AI & ML
Lilo: A Multi-Agent Conversational Sandbox for Children's Digital Communication
arXiv cs.HC0 views
arXiv:2611.14260v1 Announce Type: new Abstract: Children's peer conflicts increasingly play out in group chats, where tone and timing are stripped away, and a hurtful message is easier to send than to say aloud. We present Lilo, a multi-agent conversational system that lets children practice such conversations, so they are better prepared when they encounter them in real digital communication. In Lilo, the child joins two AI-driven peers mid-argument, coached by a mentor character, also named Lilo, through a Stop-Think-Act traffic light. Through an exploratory study with 28 children (aged 9-11), we examine how children engage with Lilo's simulated peer conflict and the mentor's traffic-light interventions. The study included an intervention with Lilo, surveys before and after the intervention, and exit interviews. Participants reported significantly higher self-efficacy for coping with unpleasant messages after their interaction with Lilo. They felt safe experimenting, knowing nothing they wrote would be held against them.
Source:https://arxiv.org/abs/2611.14260PreviousUneven Self-Reported AI Use: An Embedded Case Study Across Eleven Teams in a Software OrganizationNextNinety-Six Ways to Measure a Scribe: Fragmented Evaluation of Ambient Scribes in Healthcare
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