Advanced International Journal for Research

E-ISSN: 3048-7641   •   Impact Factor: 9.11

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Generative AI in Child-Robot Interaction: An Adaptive Framework with Safety and Developmental Alignment

Author(s) Mr. Frederick Kwame Minta, Mr. Fitsum Gedefaw Legese, Ms. Adiza Alhassan
Country Ethiopia
Abstract Traditional child-robot interaction (CRI) systems constrained by scripted dialogue lack adaptability and engagement sustainability. This paper presents the GAI-CRI Adaptive Framework (GCAF), which integrates generative AI with three core components: Adaptive Cognition (real-time personalization via multimodal sensing), Developmental Alignment (age-appropriate content generation), and Ethical Safeguards (multi-layer content filtering). A controlled study with N = 42 children (M age = 9.3 years, ages 7–12) compared GCAF with scripted baselines. Results demonstrate GCAF significantly improved engagement (35.1% eye gaze increase, t(41) = 4.82, p ¡ 0.001), child-initiated interaction (41.8% more turns, t(41) = 5.23, p ¡ 0.001), and learning outcomes (184.4% gain, d = 2.20, p ¡ 0.001). Qualitative analysis identified five themes enhancing interaction quality (κ = 0.78). Safety evaluation achieved 96.2% accuracy in harmful content detection with minimal disruption. The framework demonstrates responsible GAI deployment in child-facing systems is achievable through architectural design and formal constraints.
Keywords Generative AI, Child-Robot Interaction, Adaptive Learning, Developmental Psychology, AI Ethics
Field Computer > Automation / Robotics
Published In Volume 6, Issue 5, September-October 2025
Published On 2025-10-29
DOI https://doi.org/10.63363/aijfr.2025.v06i05.1743
Short DOI https://doi.org/g99qn2
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