Oral health directly influences children's overall well-being, yet pervasive dental anxiety has become an unavoidable barrier to pediatric dental care.While behavior management proves more effective than environmental or equipment improvements in reducing pediatric dental anxiety while improving treatment understanding and oral health awareness, its time-intensive nature often conflicts with dentists' demanding workloads.Through formative research, we proposed a structured combination of behavior management techniques within dental waiting rooms, employing tangible interactions to create a comprehensive anxiety relief system for children aged 5-10 years.The system encompasses two core processes, dental caries treatment and caries prevention.We conducted the validation experiment and pilot study to refine the system.Then we deployed a user study involving 32 child-parent groups.The results demonstrated that the system effectively alleviates children's dental anxiety, prepares them for dental visits, promotes parent-child interaction, and supports children's participation.

Picky eating behavior is common in preschoolers and has been linked to a lack of food literacy with support from certain research. Recent research has focused on interventions for children’s mealtime behaviors which can lead to distraction and neglect of food literacy learning. We propose FeastyMaze, a tangible and multi-sensory interactive puzzle game for young children to improve eating behavior. With the Five-color Diet Theory, our approach enables children to actively learn about food nutrition and balanced diets. To evaluate the effectiveness and acceptability of FeastyMaze, we conducted a user study with preschoolers (N = 12) who exhibited picky eating behaviors. The results showed that it effectively increased children’s familiarity and understanding of food knowledge, built positive attitudes towards previously disliked foods, and had the potential to improve their eating behavior.

Laser cutting revolutionizes the creation of personal-fabricated prototypes. These objects can have transformable properties by adopting different materials and be interactive by integrating electronic circuits. However, circuits in laser-cut objects always have limited movements, which refrains laser cutting from achieving interactive prototypes with more complex movable functions like mechanisms. We propose MechCircuit, a design and fabrication pipeline for making mechanical-electronical objects with laser cutting. We leverage the neodymium magnet’s natures of magnetism and conductivity to integrate electronics and mechanical structure joints into prototypes. We conduct the evaluation to explore technological parameters and assess the practical feasibility of the fabrication pipeline. And we organized a user-observing workshop for non-expert users. Through the outcoming prototypes, the result demonstrates the feasibility of MechCircuit as a useful and inspiring prototyping method.

The amount and quality of mathematical language in the family are positively associated with promoting children’s mathematical abilities. However, mathematical language in many families is poor. Through need-finding investigation, we developed MathKingdom, a voice-agent-based game that helps children aged 4–7 learn and use rich, accurate mathematical language (e.g., mathematical expressions related to measurement, sequence, patterns). The game has four flows, in which users can wake up, transform, decorate, and perform as their avatars, as well as practice basic mathematical vocabulary, mathematical single sentences, coherent mathematical statements, and free expression. We refined the system design through wizard-of-oz testing and then evaluated it with 18 families. The results showed that MathKingdom effectively engaged children, enhanced their mathematical language skills and mathematical abilities, and encouraged parent-child conversations about math.

Multi-Label Classification (MLC), which aims to assign multiple labels to each sample simultaneously, has achieved great success in a wide range of applications. MLC saves global label correlation by building a single model shared by all samples but ignores sample-specific local structures, while Personalized Learning (PL) is able to preserve sample-specific information by learning local models but ignores the global structure. Integrating PL with MLC is a straightforward way to overcome the limitations, but it still faces three key challenges. 1) capture both local and global structures in a unified model; 2) efficiently preserve high-order interactions among labels, features and samples; 3) learn a concise and interpretable model where only a fraction of interactions are associated with multiple labels. In this paper, we propose a novel Multi-Label Personalized Classification (MLPC) method to handle these challenges. For 1), it integrates local and global components to preserve sample-specific information and global structure shared across samples, respectively. For 2), a multilinear model is developed to capture high-order interactions, and over-parameterization is avoided by tensor factorization. For 3), exclusive sparsity regularization penalizes factorization by promoting intra-group competition, thereby eliminating irrelevant and redundant interactions during Exclusive Sparse Tensor Factorization (ESTF). Moreover, theoretical analysis reveals the equivalence between MLPC with a family of jointly regularized counterparts. We develop an alternating algorithm to solve the optimization problem, and extensive experiments on various datasets demonstrate its effectiveness.

Visual storytelling is a new approach to creative expression based on verbal and figural creativity. The keys to visual storytelling are narrating and drawing over a period of time, which can be beneficial but also demanding on creativity for children. Informed by need-finding investigations, we developed StoryDrawer, a co-creative system that supports visual storytelling for children aged 6–10 years through collaborative drawing between children and artificial intelligence (AI). The system includes a context-based voice agent and two AI-driven collaborative strategies: the real-time transformation of children's telling into drawings, and the generation of abstract sketches with semantic similarity to existing story content. We conducted a 2 × 2 study with 64 children to evaluate the efficacy of StoryDrawer by varying the two strategies in four conditions. The results suggest that StoryDrawer provoked participants’ creative and elaborate ideas and contributed to their creative outcomes during an engaging visual storytelling experience.