The study examines how teenagers discuss and navigate generative AI in their daily lives, using data from r/teenagers between January 2023 and July 2026. Analysis of 11,083 posts identified eight interconnected domains of experience, with everyday and social use being most common. Over time, discussions shifted toward concerns about authenticity, personal control, safety, and the future role of humans versus AI, highlighting adolescents’ active negotiation of AI’s place in their lives.
By Jianfeng Zhu
Explore OpenAI’s approach to balancing teen safety, freedom, and privacy in AI use.
arXiv:2608. 07902v1 Announce Type: cross Abstract: Youth increasingly turn to AI chatbots for social and emotional support, raising concerns about how these systems respond, especially in high-stakes situations.
By Hannah Cha, Neha Shukla, Solon Barocas, Alexandra Chouldechova, Eugenia Kim, Jennifer Wortman Vaughan
Learn how OpenAI is making ChatGPT safer for teens with age-appropriate protections, learning tools, parental controls, and expert partnerships.
The paper systematically reviews 100 empirical HCI studies on children and youth interacting with AI in various settings. Using the YAIR risk taxonomy and MIT Mitigation Taxonomy, it maps identified risks to countermeasures, finding that most risks are only matched with proposed countermeasures, few are implemented, and even fewer are evaluated. The authors highlight gaps in coverage and testing, and suggest concrete research directions to improve youth AI safety.
By Pratyasha Saha, Yaman Yu, Yang Wang
The study investigates how community‑led humanitarian responders in Sudan’s Emergency Response Rooms use Generative AI during armed conflict. Through surveys and interviews, it finds that responders rely on GenAI when human expertise is hard to reach, yet they warn that the technology cannot independently capture rapidly changing local conditions and may lead to harmful decisions if not verified. Risks also arise before use, such as exposure to danger from carrying devices or sharing sensitive information, and concerns that AI‑assisted reports could damage credibility with donors.
By Dalia Ali, Yearin Lee, Cheng Yu, Ro Encarnaci\'on, Alsanosi Adam, Dana\'e Metaxa, Ingmar Weber, Orestis Papakyriakopoulos
OpenAI calls for global action on youth AI safety, proposing an international institute to strengthen safeguards, standards, and opportunities for young people.
The study explores how children design AI chatbots and what they consider trustworthy. Using a custom chatbot-building environment, 115 learners aged 8‑18 created 119 chatbots and adjusted traits such as confidence, transparency, and formality. Findings show younger children equate trust with purpose‑fulfillment, while older children focus on transparent, calibrated design, and that students calibrate academic chatbots to be more formal and transparent than hobby ones.
By Deniz Ozturk, Jiayu Li, Daksh Pratap Singh, Yasitha Rajapaksha, Fasika Melese, Bahare Riahi, Shiyan Jiang, Qiao Jin, Joey Huang, Veronica Catet\'e, Tiffany Barnes, Xiaoyi Tian
Discover OpenAI’s Teen Safety Blueprint—a roadmap for building AI responsibly with safeguards, age-appropriate design, and collaboration to protect and empower young people online.
The study evaluates the safety of 18 AI chatbot APIs in simulated conversations with adolescents aged 13‑17, using 32,555 dialogues across 983 scenario variants. It finds that overall safety failures drop from 43.3% to 24.9% when a teen‑aware prompt is used, yet failures remain significant, especially when teens challenge the chatbot’s responses. The results highlight that safety depends on conversational dynamics and that teen‑aware prompting can mitigate but not eliminate risks.
By Andrea Mock, Valerie Chen, Eric Lin, Rayan Krishnan, Diyi Yang, Caroline Figueroa
arXiv:2607. 19699v1 Announce Type: cross Abstract: The rise of generative AI (GenAI) in higher education has prompted urgent debates surrounding academic integrity and ethical use.
By Brian Harrington, Irina Zlotnikova, Gayathri Nadarajan, Samuel Ekundayo
The paper presents a child-centered harm taxonomy for AI systems and analyzes 1,331 publicly reported child-related AI incidents. It finds that harms are concentrated in representational, dignity-related, and psychological categories, often linked to deployment-context, governance, and misuse failures rather than model defects. The study highlights that model-level evaluations alone cannot anticipate the harm pathways most frequently documented for children, and recommends broader mitigations such as age assurance, consent verification, safety-by-design UX constraints, incident disclosure norms, and targeted auditing regimes.
By Junfeng Jiao, Kevin Chen, Shrestha Mishra, Abhejay Murali, David Atkinson, Shriti Raj, Saleh Afroogh