Exploring AI Companions Together: A Structured Family Co-Investigation of Character.AI

Exploring AI Companions Together: A Structured Family Co-Investigation of Character.AI

Keywords: generative AI, AI companions, children, parents, parental mediation, character.ai

Keywords: generative AI, AI companions, children, parents, parental mediation, character.ai

Teens today are increasingly drawn to emotionally responsive AI systems, often called AI companions. As these technologies grow in popularity, families face new questions about mediating teens' interactions with them. Traditional mediation strategies, such as monitoring or restriction, often fall short when interactions are private, emotionally persuasive, and relational. In this study, we examine how families, parents and teens, can jointly explore, interpret, and negotiate the use of AI companions together. We conducted a four-week study with 11 families with teens aged 13–15, centered on Character.AI and structured around activities that progressed from joint exploration to teen independent use. Our findings show that co-exploration fostered family connection, supported joint calibration of AI behaviors and limits, gave parents real-time insight into teens’ decision-making, and prompted families to reassess their assumptions about AI companions. We conclude with implications and recommendations for designing parental-mediation tools that help scaffold healthy teen-AI companion use.

Authors:

Meghna Gupta, Mitsuka Kiyohara, Ranjitha Rangaswamy, Franziska Roesner, Julie A. Kientz

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Keywords: participatory design, children, AI literacy, sustainability, generative AI, environmental impact

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To appear in ACM IDC 2026

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ITiCSE 25 Working Group

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Proceedings of the 2025 Working Group Reports on Innovation and Technology in Computer Science Education

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Keywords: student–project allocation, computing education, fairness in group formation

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Proceedings of the 2025 Working Group Reports on Innovation and Technology in Computer Science Education

2/2026

Keywords: student–project allocation, computing education, fairness in group formation

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