Generative AI Use and Loneliness

Last registered on January 06, 2026
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Pre-Trial

Trial Information

General Information

Title
Generative AI Use and Loneliness
RCT ID
AEARCTR-0016749
Initial registration date
January 03, 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
January 06, 2026, 7:19 AM EST

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
Claudia Senik
Affiliation
Paris School of Economics
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Other Primary Investigator(s)

PI Name
Louis Fréget
PI Affiliation
CEPREMAP
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PI Name
Ariell Reshef
PI Affiliation
Paris School of Economics
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Additional Trial Information

Status
In development
Start date
2026-01-05
End date
2026-02-28
Keywords
Behavior, Health, Welfare
Additional Keywords
Artificial Intelligence, Subjective Well-Being, Loneliness, Social Connectedness, Survey Experiment, WTA, Work From Home
JEL code(s)
C93, I31, I12, O33
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Against the backdrop of increasing use of generative AI, we study the relationship between AI use and well-being using a large scale survey on a representative sample of the French population. First, we conduct descriptive analyses documenting how baseline AI use relates to both subjective and objective loneliness. Second, we run a randomized controlled trial in which participants are assigned either to engage in daily personal conversations with a generative AI for 28 days or to continue their usual behavior. This allows us to uncover the causal effect of daily conversations with generative AI on subjective loneliness and well-being as well as on objective loneliness (real-life interactions).
External Link(s)

Registration Citation

Citation
Fréget, Louis , Ariell Reshef and Claudia Senik. 2026. "Generative AI Use and Loneliness." AEA RCT Registry. January 06. https://doi.org/10.1257/rct.16749-1.0
Sponsors & Partners

Sponsors

Experimental Details

Interventions

Intervention(s)
We run a survey experiment where the treatment group is nudged to have daily conversations about personal matters with a generative AI tool.
Intervention Start Date
2026-01-26
Intervention End Date
2026-02-28

Primary Outcomes

Primary Outcomes (end points)
Life satisfaction (0–10 scale, single-item).
3-item UCLA Loneliness Scale (UCLA - 3): constructed from 3 items: lack of companionship, exclusion, isolation.
Self-reported loneliness.
Affect (two items): happiness yesterday, depression yesterday.
Primary Outcomes (explanation)
All outcomes will be standardized in the main regression tables. Regarding the 3-item UCLA Loneliness Scale (UCLA-3), Each item (‘lack of companionship,’ ‘feeling left out,’ ‘feeling isolated from others’) is answered on a 3-point Likert scale (1 = hardly ever/never, 2 = some of the time, 3 = often). Scores from the three items will be summed to create an index ranging from 3 to 9, with higher scores indicating greater loneliness. For analysis, this index will be standardized (mean 0, standard deviation 1) so that effect sizes are expressed in standard deviation units.

Secondary Outcomes

Secondary Outcomes (end points)
Number of days spent chatting about personal matters with an AI (first stage).
Frequency of use of AI for different matters (first stage)
Relationship satisfaction (romantic partner, if applicable).
Family relations satisfaction.
Friendship satisfaction.
Objective loneliness (meals alone, meetings with friends/relatives)
Perceived social support: whether the respondent has someone they can rely on.
Valuation of AI (willingness-to-accept to abstain to use for personal reasons for 1 week).
Pleasant or unpleasant personal conversations with AI.
Secondary Outcomes (explanation)
Here, we merge both what is referred to as "first stage and secondary outcomes" in the PAP. If any inconsistencies arise between the information in the AEA RCT Registry and this PAP pdf file, the latter shall take precedence.

Experimental Design

Experimental Design
Participants are recruited from an online panel in France and complete two survey waves spaced four weeks apart. The unit of randomization and analysis is the individual. After Wave 1, participants are randomly assigned in equal proportions to treatment or control. Outcomes are measured again in Wave 2 using the same instruments, allowing comparison across arms while controlling for baseline measures. Randomization is implemented by the survey platform.
Experimental Design Details
Not available
Randomization Method
Randomization performed by the survey company.
Randomization Unit
Individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
10,000 survey respondents
Sample size: planned number of observations
10,000 survey respondents
Sample size (or number of clusters) by treatment arms
5,000
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Two-arm RCT, equal allocation, two-sided α = 0.05. We expect 20% attrition between baseline and endline surveys. Total N = 8,000. Primary outcomes are standardized (unit: SD). Under these assumptions, the minimum detectable effect (MDE) at 80% power is 0.06 SD. Accordingly, the power to detect an effect of 0.10 SD (a benchmark often used in survey experiments) exceeds 99%. These estimates are conservative, since they do not incorporate baseline outcome adjustment (which would reduce residual variance).
Supporting Documents and Materials

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IRB

Institutional Review Boards (IRBs)

IRB Name
Paris School of Economics
IRB Approval Date
2025-10-09
IRB Approval Number
PSE: IRB 2025-044
Analysis Plan

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