Shang-Ling Hsu, Raj Sanjay Shah, et al.
Proceedings of the ACM on Human Computer Interaction
Large Language Models (LLMs) have introduced a paradigm shift in interaction with AI technology, enabling knowledge workers to complete tasks by specifying their desired outcome in natural language. LLMs have the potential to increase productivity and reduce tedious tasks in an unprecedented way. A systematic study of LLM adoption for work can provide insight into how LLMs can best support these workers. To explore knowledge workers' current and desired usage of LLMs, we ran a survey (n=216). Workers described tasks they already used LLMs for, like generating code or improving text, but imagined a future with LLMs integrated into their workflows and data. We ran a second survey (n=107) a year later that validated our initial findings and provides insight into up-to-date LLM use by knowledge workers. We discuss implications for adoption and design of generative AI technologies for knowledge work.
Shang-Ling Hsu, Raj Sanjay Shah, et al.
Proceedings of the ACM on Human Computer Interaction
Jean McKendree, John M. Carroll
CHI 1986
Praveen Chandar, Yasaman Khazaeni, et al.
INTERACT 2017
Konstantinos Mavrogiorgos, Shlomit Gur, et al.
DCOSS-IoT 2025