Evaluating an AI Evidence Intermediary to Improve Research Use in Sri Lanka’s Economic Policymaking

Last registered on June 03, 2026
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Pre-Trial

Trial Information

General Information

Title
Evaluating an AI Evidence Intermediary to Improve Research Use in Sri Lanka’s Economic Policymaking
RCT ID
AEARCTR-0018171
Initial registration date
May 29, 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 03, 2026, 9:17 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
Pui Hang Wong
Affiliation
Maastricht University
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Other Primary Investigator(s)

PI Name
Tiloka de Silva
PI Affiliation
University of Moratuwa
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PI Name
Indra Mahakalanda
PI Affiliation
University of Moratuwa
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PI Name
Mindel van de Laar
PI Affiliation
UNU-MERIT and Maastricht University
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PI Name
Chethika Gunasiri
PI Affiliation
DevelopMetrics
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PI Name
Lindsey Moore
PI Affiliation
DevelopMetrics
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Additional Trial Information

Status
In development
Start date
2026-07-31
End date
2028-01-01
Keywords
Governance, Labor, Other
Additional Keywords
AI, technology, capacity building, public policy
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Governments and development agencies routinely conduct evaluations as part of their workflow. However, this knowledge may not feed back to future policymaking due to information overload. The rise of large language models (LLMs) and artificial intelligence provides a perfect solution to expedite policy learning. However, whether and how these AI agents facilities the use of evidence in policymaking remains untested. To address this question, we examine whether access to a generative AI platform powered by a domain-specific LLM improves the use of evidence in the policymaking process among government officials in Sri Lanka.
External Link(s)

Registration Citation

Citation
de Silva, Tiloka et al. 2026. "Evaluating an AI Evidence Intermediary to Improve Research Use in Sri Lanka’s Economic Policymaking." AEA RCT Registry. June 03. https://doi.org/10.1257/rct.18171-1.0
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Experimental Details

Interventions

Intervention(s)
Participating government officials will be granted access to a generative AI–powered evidence platform that enables users to search, retrieve, and interact with evidence from policy documents produced by the Sri Lankan government and evaluation reports from international development organizations such as USAID. Participants can make an inquiry, and the platform will provide responses based on the input reports and documents contained in the library. Core functions of the platform include: (i) search across a curated corpus using a domain-specific language model; (ii) assisted summarization through a chat interface; and (iii) transparent citation trails that allow users to trace major findings from source documents and search for information within them.
Intervention Start Date
2026-09-01
Intervention End Date
2027-09-30

Primary Outcomes

Primary Outcomes (end points)
Attitudes towards evidence-informed policy making (EIPM), perception on use of AI tools in EIPM, EIPM practice
Primary Outcomes (explanation)
Attitudes toward and perceptions of evidence-informed policymaking (EIPM) will be measured using a 28-item questionnaire. Latent constructs will be derived through factor analysis. EIPM practices will be measured using a single survey item.

Secondary Outcomes

Secondary Outcomes (end points)
Text data from policy documents
Secondary Outcomes (explanation)
Use of research and policy evidence in policy documents will be assessed based on a coding manual.

Experimental Design

Experimental Design
The target population consists of mid- and senior-level government officials who are responsible for drafting or reviewing policy documents, including policy briefs, policy reports, and strategy papers. Approximately 60 officials will be randomly assigned to either the treatment group or the control group, with 30 individuals in each group.
As part of a broader partnership with the Sri Lankan government, an additional group of approximately 30 government officials will participate in a voluntary capacity-building program that includes access to the AI platform and complementary training activities. The impact of this capacity-building intervention will be evaluated separately using a mixed-methods approach combining quantitative and qualitative data.
Experimental Design Details
Not available
Randomization Method
Random number generator
Randomization Unit
Individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
1
Sample size: planned number of observations
60 government officials in the RCT.
Sample size (or number of clusters) by treatment arms
30 individuals in the treatment group and 30 individuals in the control group.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
With n = 30, the minimum detectable effect size (Cohen's d) is 0.73. The study is powered to detect a large standardized effect.
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
University Ethics Review Committee, University of Moratuwa
IRB Approval Date
2026-04-27
IRB Approval Number
EDN/2026/012
Analysis Plan