AI for Me, Not (Yet) for Thee? Desirable Difficulties and Deliberate Friction with LLMs
In December 2025, I was grading final projects for my course on data visualization with R. On one of my screens, I had a browser open to my university’s learning management system and came across a now-familiar phenomenon: a student had used verbatim ChatGPT output for their assignment. The code they included was flawless, but used function arguments and code syntax that we never covered in class and that weren’t actually necessary. More concerning, the original chat prompt was inadvertently still included, along with fill-in-the-blank sections that the large language model (LLM) had included to help tailor the response to the course (e.g. “[insert an example from your professor’s class here]”). Following my course’s AI policy, I gave a sizable point reduction, sighed heavily, and moved on to the next assignment.
“Writing is hard.” Thrilled to share that this simple idea led to a new paper in Political Analysis! Where most text methods focus on content, I test if expression is also effortful action. I find simple measures like character counts reveal attitudes and predict voting. cup.org/4cUmoXi 1/