Authorship Drift: How Self-Efficacy and Trust Evolve During LLM-Assisted Writing
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866917262457307136 |
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| author | Park, Yeon Su Arvi, Nadia Azzahra Putri Kim, Seoyoung Kim, Juho |
| author_facet | Park, Yeon Su Arvi, Nadia Azzahra Putri Kim, Seoyoung Kim, Juho |
| contents | Large language models (LLMs) are increasingly used as collaborative partners in writing. However, this raises a critical challenge of authorship, as users and models jointly shape text across interaction turns. Understanding authorship in this context requires examining users' evolving internal states during collaboration, particularly self-efficacy and trust. Yet, the dynamics of these states and their associations with users' prompting strategies and authorship outcomes remain underexplored. We examined these dynamics through a study of 302 participants in LLM-assisted writing, capturing interaction logs and turn-by-turn self-efficacy and trust ratings. Our analysis showed that collaboration generally decreased users' self-efficacy while increasing trust. Participants who lost self-efficacy were more likely to ask the LLM to edit their work directly, whereas those who recovered self-efficacy requested more review and feedback. Furthermore, participants with stable self-efficacy showed higher actual and perceived authorship of the final text. Based on these findings, we propose design implications for understanding and supporting authorship in human-LLM collaboration. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_05819 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Authorship Drift: How Self-Efficacy and Trust Evolve During LLM-Assisted Writing Park, Yeon Su Arvi, Nadia Azzahra Putri Kim, Seoyoung Kim, Juho Human-Computer Interaction Large language models (LLMs) are increasingly used as collaborative partners in writing. However, this raises a critical challenge of authorship, as users and models jointly shape text across interaction turns. Understanding authorship in this context requires examining users' evolving internal states during collaboration, particularly self-efficacy and trust. Yet, the dynamics of these states and their associations with users' prompting strategies and authorship outcomes remain underexplored. We examined these dynamics through a study of 302 participants in LLM-assisted writing, capturing interaction logs and turn-by-turn self-efficacy and trust ratings. Our analysis showed that collaboration generally decreased users' self-efficacy while increasing trust. Participants who lost self-efficacy were more likely to ask the LLM to edit their work directly, whereas those who recovered self-efficacy requested more review and feedback. Furthermore, participants with stable self-efficacy showed higher actual and perceived authorship of the final text. Based on these findings, we propose design implications for understanding and supporting authorship in human-LLM collaboration. |
| title | Authorship Drift: How Self-Efficacy and Trust Evolve During LLM-Assisted Writing |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2602.05819 |