Evidence map›Paper›PMID 42347701›Full record

ArticleJournal of medical Internet research2026

How US Youth Use AI Chatbots: Conversation Patterns From Naturalistic Keystroke Observations.

Anne J Maheux, Debra Boeldt, Samir Akre-Bhide, Jessica Flannery, Allison Paige, Giavanna Villella, Kaitlyn Burnell, Eva H Telzer, Scott H Kollins

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Anne J MaheuxDepartment of Psychology & Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.ORCID https://orcid.org/0000-0003-1308-5798
Debra BoeldtAura, Boston, MA, United States.ORCID https://orcid.org/0000-0001-6127-592X
Samir Akre-BhideAura, Boston, MA, United States.ORCID https://orcid.org/0000-0003-1495-6399
Jessica FlanneryAura, Boston, MA, United States.ORCID https://orcid.org/0000-0003-0710-2512
Allison PaigeAura, Boston, MA, United States.ORCID https://orcid.org/0009-0002-6847-2074
Giavanna VillellaAura, Boston, MA, United States.ORCID https://orcid.org/0009-0006-6982-7446
Kaitlyn BurnellDepartment of Psychology & Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.ORCID https://orcid.org/0000-0003-0959-6632
Eva H TelzerDepartment of Psychology & Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.ORCID https://orcid.org/0000-0002-2351-6339
Scott H KollinsAura, Boston, MA, United States.ORCID https://orcid.org/0000-0001-6847-6935

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundYouth are increasingly engaging with generative AI (GenAI) chatbots; yet, little is known about the nature of their interactions.

objectiveThis study used naturalistic behavioral observations of keystrokes typed into GenAI mobile apps to characterize the topics youth discuss with chatbots, describing themes by age and app, word count by theme (a proxy for quantity of engagement), and co-occurring themes.

methodsParticipants were 3363 US youth aged 1-17 years using the Aura parental monitoring app, a commercially available service that guardians install on children's mobile devices for digital safety. Participants included only those who opted into the overlay Aura keyboard (to collect keystroke data) and who engaged in some degree of GenAI use. Participants' keystrokes for distinct GenAI apps by day (ie, user-app-days) were coded using a large language model with human review.

resultsUsage patterns were highly variable, with most users accessing only 1 to 2 GenAI apps (range 1-15) across a median of 4 days (IQR 1-11, range 1-247; 0.4%-90% of days during the study period), sending a median of 6 (IQR 2-27, range 2-2865) messages per app-day. Results identified 11 mutually inclusive themes, including functional tool use; social practice; role-play, including general, romantic, and sexual; emotional support; friend-like interactions; and violence. Most user-app-days involved using AI as a tool (26,343/42,355, 62%), but notable proportions included violence (6460/42,355, 15%), sexual role-play (6393/42,355, 15%), romantic role-play (3538/42,355, 8%), general role-play (3707/42,355, 9%), and friend-like interaction (2624/42,355, 6%). Theme prevalence varied by age: tool use was more common among older adolescents, whereas violence and sexual or romantic role-play were relatively more common among younger users. Themes also varied by app, with general-purpose apps most often used as tools and companionship or role-play apps showing higher usage of sexual, romantic, general role-play, and violent content; however, ChatGPT appeared across all themes due to high overall use. Word counts were highest for user-app-days involving violence, followed by sexual role-play, romantic role-play, and emotional support, suggesting greater engagement when using GenAI in these ways. Co-occurrence analyses showed that tool use often appeared alone, whereas violence, sexual role-play, and romantic role-play frequently co-occurred within a user-app-day, including instances suggestive of violent sexual role-play.

conclusionsFindings suggest that youth engagement with GenAI varies substantially across users. Tool use is most common, requiring more nuanced investigation into the precise ways youth use GenAI to potentially support or thwart their development. GenAI engagement also may involve developmentally salient and potentially risky themes of sexuality, romance, and violence, especially among younger users. More research is needed that pairs GenAI engagement with psychological measures of well-being to examine the impacts of AI interactions and promote safety-focused design features.

Indexed as

CommunicationMobile ApplicationsAdolescentChildChild, PreschoolFemaleGenerative Artificial IntelligenceHumansInfantMaleUnited StatesadolescenceAIartificial intelligencedigital behaviorGenAIgenerative artificial intelligencehuman-AI interactionpassive sensingyouth

Identifiers

PMID42347701
PMCPMC13458353

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.