HCMay 14

Overreliance in Writing Tasks: Exploring Similarity-Based Measures of AI Influence on Writing and Proposing a Reflective Writing Interface Intervention

arXiv:2605.153227.5
Predicted impact top 64% in HC · last 90 daysOriginality Synthesis-oriented
AI Analysis

For researchers and designers of AI-assisted writing tools, this work provides empirical methods to detect overreliance and demonstrates a potential interface intervention, though the evidence is preliminary.

The paper investigates how generative AI assistance affects users' writing by measuring textual overlap between AI suggestions and final text, finding patterns of suggestion reuse. It then proposes a reflective writing interface that, in a small study (n=4), increased users' awareness of AI influence and promoted more conscious engagement.

As generative AI (GenAI) systems become increasingly proficient at simulating human-like and well-reasoned text, users may attribute authority to AI outputs, shaping how they engage with writing and reasoning tasks. While prior work has raised concerns about AI overreliance, empirical approaches for observing this phenomenon during open-ended writing remain limited. In this paper, we examine how GenAI assistance influences users' interactions with AI suggestions during writing. We report results from a mixed-methods study in which 47 participants completed analysis and synthesis writing tasks with or without AI assistance. We quantify the textual overlap between AI suggestions and participants' writing and analyze participants' reflections. Our results show that AI assistance is associated with patterns of suggestion reuse. Building on these findings, we design and evaluate an interactive writing interface that may support reflection on the usage of the AI suggestions during writing. Evidence from a small follow-up think-aloud study (n = 4) suggests that the interface can increase users' awareness of how AI outputs are incorporated into their writing and may support more conscious engagement with AI assistance. Together, our findings contribute empirical methods for studying AI adoption in writing contexts and demonstrate how interface design can shape user-AI interaction.

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