Investigating Context-Aware Collaborative Text Entry on Smartphones using Large Language Models

要旨

Text entry is a fundamental and ubiquitous task, but users often face challenges such as situational impairments or difficulties in sentence formulation. Motivated by this, we explore the potential of large language models (LLMs) to assist with text entry in real-world contexts. We propose a collaborative smartphone-based text entry system, CATIA, that leverages LLMs to provide text suggestions based on contextual factors, including screen content, time, location, activity, and more. In a 7-day in-the-wild study with 36 participants, the system offered appropriate text suggestions in over 80% of cases. Users exhibited different collaborative behaviors depending on whether they were composing text for interpersonal communication or information services. Additionally, the relevance of contextual factors beyond screen content varied across scenarios. We identified two distinct mental models: AI as a supportive facilitator or as a more equal collaborator. These findings outline the design space for human-AI collaborative text entry on smartphones.

著者
Weihao Chen
Tsinghua University, Beijing, China
Yuanchun Shi
Tsinghua University, Beijing, China
Yukun Wang
Tsinghua University, Beijing, China
Weinan Shi
Tsinghua University, Beijing, China
Meizhu Chen
Tsinghua University, Beijing, China
Cheng Gao
Tsinghua University, Beijing, China
Yu Mei
Tsinghua University, Beijing, China
Yeshuang Zhu
Tencent Inc., Beijing, China
Jinchao Zhang
Tencent Inc., Beijing, China
Chun Yu
Tsinghua University, Beijing, China
DOI

10.1145/3706598.3713944

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713944

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Text Entry

Annex Hall F205
7 件の発表
2025-04-29 23:10:00
2025-04-30 00:40:00
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