The automatic detection of gaze targets in autistic children through artificial intelligence can be impactful, especially for those who lack access to a sufficient number of professionals to improve their quality of life. This paper introduces a new, real-world AI application for gaze target detection in autistic children, which predicts a child's point of gaze from an activity image. This task is foundational for building automated systems that can measure joint attention--a core challenge in Autism Spectrum Disorder (ASD). To facilitate the study of this challenging application, we collected the first-ever Autism Gaze Target (AGT) dataset. We further propose a novel Socially A ware Coarse-to-Fine (SACF) gaze detection framework that explicitly leverages the social context of a scene to overcome the class imbalance common in autism datasets--a consequence of autistic children's tendency to show reduced gaze to faces. It utilizes a two-pathway architecture with expert models specialized in social and nonsocial gaze, guided by a context-awareness gate module. The results of our comprehensive experiments demonstrate that our framework achieves new state-of-the-art performance for gaze target detection in this population, significantly outperforming existing methods, especially on the critical minority class of face-directed gaze.

Things to Do in L.A. Tap to enable a layout that focuses on the article. L.A. County gets a new tool to find and save vulnerable people with cognitive disabilities Jordan Wall, 27, of Chatsworth, -- an athlete, actor and global messenger for the Special Olympics -- wears her new GPS watch from the group L.A. Found on Oct. 15, 2025. The county program L.A. Found offers free tracking devices to residents with cognitive disabilities who are at risk of wandering away from home. Since launching seven years ago, more than 1,800 people have received devices through the program, with 29 successfully located after going missing. Janet Rivera cares for both her 79-year-old mother, who has dementia, and her 25-year-old son, who has a genetic condition called Fragile X syndrome.

Social narratives are known to help autistic children understand and navigate social situations through stories. To ensure effectiveness, however, the materials need to be customized to reflect each child's unique behavioral context, requiring considerable time and effort for parents to practice at home. We present AutiHero, a generative AI-based social narrative system for behavioral guidance, which supports parents to create personalized stories for their autistic children and read them together. AutiHero generates text and visual illustrations that reflect their children's interests, target behaviors, and everyday contexts. In a two-week deployment study with 16 autistic child-parent dyads, parents created 218 stories and read an average of 4.25 stories per day, demonstrating a high level of engagement. AutiHero also provided an effective, low-demanding means to guide children's social behaviors, encouraging positive change. We discuss the implications of generative AI-infused tools to empower parents in guiding their children's behaviors, fostering their social learning.

Artificial Intelligence (AI) technologies have been applied in various domains, including early childhood education (ECE). Integration of AI educational technology is a recent significant trend in ECE. Currently, there are more and more studies of AI in ECE. To date, there is a lack of survey articles that discuss the studies of AI in ECE. In this paper, we provide an up-to-date and in-depth overview of the key AI technologies in ECE that provides a historical perspective, summarizes the representative works, outlines open questions, discusses the trends and challenges through a detailed bibliometric analysis, and provides insightful recommendations for future research. We mainly discuss the studies that apply AI-based robots and AI technologies to ECE, including improving the social interaction of children with an autism spectrum disorder. This paper significantly contributes to provide an up-to-date and in-depth survey that is suitable as introductory material for beginners to AI in ECE, as well as supplementary material for advanced users.

An example of mocking interaction that has produced a lot of hilarity and has been revived during several meetings is related to the understanding by the children that pronounce at the beginning of the question the keywords have you ever been in... allowed access to the standard answer: no, but I would like to visit the whole world, even though the question was not completed with a real location, prompting participants to ask if Pepper had ever visited the Moon, heaven or the land of the stupidity. As per the goal of stimulating the social interaction between the participants, the technical difficulties that emerged during this phase encouraged the children's curiosity about the robot, encouraging collective discussion about how Pepper works and brainstorming suggestions for improvements to make it a more enjoyable conversation partner. On the other hand, the analysis of these phases, despite not having produced satisfactory therapeutic results, is extremely interesting to conduct an evaluation of interactions aimed at improving the dialogue system, pointing out that, even before technical problems, communication with the robot is deficient because it is not fluent in the slang used by children, compromising its position as a mediator because it is unable to act as an interpreter between children and specialized adults working in the laboratory.

According to observations, children with autism frequently speak more slowly than similarly developing kids. They differ in their speech in other ways, most notably in tone, intonation, and rhythm. It is very challenging to consistently and objectively describe these "prosodic" distinctions, and it has been decades since their roots have been identified. Researchers from Northwestern University and Hong Kong collaborated on a study to shed light on the causes and diagnoses of this illness. This method uses machine learning to find speech patterns in autistic children that are similar in Cantonese and English.

According to observations, children with autism frequently speak more slowly than similarly developing kids. They differ in their speech in other ways, most notably in tone, intonation, and rhythm. It is very challenging to consistently and objectively describe these "prosodic" distinctions, and it has been decades since their roots have been identified. Researchers from Northwestern University and Hong Kong collaborated on a study to shed light on the causes and diagnoses of this illness. This method uses machine learning to find speech patterns in autistic children that are similar in Cantonese and English.

Autism Spectrum Disorder (ASD) is found to be a major concern among various occupational therapists. The foremost challenge of this neurodevelopmental disorder lies in the fact of analyzing and exploring various symptoms of the children at their early stage of development. Such early identification could prop up the therapists and clinicians to provide proper assistive support to make the children lead an independent life. Facial expressions and emotions perceived by the children could contribute to such early intervention of autism. In this regard, the paper implements in identifying basic facial expression and exploring their emotions upon a time variant factor. The emotions are analyzed by incorporating the facial expression identified through CNN using 68 landmark points plotted on the frontal face with a prediction network formed by RNN known as RCNN-FER system. The paper adopts R-CNN to take the advantage of increased accuracy and performance with decreased time complexity in predicting emotion as a textual network analysis. The papers proves better accuracy in identifying the emotion in autistic children when compared over simple machine learning models built for such identifications contributing to autistic society.

Scientists have developed an artificial intelligence system that can allow robots to interact with autistic children undergoing therapy. People with autism see, hear and feel the world differently from other people, which affects how they interact with others. This makes communication-centred activities quite challenging for children with autism spectrum conditions (ASCs). To address this challenge, therapists recently began to use humanoid robots in therapy sessions. However, existing robots lack the ability to autonomously engage with children, which is vital for improving the therapy.

Researchers have developed a new personalized learning robot for autistic children that uses machine learning to adapt its lessons to each kid's changing needs. The University of Southern California team put a "socially assistive robot" called Kiwi in the homes of 17 autistic children and set the two-foot-tall, green-feathered robot to give each child personalized classes. Over the course of a month, the children played space-themed math games on a tablet device while Kiwi provided feedback and instruction, such as congratulating them on a correct answer or giving tips after a wrong one. As the lessons progressed, algorithms adjusted Kiwi's feedback and the difficulty of the games to the child's individual needs. By the end of the month, all of the children had improved their math skills, while 92% had also improved their social skills.