Prompting Democracy: A Field Experiment on Generative AI and Participatory Politics

Last registered on June 29, 2026

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Pre-Trial

Trial Information

General Information

Title

Prompting Democracy: A Field Experiment on Generative AI and Participatory Politics

RCT ID

AEARCTR-0018990

Initial registration date

June 23, 2026

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published

June 29, 2026, 8:32 AM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Locations

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Primary Investigator

Name

Saul Martinez Castellanos

Affiliation

European University Institute

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Other Primary Investigator(s)

PI Name

Vincenzo Galasso

PI Affiliation

Bocconi

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PI Name

Matteo Giugovaz

PI Affiliation

Universitat Pompeu Fabra

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PI Name

Matteo Muntoni

PI Affiliation

European University Institute

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PI Name

Tommaso Nannicini

PI Affiliation

European University Institute

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Additional Trial Information

Status

In development

Start date

2026-04-11

End date

2026-10-05

Keywords

Electoral

Additional Keywords

AI, Political economy

JEL code(s)

Secondary IDs

Prior work

This trial does not extend or rely on any prior RCTs.

Abstract

Generative AI can now be used to help citizens move from a mere social concern to a concrete, structured policy proposal. This creates a question for participatory politics: what changes when part of the work of generating a proposal can be supported by AI? AI may matter on the formulation side, by shaping the clarity, structure, feasibility, and appeal of the proposals citizens formulate. It may also matter on the reception side, by shaping how other citizens react to those proposals, especially when they know that AI was involved in producing them.

We study these questions in the context of Le Primarie delle Idee, an Italian participatory platform launched in April 2026. The platform allows registered users to develop policy proposals either with the support of a generative AI assistant or on their own. In the AI-assisted route, users interact with a multi-turn conversational facilitator based on a generative AI model, called AIdea and designed to help them turn an initial concern into a more concrete proposal. In the self-authored route, users draft their proposal directly using a template. Once submitted, proposals can be read, discussed, supported, and voted on by the user community.

The experiment therefore studies both the formulation and reception of policy proposals in an online political platform. On the formulation side, we ask whether AI-assisted proposals differ from self-authored proposals in quality, structure, and electoral appeal. Among users who receive AI assistance, we also ask whether the design of that assistance matters, by comparing a procedural, scaffolded version of the assistant with a more open-ended version and measuring effects on users' policy-reasoning skills. On the reception side, we ask whether disclosure matters: do users engage differently with a proposal when they are told whether it was AI-assisted or self-authored?

We answer these questions through two main randomizations: one over the proposal-development route and one over whether proposals display an authorship tag indicating that they were either AI-assisted or self-authored, plus a nested randomization over the version of the assistant assigned to users.

External Link(s)

Live platform

Registration Citation

Citation

Galasso, Vincenzo et al. 2026. "Prompting Democracy: A Field Experiment on Generative AI and Participatory Politics." AEA RCT Registry. June 29. https://doi.org/10.1257/rct.18990-1.0

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

The platform offers two routes for developing a policy proposal: an AI-assisted route, in which users develop their proposal through a multi-turn conversation with a generative AI facilitator, and a self-authored route, in which users draft their proposal directly using a template. Within the AI-assisted route, the facilitator operates in one of two modes: a procedural, scaffolded style that guides the conversation through a structured sequence, or an open-ended style without a fixed sequence. Separately, the platform interface either displays or does not display a tag disclosing whether each proposal was AI-assisted or self-authored. Users are randomized over the proposal-development route, over the facilitator mode, and over whether the authorship-disclosure tag is shown.

Intervention Start Date

2026-06-25

Intervention End Date

2026-10-05

Primary Outcomes

Primary Outcomes (end points)

The primary outcomes, grouped by randomization, are:
(1) For the proposal-development route: proposal quality (a blind-evaluated rubric score), proposal-level engagement on the platform (supports, comments, event participation, and sharing), and individual-level submission behavior (whether a user submits any proposal, and the number of proposals submitted).
(2) For the facilitator mode (procedural vs. open-ended): proposal quality, and characteristics of the development conversation, including its length and how it concludes, the breadth of policy-reasoning components covered, and the extent of post-generation editing of the proposal.
(3) For the authorship-disclosure tag: engagement with proposals on the platform (supports, comments, event participation, sharing, and final voting), and whether users open or submit a proposal via the AI-assisted or self-authored route.
The partition of outcomes into primary and secondary is preliminary and depends on the statistical power available given platform adoption; it will be finalized in an updated plan once the realized sample is known.

Primary Outcomes (explanation)

Several outcomes are constructed. Proposal quality is a 0–12 score from a six-item rubric (clarity/coherence, causal mechanism, feasibility acknowledgment, trade-off recognition, evidence referenced, distributional specificity), each scored 0–2 by two blind independent human evaluators, with a third evaluator triggered by disagreement greater than two points on any item; the full proposal corpus is additionally scored by LLM evaluators using the same rubric as a robustness check, validated against human scores, with human scores as the primary measure. Platform engagement is constructed from logged user interactions (supports, comments, event participations, and shares); a platform "most shared" score weights these three points per support, two per event participation, and one per comment. Conversation complexity is constructed from the coverage of policy-reasoning components and a measure of user initiative (the share of components first introduced by the user rather than the facilitator). Post-generation editing is measured as both a binary indicator of any manual modification and the magnitude of change (semantic and textual distance between the generated and submitted versions). Detailed operational definitions are provided in the uploaded pre-analysis plan.

Secondary Outcomes

Secondary Outcomes (end points)

Secondary outcomes, by randomization:
(1) Proposal-development route: self-assessed expected impact and approval of the proposal; off-platform mobilization (whether a user organizes at least one event); a platform engagement index based on login frequency; and the conversation-level outcomes listed below.
(2) Facilitator mode (procedural vs. open-ended): satisfaction with the AI-assisted experience and an open-text report of what the assistant was most useful for (collected from AI-assisted users at submission).
(3) Conversation-level (analyzed where applicable): conversation length and how it concludes; breadth of policy-reasoning components covered and user initiative; and the extent of post-generation editing of the generated proposal.
We additionally report secondary estimands alongside the primary intent-to-treat analyses, including a complier-average (instrumental-variables) effect for the proposal-development route. The primary/secondary partition is preliminary and will be finalized once the realized sample is known.

Secondary Outcomes (Explanation)

Secondary Outcomes (explanation)

The engagement index is constructed from logins per active week. Off-platform mobilization is an indicator for organizing at least one online or in-person event linked to a proposal. Self-assessed impact and approval are measured via fixed-response survey items at submission. Conversation-level outcomes (length, conclusion type, policy-reasoning breadth, user initiative, and editing magnitude) are constructed as described under the primary-outcome explanation. Satisfaction is a fixed-response item; the usefulness report is free text. Detailed operational definitions are in the uploaded pre-analysis plan.

Experimental Design

Registered users are randomized at the individual level along three orthogonal dimensions. The first assigns users to one of two proposal-development routes: an AI-assisted route, in which they develop a proposal through conversation with a generative AI facilitator, or a self-authored route, in which they draft directly using a template. Nested within this, a second assignment sets the AI facilitator to one of two modes: a procedural, scaffolded style or an open-ended style, and applies to all users, becoming operative only for those who use the facilitator. A third, independent assignment determines whether the platform interface displays a tag disclosing each proposal's mode of authorship (AI-assisted or self-authored). The three assignments are orthogonal by design. Analysis is primarily by intent-to-treat. The study runs on a live participatory platform from June to October 2026, concluding with a final platform vote among the most-supported proposals. Because the realized sample depends on platform adoption over the experimental window, the partition of outcomes and some specifications are preliminary and will be finalized in an updated plan. Further design detail is provided in the uploaded pre-analysis plan.

Experimental Design Details

Not available

Randomization Method

Randomization done by computer. For users registered before the experimental start, assignment is performed by the research team, stratified by registration phase, with balanced allocation across the eight experimental cells within each stratum. For users registering during the experimental period, assignment is made on a rolling basis using a deterministic eight-registration cycle that preserves the same target proportions. All three randomized dimensions are assigned at the user level and remain fixed throughout the experimental period.

Randomization Unit

Individual (user). All three randomized dimensions, proposal-development route, AI-facilitator mode, and authorship-disclosure tag, are assigned at the individual user level. For users registered before the experimental start, randomization is stratified by registration phase. Note: the realized sample size is not yet known, as it depends on platform adoption over the experimental window; the figures below are planned targets derived from ex-ante power calculations and will be updated once the realized sample is known.

Was the treatment clustered?

Yes

Experiment Characteristics

Sample size: planned number of clusters

The realized number is not yet known, as it depends on platform adoption over the experimental window (June–October 2026); this field will be updated once the realized sample is known.

Sample size: planned number of observations

The realized number of individuals, proposals, and conversations is not yet known, as it depends on platform adoption over the experimental window; these figures will be updated once the realized sample is known.

Sample size (or number of clusters) by treatment arms

The three randomizations are orthogonal, so each user contributes to all three contrasts simultaneously; the arm sizes above are not additive across randomizations. Realized arm sizes are not yet known and depend on platform adoption; these are planned targets and will be updated once the realized sample is known.

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Ex-ante calculations assume significance α = 0.05, power = 0.80, minimum detectable effect = 0.20 standard deviations, within-individual correlation across proposals ρ = 0.50, and an average of two proposals per individual. For R1, accounting for two-sided non-compliance, calculations additionally assume a first-stage compliance rate κ = 0.80. Under these assumptions the planned per-arm targets are approximately 614 individuals (R1, participant level) and 393 individuals per arm (R1.1 and R2, participant level); lower per-arm requirements apply at the proposal/conversation level (approximately 460 for R1 and 295 for R1.1 and R2). All minimum detectable effects are expressed in standard-deviation units of the respective outcome. These calculations are ex-ante; realized power depends on platform adoption and will be revisited once the realized sample is known.

Supporting Documents and Materials