AI Chatbots as a Source of Political Information: Evidence from Benin

Last registered on April 06, 2026

View Trial History

Pre-Trial

Trial Information

General Information

Title

AI Chatbots as a Source of Political Information: Evidence from Benin

RCT ID

AEARCTR-0018190

Initial registration date

April 02, 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

April 06, 2026, 8:11 AM EDT

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

Locations

Country

Benin

Region

Primary Investigator

Name

Eugenia Maria Frezza

Affiliation

Università della Svizzera italiana

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Philine Widmer

PI Affiliation

Paris School of Economics

Contact Investigator

PI Name

Ekaterina Zhuravskaya

PI Affiliation

Paris School of Economics

Contact Investigator

PI Name

Sergei Guriev

PI Affiliation

London Business School

Contact Investigator

PI Name

Joël Eke

PI Affiliation

Paris School of Economics

Contact Investigator

Additional Trial Information

Status

On going

Start date

2026-03-22

End date

2027-06-30

Keywords

Electoral, Governance

Additional Keywords

large language models, chatbots, political information, voting behavior, political knowledge, democratic attitudes, mobile internet, information costs, low-information environment

JEL code(s)

D83, L86, O33, P16

Secondary IDs

Prior work

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

Abstract

AI chatbots hold the promise of transforming access to information in developing countries. In Benin, there are 16.4 million mobile connections (110% of the population) but only 32% internet access. Mobile data costs are high; Sub-Saharan Africans pay on average 2.4% of monthly income for 1 GB, well above international affordability thresholds. Information search on smartphones is difficult: small screens, no tabs, and limited ability to compare or synthesize sources. Research shows that mobile-only users often have a limited "mental model" of the internet, relying on just one or two apps. In this environment, AI chatbots could be transformative: a single conversational query replaces multiple web searches and synthesizes complex information into plain language. Yet there is almost no causal evidence on the political effects of AI chatbots, and none from a low-income, developing-country context.

This project studies whether access to AI chatbots affects political knowledge, attitudes, and electoral engagement in a low-information environment. We conduct a randomized controlled trial with university students in Benin around the April 12, 2026 presidential election. We deploy a general-purpose AI chatbot via WhatsApp with one treatment arm additionally receiving access to a fact-checking chatbot. WhatsApp is the dominant communication platform across Sub-Saharan Africa, making it a natural delivery channel for our study. The core question is: Does access to AI chatbots change how people engage with political information, what they know, what they believe, and how they vote?

External Link(s)

Registration Citation

Citation

Eke, Joël et al. 2026. "AI Chatbots as a Source of Political Information: Evidence from Benin." AEA RCT Registry. April 06. https://doi.org/10.1257/rct.18190-1.0

Sponsors & Partners

There is information in this trial unavailable to the public. Use the button below to request access.

Request Information

Experimental Details

Interventions

Intervention(s)

We conduct a three-arm randomized controlled trial with approximately 3,000 university students recruited in person at public universities in Benin (Universite d'Abomey-Calavi and Universite de Parakou). After in-person recruitment, we follow up with participants on WhatsApp and randomly assign them to: (1) a control group with no intervention; (2) a treatment group encouraged to use a general-purpose AI chatbot deployed via WhatsApp; or (3) a treatment group encouraged to use both a fact-checking chatbot (AskVera) and the same general-purpose AI chatbot. Participants receive instructions on how to access and use the chatbots, and the cost of using the AI tools is covered by the research team. Incentivized quizzes encourage the use of these tools. Participants are free to use the tools as much or as little as they wish over the treatment period.

Intervention Start Date

2026-04-02

Intervention End Date

2026-05-15

Primary Outcomes

Primary Outcomes (end points)

- AI chatbot usage (frequency, topics discussed, user experience, trust in tool);
- Political knowledge (current events quiz scores, beliefs about political issues);
- Voting behavior (turnout, candidate choice);
- Political attitudes (policy issue salience, policy positions, candidate familiarity and favorability);
- Institutional trust and democratic satisfaction;
- Self-reported well-being.

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

Secondary Outcomes (explanation)

Experimental Design

Participants are randomly assigned to one of three groups: a control group, a treatment group receiving access to a general-purpose AI chatbot via WhatsApp, or a treatment group receiving access to the same chatbot plus a fact-checking service. We collect baseline and endline survey data as well as chatbot usage logs.

Experimental Design Details

Not available

Randomization Method

Randomization is conducted using a computer-based random number generator in Stata. A seed is set to ensure reproducibility.
Randomization stratified by gender, region of origin, and field of study (STEM vs non-STEM).

Randomization Unit

Individual

Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters

Sample size: planned number of observations

3000 University Students.

Sample size (or number of clusters) by treatment arms

1000 students will be assigned to the Control group.
1000 students will be assigned to the Treatment Arm 1.
1000 students will be assigned to the Treatment Arm 2.

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

Supporting Documents and Materials

There is information in this trial unavailable to the public. Use the button below to request access.

Request Information

IRB