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Related Experiment Video

Updated: Jun 26, 2026

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models

Published on: December 23, 2025

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

Anne Maheux¹, Debra Boeldt², Samir Akre-Bhide²

¹900 Willow Dr, Department of Psychology & Neuroscience, University of North Carolina at Chapel Hill, 235 E Cameron Ave, Chapel Hill, US.

Journal of Medical Internet Research

|June 25, 2026

Summary

This summary is machine-generated.

Youth interactions with generative AI (genAI) chatbots vary widely. While tool use is common, risky themes like violence and sexual role-play emerge, particularly in younger users, necessitating further research on developmental impacts.

Related Experiment Videos

Last Updated: Jun 26, 2026

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models

Published on: December 23, 2025

Area of Science:

Digital Health
Human-Computer Interaction
Adolescent Psychology

Background:

Youth are increasingly using generative artificial intelligence (genAI) chatbots.
Understanding the nature of these youth-genAI interactions is limited.

Purpose of the Study:

To characterize youth discussions with genAI chatbots by analyzing naturalistic keystroke data.
To describe interaction themes by age and app, engagement quantity, and theme co-occurrence.

Main Methods:

Utilized keystroke data from 3,363 U.S. youth (ages 1-17) via the Aura parental monitoring app.
Coded interactions using a large language model with human review for distinct genAI apps and user-app-days.
Analyzed themes, word counts (proxy for engagement), and co-occurring themes across different age groups and applications.

Main Results:

Identified 11 themes, with tool use being most common (62%).
Significant proportions of interactions involved violence (15%), sexual role-play (15%), and romantic role-play (8%).
Theme prevalence varied by age and app; younger users showed more violence/sexual themes, older users more tool use. Violence and sexual/romantic role-play often co-occurred.

Conclusions:

Youth genAI engagement is highly variable, with tool use prevalent but potentially risky themes also present.
Developmentally salient themes of sexuality, romance, and violence require nuanced investigation, especially for younger users.
Further research linking genAI use with well-being measures is crucial for promoting safety and informing design.