mlsanityData to 14 Oct 2026

Understanding Parents' Desires in Moderating Children's Interactions with GenAI Chatbots through LLM-Generated Probes

arXiv 2603.03727 ↗First posted 4 Mar 2026cs.HC cs.AIcited by 2

Not on the map. Why?

Abstract (arXiv)

This paper studies how parents want to moderate children's interactions with Generative AI chatbots, with the goal of informing the design of future GenAI parental control tools. We first used an LLM to generate synthetic child-GenAI chatbot interaction scenarios and worked with four parents to validate their realism. From this dataset, we carefully selected 12 diverse examples that evoked varying levels of concern and were rated the most realistic. Each example included a prompt and a GenAI chatbot response. We presented these to parents (N=24) and asked whether they found them concerning, why, and how they would prefer the responses to be modified and communicated. Our findings reveal three key insights: (1) parents express concern about interactions that current GenAI chatbot parental controls neglect; (2) parents want fine-grained transparency and moderation at the conversation level; and (3) parents need personalized controls that adapt to their desired strategies and children's ages.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 14 of its 105 references that are in the map. First posted on arXiv 4 Mar 2026.

Builds on

14 of 105 references.

References · 14

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  3. 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,568
  4. 2022Decomposed Prompting: A Modular Approach for Solving Complex Taskscited by 558
  5. 2024A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applicationscited by 431
  6. 2023"Kelly is a Warm Person, Joseph is a Role Model": Gender Biases in LLM-Generated Reference Letterscited by 199
  7. 2023Identifying and Mitigating the Security Risks of Generative AIcited by 91
  8. 2024Evaluating Cultural and Social Awareness of LLM Web Agentscited by 45
  9. 2024Identifying and Manipulating Personality Traits in LLMs Through Activation Engineeringcited by 14
  10. 2025Traceability and Accountability in Role-Specialized Multi-Agent LLM Pipelinescited by 10
  11. 2022From Parental Control to Joint Family Oversight: Can Parents and Teens Manage Mobile Online Safety and Privacy as Equals?cited by 2
  12. 2022Guidelines to Develop Trustworthy Conversational Agents for Childrencited by 2
2 more
  1. 2025The Pitfalls of "Security by Obscurity" And What They Mean for Transparent AIcited by 2
  2. 2025SET-PAiREd: Designing for Parental Involvement in Learning with an AI-Assisted Educational Robotcited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.