AI literacy is critical for young children as AI is rapidly integrated into people’s daily lives. However, the complexity of AI knowledge presents significant learning challenges, and there is currently a lack of effective approaches for converting complex AI concepts into easy-comprehend content. Based on the formative analysis, we propose Hello!AI, an interactive rhyme-based AI literacy education game targeted at children in Grades 2–6 of primary schools. Hello!AI comprises 3 modules: (i) Algorithm Adventure, focusing on basic AI concept learning, (ii) Algorithm Handbook, promoting thinking and reflection on AI algorithms, and (iii) City Builder, emphasizing the application of AI algorithm to solve real-life problems. We developed the prototype system, iterated it through pilot study, and then conducted a user study. The results demonstrate that Hello!AI can effectively engage children and, to a certain extent, improve their ability to understand and apply AI knowledge, as well as their thinking and reflective capabilities regarding AI technologies.

In the era of widespread digital art dissemination, visible watermarks provide immediate copyright identification by overlaying visible markers, addressing the lag issue of invisible watermarks that are difficult to prevent in advance due to post hoc evidence collection, thus meeting artists' needs for preemptive prevention and explicit protection. However, existing approaches struggle to balance aesthetics and functionality. To address this challenge, we conducted an exploratory study with watermarking experts, identifying key principles, six common design patterns, and a systematic watermarking workflow. Based on these insights, we developed an end-to-end, perceptual-aware framework for aesthetic-preserving watermark embedding, modeled after expert workflows in 5 phases. Using the Chain-of-Thought strategy, we optimized prompt instructions to guide the Vision-Language Model in emulating experts' decision-making, generating effective watermarking schemes and conducting objective visual evaluations. Iterative feedback optimization ensures watermarked images adhere to aesthetic principles. Quantitative and qualitative experiments demonstrate the system's superiority over baseline methods in preserving aesthetics and ensuring effective copyright protection.

The unique artistic style is crucial to artists’ occupational competitiveness, yet prevailing Art Commission Platforms rarely support style-based retrieval. Meanwhile, the fast-growing generative AI techniques aggravate artists’ concerns about releasing personal artworks to public platforms. To achieve artistic style-based retrieval without exposing personal artworks, we propose FedStyle, a style-based federated learning crowdsourcing framework. It allows artists to train local style models and share model parameters rather than artworks for collaboration. However, most artists possess a unique artistic style, resulting in severe model drift among them. FedStyle addresses such extreme data heterogeneity by having artists learn their abstract style representations and align with the server, rather than merely aggregating model parameters lacking semantics. Besides, we introduce contrastive learning to meticulously construct the style representation space, pulling artworks with similar styles closer and keeping different ones apart in the embedding space. Extensive experiments on the proposed datasets demonstrate the superiority of FedStyle.