Nearly 1 in 3 young adults in the U.S. reported using generative AI to seek substance-related information, with higher rates of use among Black and male respondents, according to a study published Oct. 19 in JMIR Formative Research.
Researchers at Rutgers, the State University of New Jersey in New Brunswick, surveyed 995 U.S. adults ages 18-29 in April 2026 through the online research platform Prolific. The cross-sectional study examined the prevalence and frequency of, and reasons for, substance-related AI use, along with demographic differences and perceptions of the technology.
Here are five things to know:
1. About one-third used AI for substance-related information. Overall, 32.3% of respondents reported using generative AI for substance-related purposes, compared with 57.5% who reported conducting substance-related Google searches.
2. Most sought information about substance-related risks. Among respondents who used AI for substance-related purposes, 92.8% sought information about the consequences of substance use, 48.6% sought substance-specific information and 34.9% sought information related to substance use disorders.
3. Black and male respondents were more likely to report use. Black respondents had higher adjusted odds of substance-related AI use than white respondents (adjusted odds ratio, 2.52), while male respondents had higher adjusted odds than female respondents (adjusted odds ratio, 2.04).
4. Nearly half used AI at least monthly. Among respondents reporting substance-related AI use, 48.2% used it at least monthly, including 32.1% monthly, 12.1% weekly and 4% daily or almost daily.
5. AI was perceived as more trustworthy for substance-related information. Respondents rated the trustworthiness of AI for substance-related purposes higher than for general use, with mean scores of 3.66 and 3.51, respectively, on a 5-point scale.
Researchers said further studies are needed to assess the accuracy of AI-generated substance-related information and how young adults interpret and act on it. They cautioned that the online, nonprobability sample could limit the findings’ generalizability.
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