facet of human-life: our mundane, everyday tasks that can easily be taken over by a simple-minded machine.
After a month of studying Business Information Management (BIM) at Erasmus University Rotterdam however, I have noticed that many mundane tasks are still in the hands of students. This has 2 main reasons. First, the university relies on AI checks to monitor written assignments, even though it is shown that these tools are proven to be unreliable in many different contexts, and can result in both false positives and false negatives (Weber-Wulff, 2023). The second reason is that the course curriculum is already adjusted to the AI revolution. Instead of having to regurgite study materials superficially, there is now room for more in-depth insights, and demonstrations of true understanding of the materials. Take-home assignments are disappearing, and even the assignments that you do take home are checked on AI. It seems that the university combats AI tools properly, and leaves the “real work” to students. Good thing right?
Not entirely. Unfortunately combatting AI is nearly impossible. With a plethora of tools available and the endless ways to apply them it is inevitable that students (including myself) will use those tools. But if not with mundane tasks, what is AI used for? The question becomes not whether students use the tools, but how.
Generally I would argue that the more creative and high-level the task is, the harder it is to check for AI. Normal plagiarism for example, like stealing a sentence or paragraph, is easy to detect. However, stealing high-level concepts, such as ideas, overall structure or creative elements, is far harder to trace (Jindal, 2024). The less ‘mundane’ a task, the harder it is to trace its origin. This is equally true for GenAI, where it is seemingly harder to detect the input of high-level ideas that are generated by AI rather than pieces of text.
That’s why in practice, GenAI is mostly used for creative tasks, such as idea generation, where you can input an assignment and ask for inspiring ideas. You can then just pick one that suits you and your assignment best, after which you will work out the assignment yourself. Additionally, you can use generative AI for the creative elements of your assignment, such as logos or graphics. You can even use tools to create prototypes, for example by using applications such as base.44, which will not only decide the content for your prototype, but also all its design features. Often you use ChatGPT or Bard as a high-level thought partner, after which you simply execute its ideas, and add creative elements generated by other AI tools.
In conclusion, it seems that in our current university system, GenAI hardly takes over our mundane tasks, but rather makes us stifle creativity when it comes to the overarching elements of an assignment. Instead of orchestrating a task and letting AI execute it, we are the ones being orchestrated.
Perhaps we should try looking at how we can motivate our students to do the thinking for themselves, and leave the mundane tasks such as writing out the assignment to the AI tools. We should foster real-time creativity by using class discussions, real-time case studies or oral exams, and leave the writing to the tools made for it. While we put AI detection tools in the garbage, let’s focus on depth of analysis, originality of ideas and real-time input of students.
Chatterji, A., Cunningham, T., Deming, D., Hitzig, Z., Ong, C., Shan, C., & Wadman, K. (2025, September 15). How people use ChatGPT. OpenAI; Duke University; Harvard University. https://cdn.openai.com/pdf/a253471f-8260-40c6-a2cc-aa93fe9f142e/economic-research-chatgpt-usage-paper.pdf
Jindal, M. (2024, September 17). How can plagiarism be detected? Bytescare. https://bytescare.com/blog/how-can-plagiarism-be-detected?
Rao, A. (2023, August 25). Doing 90% mundane HR tasks like promotion, assessment of people with AI: IBM CEO Arvind Krishna. The Economic Times. https://economictimes.indiatimes.com/tech/technology/doing-90-mundane-hr-tasks-like-promotion-assessment-of-people-with-ai-ibm-ceo-arvind-krishna/articleshow/103055354.cms
Weber-Wulff, D., Anohina-Naumeca, A., Bjelobaba, S., Foltýnek, T., Guerrero-Dib, J., Popoola, O., Šigut, P., Waddington, L., et al. (2023, December 25). Testing of detection tools for AI-generated text. International Journal for Educational Integrity, 19, Article 26. https://doi.org/10.1007/s40979-023-00146-z
Winn, Z. (2023, July 14). Study finds ChatGPT boosts worker productivity for some writing tasks. MIT News. https://news.mit.edu/2023/study-finds-chatgpt-boosts-worker-productivity-writing-0714
