How Much Are AI Chatbots Worth to Students? A Cross-Country Discrete Choice Experiment on Preferences and Future Expectations

Last registered on May 18, 2026

Pre-Trial

General Information

Title

RCT ID

AEARCTR-0018605

Initial registration date

May 11, 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

May 18, 2026, 4:08 AM EDT

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

Locations

Country

Bulgaria

Region

Country

Germany

Region

Country

France

Region

Country

Italy

Region

Country

Portugal

Region

Country

Sweden

Region

Primary Investigator

Name

Andrea Blasco

Affiliation

European Commission's JRC

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Margherita Benedetti

PI Affiliation

University of Trento

Contact Investigator

Additional Trial Information

Status

In development

Start date

2026-05-02

End date

2026-05-22

Keywords

Behavior, Education

Additional Keywords

Artificial Intelligence, education, discrete choice experiment, information provision, career choices

JEL code(s)

Secondary IDs

Prior work

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

Abstract

This study examines how information about the labour-market relevance of artificial intelligence affects students’ preferences for AI chatbot services, intended use of AI for studying, and expectations about future careers. The study is conducted as an online randomised survey experiment with an embedded discrete choice experiment among 2,310 students aged 18-25 in six European countries. Participants receive information on either the labour-market value of AI skills or the extent to which occupations can be performed or supported by generative AI. The discrete choice experiment elicits preferences for AI chatbot subscription plans that vary in price, accuracy, privacy, usage limits, task domain, and image-generation features. Primary outcomes include chatbot choices, intended study use, perceptions of AI tools, career expectations, employment anxiety, and interest in AI-skills training. The study estimates the causal effects of information provision on these outcomes and on the valuation of chatbot attributes.

External Link(s)

Registration Citation

Citation

Benedetti, Margherita and Andrea Blasco. 2026. "How Much Are AI Chatbots Worth to Students? A Cross-Country Discrete Choice Experiment on Preferences and Future Expectations." AEA RCT Registry. May 18. https://doi.org/10.1257/rct.18605-1.0

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

The survey includes two information provisions. The first provides information on the labor-market value of AI skills; the second provides information on the extent to which tasks in future occupations can be performed or supported by generative AI. The DCE elicits preferences over AI chatbot subscription plans that vary in monthly price, usage limits, accuracy, data storage/privacy, task domain, and image-generation limits.

Intervention Start Date

2026-05-02

Intervention End Date

2026-05-15

Primary Outcomes

Primary Outcomes (end points)

For the first intervention, we consider the following primary oucomes: the eight DCE subscription-plan choices (question: Q8). In the respondent-level file, each task records whether the respondent chooses option A, option B, or Neither. The key outcomes are (1) the willingness to pay for the AI chatbot plans and (2) the preference weights of the randomized plan attributes: price, usage limits, accuracy, data storage/privacy, task domain, and image-generation limits. We will also test the effect of the intervention on intention to use AI chatbots for studying in the future (Q12); employment anxiety (Q21) and interest in AI training (Q23)

For the second intervention, we consider the following primary outcomes: Career-related measures of the perceived impact of AI on their ideal job collected after the second information provision (Q19); Confidence that their skills will match future demand of labour (Q20); employment anxiety (Q21); and interest in AI training (Q23).

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

Secondary Outcomes (explanation)

Experimental Design

Eligible respondents will be randomly assigned at the individual level to two independent information provisions (no blocks or clusters). The first provides information on the labour-market value of AI skills; the second provides information on the exposure of occupational tasks to generative AI. Each provision is assigned with probability 0.5, creating four experimental conditions: no information, AI-skills information only, AI-task-exposure information only, and both information provisions.

The DCE attributes were randomly combined into paired A/B alternatives using the Efficient Design Tool (EDT), a Python-based framework for constructing D-efficient discrete choice experiment designs with prior parameter estimates (Hernández 2024). EDT applies a random-swapping optimisation algorithm (Quan et al. 2011) to generate statistically efficient choice sets while minimising the variance of the estimated coefficients. The resulting design enabled the construction of balanced and efficient experimental alternatives for the study.

Experimental Design Details

Not available

Randomization Method

Completely at random indpendently for each treatment; random number generator.

Randomization Unit

individual respondent

Was the treatment clustered?