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Field
Trial Status
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Before
in_development
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After
on_going
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Last Published
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Before
March 20, 2026 10:58 PM
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After
March 21, 2026 11:47 PM
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Field
Intervention (Public)
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Before
The intervention consists of a randomized provision of short informational modules designed to influence participants’ perceptions of generative artificial intelligence. Each participant is randomly assigned to one of six treatment conditions or a control condition. All interventions are delivered within an online survey with no deception being used.
Each informational module highlights a distinct dimension of AI’s potential impact:
1. AI Skills and Usage Tutorial
2. Labor-Market Disruption
3. Labor-Market Opportunities
4. Algorithmic Bias
5. Privacy Risks
6. Geopolitics and Global Inequality in AI Capacity
7. Control Group
After receiving the assigned module, respondents complete a standardized set of questions measuring willingness to use or invest in AI tools, willingness to reskill, peer contact and social effects, willingness to supply data, trust in AI technologies, preferences for domestic AI regulation and promotion, preferences for AI’s role in public decision-making, preferences for national vs. international AI governance.
The intervention is minimal-risk and informational in nature.
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After
The intervention consists of a randomized provision of short informational modules designed to influence participants’ perceptions of generative artificial intelligence. Each participant is randomly assigned to one of six treatment conditions or a control condition. All interventions are delivered within an online survey with no deception being used.
Each informational module highlights a distinct dimension of AI’s potential impact:
1. AI Skills and Usage Tutorial
2. Labor-Market Disruption
3. Labor-Market Opportunities
4. Algorithmic Bias
5. Privacy Risks
6. Geopolitics and Global Inequality in AI Capacity
7. Control Group
After receiving the assigned module, respondents complete a set of questions measuring willingness to use or invest in AI tools, willingness to reskill, peer contact and social effects, willingness to supply data, trust in AI technologies, preferences for domestic AI regulation and promotion, preferences for AI’s role in public decision-making, preferences for national vs. international AI governance.
The intervention is minimal-risk and informational in nature.
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Intervention End Date
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Before
February 28, 2026
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After
April 30, 2026
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Primary Outcomes (End Points)
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Before
Primary outcomes are grouped into related families.
1.AI Adoption and Investment: Self-reported willingness to use generative AI tools, Willingness to invest in AI, Intention to integrate AI into current or future work tasks.
2.Skill Adaptation and Reskilling: Willingness to acquire AI-related or AI-complementary skills, Planned time investment in learning or practicing AI-related skills, Perceived importance of reskilling in response to AI.
3.Human Contact vs. AI Mediation: Openness to AI replacing human interaction in various domains, Preferred balance between human and AI involvement in personal and professional interactions.
4.Data Sharing and Data Monetization: Willingness to share personal or behavioural data with AI systems, Comfort with data being used to train AI models.
5.Domestic AI Policy and Governance Preferences: Support for government promotion of AI, Support for government regulation of AI, Support for using AI in public allocation and decision-making, Preferences for AI-related redistribution policies
6.International AI Governance and Cooperation: Preference for national vs. foreign / international AI , Support for global cooperation on AI
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After
The primary outcomes capture individuals’ intended behavioral responses to AI and their preferences regarding AI-related regulation and governance. Outcomes span six broad domains: (i) AI adoption and investment, (ii) reskilling and labor-market adjustment, (iii) social interaction and peer substitution, (iv) Data-sharing behavior, (v) trust and fairness of AI based decision making, and (vi) preferences for AI governance and regulation. All outcome variables are measured after treatment exposure.
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Primary Outcomes (Explanation)
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Before
The primary outcomes will be constructed as indices, combining multiple survey items into standardized measures.
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After
The primary outcomes will be measured using multiple individual survey items under each outcome domain and will be analyzed separately.
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Experimental Design (Public)
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Before
The study conducts an online randomized survey experiment to examine how different types of information about artificial intelligence can influence individuals’ beliefs, adoption intentions, and policy preferences. Participants are recruited from two populations, general online workers (e.g., MTurk) and skilled freelancers on a major freelancing platform, with an emphasis on respondents residing in low- and middle-income countries.
After providing baseline demographic information, each participant is randomly assigned to one of six informational treatment conditions that highlight the impact of AI or a control group. Following exposure to the assigned module, all participants complete a standardized set of outcome questions on AI-related adoption intentions, willingness to reskill, attitudes toward data sharing, and preferences for domestic and international AI governance.
Randomization is conducted at the individual level within the survey platform. Total planned sample size is approximately 3,500-4000 respondents.
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After
The study conducts an online randomized survey experiment to examine how different types of information about artificial intelligence can influence individuals’ beliefs, adoption intentions, and policy preferences. Participants are recruited from two populations, general online workers (e.g., MTurk) and skilled freelancers on a major freelancing platform, with an emphasis on respondents residing in low- and middle-income countries.
After providing baseline demographic information, each participant is randomly assigned to one of six informational treatment conditions that highlight the impact of AI or a control group. Following exposure to the assigned module, all participants complete a standardized set of outcome questions on AI-related adoption intentions, willingness to reskill, attitudes toward data sharing, and preferences for domestic and international AI governance.
Randomization is conducted at the individual level within the survey platform. Total planned sample size is approximately 3,500-4000 respondents.
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Field
Planned Number of Clusters
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Before
Approximately 3,500-4000 individuals (no higher-level clusters; randomization at the individual level).
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After
Approximately 3,000-4000 individuals (no higher-level clusters; randomization at the individual level).
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Planned Number of Observations
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Before
Approximately 3,500-4000 individuals.
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After
Approximately 3,000-4000 individuals.
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