The Impact of Multiple Parallel Phrase Suggestions on Email Input and Composition Behaviour of Native and Non-Native English Writers
This addresses the design of AI writing assistants for email composition, particularly for non-native speakers, though it is incremental in exploring suggestion quantity trade-offs.
The study investigated how multiple parallel phrase suggestions from a neural language model affect email writing behavior, finding that more suggestions (up to 6) improved ideation but reduced efficiency, with non-native English speakers benefiting more from additional suggestions.
We present an in-depth analysis of the impact of multi-word suggestion choices from a neural language model on user behaviour regarding input and text composition in email writing. Our study for the first time compares different numbers of parallel suggestions, and use by native and non-native English writers, to explore a trade-off of "efficiency vs ideation", emerging from recent literature. We built a text editor prototype with a neural language model (GPT-2), refined in a prestudy with 30 people. In an online study (N=156), people composed emails in four conditions (0/1/3/6 parallel suggestions). Our results reveal (1) benefits for ideation, and costs for efficiency, when suggesting multiple phrases; (2) that non-native speakers benefit more from more suggestions; and (3) further insights into behaviour patterns. We discuss implications for research, the design of interactive suggestion systems, and the vision of supporting writers with AI instead of replacing them.