  Creative tasks such as ideation or question proposal are powerful
applications of crowdsourcing, yet the quantity of workers available for
addressing practical problems is often insufficient. To enable scalable
crowdsourcing thus requires gaining all possible efficiency and information
from available workers. One option for text-focused tasks is to allow assistive
technology, such as an autocompletion user interface (AUI), to help workers
input text responses. But support for the efficacy of AUIs is mixed. Here we
designed and conducted a randomized experiment where workers were asked to
provide short text responses to given questions. Our experimental goal was to
determine if an AUI helps workers respond more quickly and with improved
consistency by mitigating typos and misspellings. Surprisingly, we found that
neither occurred: workers assigned to the AUI treatment were slower than those
assigned to the non-AUI control and their responses were more diverse, not
less, than those of the control. Both the lexical and semantic diversities of
responses were higher, with the latter measured using word2vec. A crowdsourcer
interested in worker speed may want to avoid using an AUI, but using an AUI to
boost response diversity may be valuable to crowdsourcers interested in
receiving as much novel information from workers as possible.
