Suggestion Lists vs. Continuous Generation: Interaction Design for
Writing with Generative Models on Mobile Devices Affect Text Length, Wording
and Perceived Authorship
- URL: http://arxiv.org/abs/2208.00870v1
- Date: Mon, 1 Aug 2022 13:57:11 GMT
- Title: Suggestion Lists vs. Continuous Generation: Interaction Design for
Writing with Generative Models on Mobile Devices Affect Text Length, Wording
and Perceived Authorship
- Authors: Florian Lehmann, Niklas Markert, Hai Dang, Daniel Buschek
- Abstract summary: We present two user interfaces for writing with AI on mobile devices, which manipulate levels of initiative and control.
With AI suggestions, people wrote less actively, yet felt they were the author.
In both designs, AI increased text length and was perceived to influence wording.
- Score: 27.853155569154705
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Neural language models have the potential to support human writing. However,
questions remain on their integration and influence on writing and output. To
address this, we designed and compared two user interfaces for writing with AI
on mobile devices, which manipulate levels of initiative and control: 1)
Writing with continuously generated text, the AI adds text word-by-word and
user steers. 2) Writing with suggestions, the AI suggests phrases and user
selects from a list. In a supervised online study (N=18), participants used
these prototypes and a baseline without AI. We collected touch interactions,
ratings on inspiration and authorship, and interview data. With AI suggestions,
people wrote less actively, yet felt they were the author. Continuously
generated text reduced this perceived authorship, yet increased editing
behavior. In both designs, AI increased text length and was perceived to
influence wording. Our findings add new empirical evidence on the impact of UI
design decisions on user experience and output with co-creative systems.
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