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Suggestion Lists vs. Continuous Generation : Interaction Design for Writing with Generative Models on Mobile Devices Affect Text Length, Wording and Perceived Authorship

Title data

Lehmann, Florian ; Markert, Niklas ; Dang, Hai ; Buschek, Daniel:
Suggestion Lists vs. Continuous Generation : Interaction Design for Writing with Generative Models on Mobile Devices Affect Text Length, Wording and Perceived Authorship.
In: Mensch und Computer 2022 : Tagungsband. - New York : ACM , 2022 . - pp. 1-19
DOI: https://doi.org/10.1145/3543758.3543947

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Project information

Project title:
Project's official titleProject's id
AI Tools - Continuous Interaction with Computational Intelligence ToolsNo information

Project financing: Bayerisches Staatsministerium für Wissenschaft, Forschung und Kunst

Abstract in another language

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.

Further data

Item Type: Article in a book
Refereed: Yes
Keywords: mobile text entry; typing; language model; continuous generations;
text suggestions; initiative; control; roles; authorship; deep learning;
neural network; dataset
Institutions of the University: Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science
Faculties
Faculties > Faculty of Mathematics, Physics und Computer Science
Result of work at the UBT: Yes
DDC Subjects: 000 Computer Science, information, general works > 004 Computer science
Date Deposited: 05 Aug 2022 07:25
Last Modified: 05 Aug 2022 07:25
URI: https://eref.uni-bayreuth.de/id/eprint/71371