The advent of Generative AI (GenAI) has raised discussions about its effects on individuals. However, little is known about its impact on children’s creative collaboration, despite its importance for social and cognitive development. We examined GenAI’s role in children’s creative collaboration through five co-design sessions with 28 children (ages 5-11) using diverse GenAI tools (text, image, video, voice); 17 parents participated in focus group interviews. Our findings show that GenAI can foster positive social dynamics by enabling “Human vs. AI” teaming and children’s co-creation with shared ownership. However, GenAI disrupted collaborations when roles between children were unclear, AI ignored group dialogue, and AI dominated children’s agency. Children and parents envisioned socially attuned AI that could play an “older sibling” role–scaffolding while allowing playful disagreement–while raising concerns about children’s overreliance on GenAI. This work advances understanding of GenAI in collaboration and proposes design implications for designing AI systems that support child-centered collaboration.

A typical sense-plan-act robotics pipeline is brittle due to the inherent inaccuracies in the output of the sensing module and the lack of awareness of the planning module to those inaccuracies. This paper develops a framework to predict uncertainty estimates for neural network-based vision models used for state estimation in robotics pipelines. Our uncertainty estimates are based directly on the image observation data and are explicitly trained to model the error distribution on a held-out calibration set. We also demonstrate how predicted uncertainties can be used to select robust control strategies. We conduct experiments on the mobile manipulation problem of articulating everyday objects (e.g. opening a cupboard) and demonstrate the quality of estimated uncertainty and its downstream impact on robustness of inferred control strategies.

N6-methyladenosine (m6A) is the most abundant mRNA modification within mammalian cells, holding pivotal significance in the regulation of mRNA stability, translation and splicing. Furthermore, it plays a critical role in the regulation of RNA degradation by primarily recruiting the YTHDF2 reader protein. However, the selective regulation of mRNA decay of the m6A-methylated mRNA through YTHDF2 binding is poorly understood. To improve our understanding, we developed m6A-BERT-Deg, a BERT model adapted for predicting YTHDF2-mediated degradation of m6A-methylated mRNAs. We meticulously assembled a high-quality training dataset by integrating multiple data sources for the HeLa cell line. To overcome the limitation of small training samples, we employed a pre-training-fine-tuning strategy by first performing a self-supervised pre-training of the model on 427 760 unlabeled m6A site sequences. The test results demonstrated the importance of this pre-training strategy in enabling m6A-BERT-Deg to outperform other benchmark models. We further conducted a comprehensive model interpretation and revealed a surprising finding that the presence of co-factors in proximity to m6A sites may disrupt YTHDF2-mediated mRNA degradation, subsequently enhancing mRNA stability. We also extended our analyses to the HEK293 cell line, shedding light on the context-dependent YTHDF2-mediated mRNA degradation.