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

2026-05-14

AI Summary

This study addresses the risk of overreliance on generative AI in writing, which can undermine users’ critical reflection on AI-generated suggestions. Employing a mixed-methods approach—combining quantitative text similarity analysis with qualitative think-aloud protocols—the research investigates differences in writing behaviors with and without AI assistance and introduces a novel text similarity–based metric to measure AI influence. Building on these insights, the authors design and evaluate an interactive writing interface that scaffolds user reflection. Findings indicate that AI assistance significantly increases verbatim reuse of suggested content, whereas the proposed interface effectively enhances users’ awareness of integrating AI-generated text, fostering more deliberate human-AI collaboration. This work provides empirical grounding and an innovative design paradigm for developing responsible AI-powered writing tools.

Abstract

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.

Problem

• overreliance
• generative AI
• writing tasks
• AI influence
• human-AI interaction

Innovation

• AI overreliance
• similarity-based measurement
• reflective writing interface
• human-AI collaboration
• generative AI