AI Writing Tools Reshaped Petition Style on Change.org, Yet Success Rates Stayed Flat

When Change.org switched on an artificial intelligence writing assistant in 2023, the world’s largest petition platform became an accidental laboratory for one of the most pressing questions of the generative AI era: does machine help with words actually translate into real-world results? A new study published in Nature Human Behaviour suggests the answer is more complicated, and more sobering, than the technology industry’s boldest promises would imply. Researchers found that after the tool’s introduction, the petitions people wrote changed substantially in style and structure, and the texts became noticeably more similar to one another. Yet the outcomes that matter most to petitioners, whether their campaigns attract signatures and achieve their goals, did not improve.

The study, led by researchers examining the platform’s natural experiment, takes advantage of a rare circumstance in social science research. Rather than recruiting volunteers into an artificial setting, the team was able to observe what happened when an AI drafting tool became available to millions of real users pursuing real causes, from local community disputes to global campaigns. This ecological validity matters enormously, because laboratory experiments on AI writing support have often measured productivity and perceived quality on short, contrived tasks. Whether those gains survive contact with messy, high-stakes, emotionally charged advocacy writing has been an open question.

The findings stand in instructive tension with earlier work that helped fuel enthusiasm for generative AI in knowledge work. A widely cited 2023 experiment by Shakked Noy and Whitney Zhang published in Science found that access to ChatGPT increased worker productivity and, on average, improved the quality of professional writing tasks, with the largest gains accruing to less experienced workers. That result, alongside many others, shaped expectations that AI assistance would be a rising tide lifting all boats. The petition platform study complicates that narrative by showing that improvements in the text itself do not automatically cascade into improvements in what the text is meant to accomplish.

What exactly changed when writers gained access to the AI tool? According to the study, the petitions themselves shifted measurably. Users’ texts became more polished and more alike, converging toward a common stylistic template. This homogenization effect echoes findings from a 2024 study by Anil Doshi and Oliver Hauser, published in Science Advances, which showed that generative AI enhanced the individual creativity of short-story writers, producing stories judged as better written, while simultaneously reducing the collective diversity of the content. When many people draw on the same statistical model for inspiration, their outputs drift toward the model’s central tendencies, and the distinctive voices that once differentiated one petition from another begin to blur.

The convergence of petition texts is not merely an aesthetic concern. In advocacy, distinctiveness can be strategically valuable. A petition that reads like every other petition may struggle to capture attention, convey urgency, or signal the authentic grassroots energy that decision-makers and journalists respond to. The study’s authors suggest that the AI tool effectively acted as a stylistic filter, smoothing the idiosyncrasies of individual writers. Whether that smoothing helped or hurt any given campaign is difficult to say, but the aggregate picture is clear: the measurable transformation of style was not accompanied by any measurable transformation of success.

This dissociation between outputs and outcomes is the study’s central contribution, and it speaks to a broader pattern documented in the human-AI collaboration literature. A 2024 systematic review and meta-analysis by Michelle Vaccaro, Abdullah Almaatouq and Thomas Malone in Nature Human Behaviour examined studies comparing human-AI combinations against humans alone and AI alone, and found a striking heterogeneity in results. In some tasks, combining humans and machines produced performance gains; in others, the combination actually underperformed either humans or AI working independently. The lesson emerging from this literature is that the value of AI integration is deeply task-dependent, and that assuming complementarity is a mistake.

Petition writing, it turns out, may be a particularly unfavorable case for AI assistance. The success of a petition depends on many factors beyond prose quality: the salience of the issue, the size and engagement of the petitioner’s network, timing, media coverage, the target of the demand, and the political or corporate context in which the campaign unfolds. If a petition’s fate is determined largely by these external factors, then improving the wording offers limited leverage. The AI tool may have made petitions read more professionally, but professional reading did not move the needle on signatures or outcomes, because the bottleneck was never the writing.

There is also a psychological dimension worth considering. Research by Florian Draxler and colleagues, published in ACM Transactions on Computer-Human Interaction in 2024, documented what the authors called the AI ghostwriter effect: when users compose text with AI assistance, they are less likely to feel ownership or authorship of the words they submit. Applied to advocacy, this raises questions about authenticity and commitment. A petitioner who feels that their campaign text is partly machine-generated may be less invested in promoting it, or audiences may respond differently to text perceived as formulaic. The new study did not set out to test these mechanisms directly, but its findings are consistent with a picture in which the human element, the passion and personal stake behind a petition, remains the decisive ingredient that no drafting tool can supply.

The broader implications extend well beyond Change.org. Online advocacy communities, civic technology platforms, crowdfunding sites, and grassroots organizations are all experimenting with generative AI to lower barriers to participation. The promise is seductive: if a well-written petition is the price of entry to public discourse, then AI could democratize access for people who lack confidence or training in writing. The new findings temper that promise. Lowering the barrier to producing text is not the same as lowering the barrier to achieving change. If style converges and outcomes stagnate, platforms may be investing in polish where they should be investing in connection, mobilization, and community.

For the researchers, the study is a caution against evaluating AI tools by the metrics that are easiest to measure. Text similarity, readability, and perceived quality are quantifiable, and generative models reliably improve them. But the ultimate purpose of a petition is not to be well written; it is to persuade, mobilize, and precipitate action. The gap between those two standards is where the technology’s limitations become visible. As generative AI spreads through yet another corner of public life, this natural experiment on one of the internet’s largest advocacy platforms offers a clear-eyed reminder: changing how we write is easy, changing what our writing achieves is another matter entirely.

Subject of Research: The effect of AI writing assistance on petition style and outcomes on an online petition platform

Article Title: Petition style changes with AI writing support but outcomes do not improve

Article References: Petition style changes with AI writing support but outcomes do not improve. (n.d.). https://doi.org/10.1038/s41562-026-02579-1

Image Credits: AI Generated

DOI: 10.1038/s41562-026-02579-1

Keywords: generative AI, Change.org, online petitions, AI writing tools, human-AI collaboration, text homogenization, digital activism, Nature Human Behaviour, advocacy platforms, productivity, creativity, civic technology