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.

What is autism and what are Trump's unproven claims about a Tylenol link? US President Donald Trump has claimed there is a link between the use of painkiller Tylenol by pregnant women and an increased risk of autism in some children. Going against current scientific advice and medical opinion, he said the drug, known as paracetamol in many countries, is no good and women should fight like hell to only take it in extreme cases, such as for high fevers. Medical bodies say the drug is safe and that it remains the best treatment for pain and fever during pregnancy. What is autism and how is it diagnosed?

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.

As minimally verbal autistic (MVA) children communicate with parents through few words and nonverbal cues, parents often struggle to encourage their children to express subtle emotions and needs and to grasp their nuanced signals. We present AACessTalk, a tablet-based, AI-mediated communication system that facilitates meaningful exchanges between an MVA child and a parent. AACessTalk provides real-time guides to the parent to engage the child in conversation and, in turn, recommends contextual vocabulary cards to the child. Through a two-week deployment study with 11 MVA child-parent dyads, we examine how AACessTalk fosters everyday conversation practice and mutual engagement. Our findings show high engagement from all dyads, leading to increased frequency of conversation and turn-taking. AACessTalk also encouraged parents to explore their own interaction strategies and empowered the children to have more agency in communication. We discuss the implications of designing technologies for balanced communication dynamics in parent-MVA child interaction.

Thomas VanCott compares his son Jake's experience with autism to life on a tightrope. Upset the delicate balance and Jake, 18, plunges into frustration, slapping himself and twisting his neck in seemingly painful ways. Like many families with children on the autism spectrum, Jake's parents sought treatments beyond traditional speech and behavioral therapies. One that seemed promising was magnetic e-resonance therapy, or MERT, a magnetic brain stimulation therapy trademarked in 2016 by a Newport Beach-based company called Wave Neuroscience. The company licensed MERT to private clinics across the country that offered it as a therapy for conditions including depression, PTSD and autism. Those clinics described MERT as a noninvasive innovation that could improve an autistic child's sleep, social skills and -- most attractive to the VanCott family -- speech. It was expensive -- 9,000 -- and not covered by insurance.

Recent studies have revealed that using social robots can accelerate the learning process of several skills in areas where autistic children typically show deficits. However, most early research studies conducted interactions via free play. More recent research has demonstrated that robot-mediated autism therapies focusing on core impairments of autism spectrum disorder (e.g., joint attention) yield better results than unstructured interactions. This paper aims to systematically review the most relevant findings concerning the application of social robotics to joint attention tasks, a cardinal feature of autism spectrum disorder that significantly influences the neurodevelopmental trajectory of autistic children. Initially, we define autism spectrum disorder and explore its societal implications. Following this, we examine the need for technological aid and the potentialities of robot-assisted autism therapy. We then define joint attention and highlight its crucial role in children's social and cognitive development. Subsequently, we analyze the importance of structured interactions and the role of selecting the optimal robot for specific tasks. This is followed by a comparative analysis of the works reviewed earlier, presenting an in-depth examination of two distinct formal models employed to design the prompts and reward system that enables the robot to adapt to children's responses. These models are critically compared to highlight their strengths and limitations. Next, we introduce a novel algorithm to address the identified limitations, integrating interactive environmental factors and a more sophisticated prompting and reward system. Finally, we propose further research directions, discuss the most relevant open questions, and draw conclusions regarding the effectiveness of social robotics in the medical treatment of autism spectrum disorders.

An artificial intelligence tool can detect autism spectrum disorder with 100-percent accuracy, just by scanning images of children's eyes, according to a new study. If confirmed, this would be a major breakthrough for detecting the condition. But multiple autism experts told DailyMail.com Autism affects an estimated 1 in 36 children in the US, but many children remain undiagnosed until later in childhood, depriving them of potential therapies. If a technological solution could help cut down on long waits for autism specialists or other obstacles to diagnosis, it could benefit millions of families.

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.

This extended abstract describes the preliminary quantitative and qualitative results coming from a therapeutic laboratory focused on the use of the Pepper robot to promote autonomies and functional acquisitions in highly functioning (Asperger) children with autism. The participants recruited were four highly functioning (Asperger) children, aged between 11 and 13 years. There have been in total 16 lab sessions, all recorded by a fixed camera, in addition to the Pepper's 2D cameras. Furthermore, trainees filled out evaluation forms provided by psychotherapists, noting the children autonomy's progress in a diary with the helping of rating scales [1]. These notes were then reworked to draw up shared reports, reflecting on the behavior's evolution and progress of the children meeting by meeting.