Empowering caregivers of individuals with autism spectrum disorder through sensor-based monitoring of emotional dysregulation: A scoping review.

Literature review is a fundamental part of a research process, and systematic protocols for this activity have been used for a long time, mainly in the field of health. Specifically in the Computer Science Education area, the use of systematic literature review has grown. One of the steps in a systematic literature review (SLR) is the selection of academic databases in which to search for articles. There are several databases with academic documents that may be relevant to SLR, for example: Google Scholar, which indexes different types of documents, such as articles, dissertations, theses, and others; Scopus and Web of Science are large databases that index articles from different conferences and journals. ACM Digital Library and IEEE Xplore are also important sources of information in the field of Computer Education. These tools have different characteristics, some charge a fee, others have only information about the title and authors and do not have access to the full article, others have advanced features, with many filters. In this context, this article presents the following research questions: RQ1) What metadata can be extracted automatically from the databases?; RQ2) What kind of visualization tools are available?; RQ3) Do the documents returned by the databases cover the research topic?; RQ4) Do the databases have papers from the main CSE venues?; and RQ5) How many databases are required to perform a literature review in CSE? To answer these questions we used five academic databases: Google Scholar, Scopus, Web of Science, ACM Digital Library, and IEEE xplore. Regarding the results, Scopus and Web of Science have the best visualization of the documents and a robust query engine, however those academic databases are not free. ACM Digital library, IEEE Xplore, Scopus and Web of Science allow the automatic download of the papers’ metadata (author, title, abstract, affiliation and others). Specifically in the field of Computer Science Education, the ACM Digital Library and the IEEE Xplore have important papers from conferences (SIGCSE and FIE) and journals (ACM Transaction on Education and IEEE Transaction on Education). In this full paper, the results will be presented to help researchers to choose the most appropriate academic databases based on their requirements and available options.

In the last five years, there has been a significant shift in how user interface (UI) and user experience (UX) design are approached within enterprise systems, reflecting the growing demand for more intuitive, adaptive, and inclusive solutions. This study employs a narrative review based on 1,500 initial records screened from Scopus, IEEE Xplore, ACM Digital Library, and Google Scholar (2019–2024). After rigorous selection, 82 empirical studies were included, focusing on user-centered design (UCD), adaptive interfaces, and inclusive practices in enterprise environments.. The review draws upon academic sources indexed in Scopus, IEEE Xplore, ACM Digital Library, and Google Scholar. Keywords including "Enterprise Systems," "User Experience," "Interface Design," and "Adaptive User Interfaces" were utilized to identify relevant literature, with inclusion criteria focusing on empirical studies from the last decade. Findings from 82 included studies show that UCD practices enhance usability and user satisfaction, with some reporting 20–30% higher usability scores and faster task completion rates when end-users are actively involved throughout development.. Adaptive interfaces employing machine learning have demonstrated potential to increase task efficiency and user engagement by personalizing content and layout. Moreover, inclusive design strategies, such as universal accessibility features and assistive technologies, contribute to improved user experiences across ability levels. However, systemic barriers like organizational resistance and limited training still hinder optimal implementation. The review highlights the need for strategic design interventions, ongoing usability assessments, and context-sensitive adaptations. As enterprise systems continue to evolve, future research must explore long-term effects of adaptive design and develop unified frameworks for inclusive, responsive interfaces. These efforts are vital to ensure equitable access and effectiveness of enterprise technologies across global and cross-sectoral contexts.

Autism spectrum disorder (ASD) is a collection of neuro-developmental disorders associated with social, communicational, and behavioral difficulties. Early detection thereof is necessary to mitigate the adverse effects of this disorder by initiating special education in schools and rehabilitation centers. Two methods are available for diagnosing and rehabilitating ASD. The first is the manual method (i.e., an observation- or interview-based approach), in which the disorder is diagnosed through observation or by interviewing a parent or caregiver. This method is time-consuming, subjective, and mostly comprises the examination of behavioral symptoms. The other method involves automatic diagnosis using traditional machine learning (ML)- and modern deep learning (DL)-based approaches that rely on image analysis. Indeed, the amount of research literature concerned with the evaluation of the usefulness of DL-based methods that process images or video data to diagnose ASD to improve patients’ lives has increased significantly. This paper presents a systematic review of the DL-based approach involving the analysis of images or videos in autism research. The review covers studies that were published from 2017 to June 2023 and were indexed in PubMed, IEEE Xplore, ACM Digital Library, and Google Scholar. The results are reported according to the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. A total of 130 studies were included in the analysis. Eligible papers were categorized based on the different features extracted as input for the DL-based approach. Existing well-known public and private datasets that include images or videos for autism research are extensively reviewed and discussed in this systematic review. In addition, different rehabilitation strategies that have been shown to be highly beneficial for ASD individuals are included. Finally, various challenges presented by the automated detection, classification, and rehabilitation of ASD are discussed. The review concludes that the use of DL for the precise and affordable diagnosis of autism is increasing substantially. Our findings are expected to significantly benefit researchers, therapists, psychologists, and relevant stakeholders to advance ASD screening, monitoring, and diagnosis with the aid of a DL-based approach that entails image or video analysis.

Repetitive and stereotyped behaviors (RSBs) are core symptoms of Autism Spectrum Disorder (ASD), and they affect the functionality of individuals with ASD. Robot assisted therapy can be beneficial for children with ASD in various ways, but relevant research focusing specifically on robot enhanced interventions (REIs) for RSBs in children with ASD has been limited. A scoping review was conducted to explore the role of REIs on RSBs of children with ASD and to investigate the components of REIs focusing on RSBs of younger and older children with ASD. A literature search was made in the databases of Scopus, PubMed, Web of Science, EBSCO, MEDLINE, and Google Scholar, using keywords pertaining to robots, ASD, RSBs, and children. Of the 89 studies identified, 10 met the inclusion criteria. They involved 99 participants aged 3-14 years (mean 7.27 years) from six countries on three different continents. These studies varied with respect to sample size, the research design, the robot used, the length of intervention, the training and the type of measurement. Following the application of most REIs, the participants showed reduction in RSBs. Only one study reported that REI led to some increase in stereotyped behaviors in children with ASD and one detected no training-related changes in repetitive behaviors. The review findings indicate the potential of REIs for reducing RSBs in children with ASD, but the relevant studies were diverse, and controlled studies with larger samples of children and rigorous design are needed to clarify their impact.

Anxiety–Amygdala Associations: Novel Insights From the First Longitudinal Study of Autistic Youth With Distinct Anxiety

SummaryBackgroundArtificial intelligence (AI) holds promise for developing tools that can track social behaviours and support clinical assessments and outcomes in Autism Spectrum Disorders (ASD). This review evaluated existing AI algorithms for extracting facial information during social interaction assessments and contributing to diagnostic accuracy for ASD assessment and response to therapy.MethodsSystematic review of studies on human participants with an ASD diagnosis, sourced from Medline, Embase, Scopus, Web of Science, IEEE Xplore, and ACM Digital Library, evaluated the diagnostic accuracy of AI algorithms in ASD classification and their use in tracking social development through facial information for clinical application in social interactions. Bivariate and multi-level models addressed dependencies, heterogeneity, moderators (modalities, algorithms, tasks), and applied robust variance estimation. Publication bias was evaluated with funnel plots. The QUADAS-2 tool assessed the risk of bias and applicability. This study was registered on PROSPERO (CRD42021249905).FindingsOf 40,570 studies identified, 38 met the review criteria, and seven provided sufficient data for meta-analysis. The pooled diagnostic odds ratio of 15.917 (95% CI [4.775–53.059]), and bivariate analysis estimated an area under the receiver operating characteristic curve of 0.862. Accuracy improved with facial features, unstructured play, support vector machines, and decision tree-based algorithms. AI methods can analyse social behaviours, including eye gaze on social stimuli, emotional expression, and joint attention in ASD assessments. AI-enabled robots have also been used to guide therapy.InterpretationThis study shows that AI can accurately and objectively augment ASD assessments, track social behaviours, and enhance therapy outcomes. Further validation in diverse populations is needed to ensure clinical applicability and ethical use.FundingNone.

Come Play With Me

Inherited and De Novo Genetic Risk for Autism Impacts Shared Networks.

Purpose This review explored the design and application of Artificial Intelligence (AI) technologies supporting neurodiverse users, including individuals with Autism Spectrum Disorder (ASD), ADHD, and dyslexia. It examined system types, application domains, inclusive and adaptive design strategies, user participation, and related ethical challenges. Materials and methods A systematic search across Web of Science, PubMed, ACM Digital Library, IEEE Xplore, and Google Scholar identified studies published between 2019 and 2025. After applying the inclusion criteria and conducting cross-validation, 117 peer-reviewed papers were analysed across five themes: technical features, design strategies, user engagement, effectiveness, and ethical considerations. Results Findings reveal a growing diversity of AI applications in education, healthcare, rehabilitation, and workplace contexts. Multimodal interaction, adaptive feedback, and embodied interfaces enhance engagement and usability; however, research remains fragmented and often lacks long-term perspectives. Most studies lack neurodivergent user participation and fail to adequately address sensory and cognitive heterogeneity, accessibility barriers, and gender bias in their datasets. Conclusions AI-driven interaction design shows strong potential to enhance inclusivity and personalisation for neurodiverse users. Sustained progress requires interdisciplinary collaboration, participatory co-design, and longitudinal evaluation. Ethical principles, particularly fairness, transparency, and accessibility, should guide the development of future AI systems to ensure equitable, evidence-based support.

Studies have shown that beyond public and self stigma, stigma can also impact family members. Only scant research has examined the internalised aspects of stigma, known as affiliate stigma, among family caregivers of individuals with disabilities. This study examined affiliate stigma among family caregivers of individuals with developmental disabilities via a comparison between caregivers of individuals with intellectual disabilities (ID), autism spectrum disorders (ASD) and physical disabilities (PD) in Israel. Family caregivers (n = 171) of individuals with developmental disabilities, mainly ID (22.4%), ASD (32.9%) and PD (27.1%), completed a self-report structured questionnaire including the Affiliate Stigma Scale and background variables. Results supported a one-factor structure for the Affiliate Stigma Scale. Overall, affiliate stigma was relatively low in this sample, but was found to be higher among caregivers of individuals with ASD when compared with caregivers of individuals with ID or PD. Findings from this study point to the importance of supporting caregivers of individuals with ASD to decrease their feelings of stigma. It is also important to further develop scales measuring affiliate stigma in order to capture the multi-dimensional nature of the concept.

Subjective well-being among family caregivers of individuals with developmental disabilities: The role of affiliate stigma and psychosocial moderating variables

Background: Children with Autism Spectrum Disorder (ASD) and Social Communication Disorder (SCD) face persistent impairments in language acquisition, social communication, and daily functioning. High-technology mobile applications have demonstrated efficacy in supporting communication and language development in these populations; however, the overwhelming majority of validated applications are developed in English and culturally oriented toward Western contexts. No prior systematic review has examined the development or effectiveness of mobile applications specifically designed in Urdu, Punjabi, or Sindhi — the three primary regional languages of Pakistan and South Asia — for children with ASD or SCD. Objective: To systematically identify, characterise, and appraise the methodological quality and effectiveness of high-technology mobile applications incorporating Urdu, Punjabi, or Sindhi language support for children (aged 0–18 years) with ASD or SCD. Methods: A systematic review was conducted in accordance with PRISMA 2020 guidelines. Five electronic databases — PubMed/MEDLINE, IEEE Xplore, Scopus, Web of Science, and Google Scholar — were searched in January 2025 from inception to search date. Two independent reviewers conducted dual screening at title/abstract and full-text stages; disagreements were resolved by consensus. Methodological quality was assessed using the Mixed-Methods Appraisal Tool (MMAT), JBI Quasi-Experimental Checklist, and CASP Quantitative Checklist, as appropriate to study design. Narrative synthesis was conducted using the SWiM framework. Results: Of 847 records identified, 28 studies met eligibility criteria after removal of 156 duplicates and staged screening. Seven studies constituted the core evidence base directly aligned with the review's South Asian regional language focus. Five studies incorporated Urdu as a primary application language; one study addressed Sindhi-language digital learning; no standalone study was identified for Punjabi. The only study covering all three target languages simultaneously (Hassan et al., 2025) demonstrated measurable speech production gains and significantly improved compliance. Quality scores ranged from 5 to 9 out of 10; three studies were rated High quality, one Moderate–High, two Moderate, and one Moderate–Low. The direction of reported effect was uniformly positive across all core studies, though only two employed validated, standardised outcome instruments in controlled designs. Conclusion: Urdu-language mobile applications for ASD and SCD children in Pakistan demonstrate feasibility and positive usability but lack controlled efficacy evidence. Punjabi represents a critical and entirely unaddressed evidence gap despite representing the primary language of Pakistan's largest population. Prioritised development and rigorous evaluation of Punjabi- and Sindhi-language mobile ASD applications are urgently needed to meet international inclusive education mandates and ensure linguistically equitable intervention access.

Over a half of all proteins are glycosylated, and their proper glycosylation is essential for normal function. Unfortunately, because of structural complexity of nonlinear branched glycans and the absence of genetic template for their synthesis, the knowledge about glycans is lagging significantly behind the knowledge about proteins or DNA. Using a recently developed quantitative high throughput glycan analysis method we quantified components of the plasma N-glycome in 99 children with attention-deficit hyperactivity disorder (ADHD), 81 child and 5 adults with autism spectrum disorder, and a total of 340 matching healthy controls. No changes in plasma glycome were found to associate with autism spectrum disorder, but several highly significant associations were observed with ADHD. Further structural analysis of plasma glycans revealed that ADHD is associated with increased antennary fucosylation of biantennary glycans and decreased levels of some complex glycans with three or four antennas. The design of this study prevented any functional conclusions about the observed associations, but specific differences in glycosylation appears to be strongly associated with ADHD and warrants further studies in this direction.

Artificial intelligence robots for mental health applications: a scoping review.

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterised by deficits in social communication and restricted, repetitive behaviours, with significant impacts on individuals and their families. In recent years, eye-tracking technology has emerged as a valuable tool for examining the cognitive and perceptual differences associated with ASD. Despite technological advancements, the diversity and complexity of this technology may pose challenges for researchers, particularly in fields such as psychology and special education. This review aims to provide a comprehensive overview of the use of eye-tracking technology in supporting individuals with ASD, focusing on three key areas: major research directions, applications of the technology, and the relationship between ASD-related impairments and eye movement measures. A thorough literature search was conducted across PubMed, Google Scholar, Scopus, Web of Science, and the ACM Digital Library, resulting in the inclusion of 170 studies following a rigorous selection process. The analysis of these studies reveals a sustained interest in the application of eye-tracking technology to autism research, as evidenced by the diverse range of research directions explored. For each direction, data collection and analysis methodologies are examined to identify best practices and key considerations. Accordingly, this review offers guidance for future research by highlighting current achievements, acknowledging existing limitations, and proposing promising directions for the continued use of eye tracking in ASD-related studies.