A machine in the loop: the peculiar intervention of artificial intelligence in writer’s block

Gilburt, Iona

doi:10.1080/14790726.2023.2223176

Cited by 2 publications

(1 citation statement)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Expert writers may eschew AI assistance, fearing it could stifle their creativity or impose a "cookie-cutter" structure on their unique style [55]. Conversely, non-experts or those experiencing writer's block might find initial AI suggestions invaluable to kick-start their writing process, editing and refining AI-generated content to better align it with their own voice and objectives [32]. Similarly, novice writers might benefit more from comprehensive AI support, where low-level suggestions, like individual sentence prompts, may be inadequate [7].…”

Section: Introductionmentioning

confidence: 99%

Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models

Dhillon,

Molaei,

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

View full text Add to dashboard Cite

Advances in language modeling have paved the way for novel human-AI co-writing experiences. This paper explores how varying levels of scaffolding from large language models (LLMs) shape the co-writing process. Employing a within-subjects field experiment with a Latin square design, we asked participants (N=131) to respond to argumentative writing prompts under three randomly sequenced conditions: no AI assistance (control), next-sentence suggestions (low scaffolding), and next-paragraph suggestions (high scaffolding). Our findings reveal a U-shaped impact of scaffolding on writing quality and productivity (words/time). While low scaffolding did not significantly improve writing quality or productivity, high scaffolding led to significant improvements, especially benefiting non-regular writers and less tech-savvy users. No significant cognitive burden was observed while using the scaffolded writing tools, but a moderate decrease in text ownership and satisfaction was noted. Our results have broad implications for the design of AI-powered writing tools, including the need for personalized scaffolding mechanisms.

show abstract

Section: Introductionmentioning

confidence: 99%