Can LANA CITS Support Learning in Autistic Children? A case study evaluation

LANA CITS is a Conversational Intelligent Tutoring System that uses the Visual, Auditory, and Kinaesthetic learning style (VAK). It supports learning in autistic pupils, who are studying in mainstream primary schools. Facilitating the learning of these pupils using traditional teaching within mainstream schools is complex and poorly understood. This paper present investigation into how LANA CITS using VAK learning style model can adapt to autistic pupils learning style and improve their learning in mainstream schools. This paper provides a case study evaluation of three children with high-functioning autism examining the effectiveness of learning with LANA CITS. The case study took place in primary school in Saudi Arabia. The results were positive with the students engaged in the tutorial and the teacher noticed some improvement over classroom activities. This results support for the continuing development, evaluation, and use of CITS for pupils with autism in mainstream schools

Investigating the Experience of Social Engineering Victims: Exploratory and User Testing Study

The advent of mobile technologies and social network applications has led to an increase in malicious scams and social engineering (SE) attacks which are causing loss of money and breaches of personal information. Understanding how SE attacks spread can provide useful information in curbing them. Artificial Intelligence (AI) has demonstrated efficacy in detecting SE attacks, but the acceptability of such a detection approach is yet to be investigated across users with different levels of SE awareness. This paper conducted two studies: (1) exploratory study where qualitative data were collected from 20 victims of SE attacks to inform the development of an AI-based tool for detecting fraudulent messages; and (2) a user testing study with 48 participants with different occupations to determine the detection tool acceptability. Overall, six major themes emerged from the victims’ actions “experiences: reasons for falling for attacks; attack methods; advice on preventing attacks; detection methods; attack context and victims”. The user testing study showed that the AI-based tool was accepted by all users irrespective of their occupation. The categories of users’ occupations can be attributed to the level of SE awareness. Information security awareness should not be limited to organizational levels but extend to social media platforms as public information

A Multimodal Messaging App (MAAN) for Adults With Autism Spectrum Disorder: Mixed Methods Evaluation Study

BackgroundIndividuals with autism spectrum disorder (ASD) often exhibit difficulties in social and communication skills. For more than 30 years, specialists, parents, and caregivers have used techniques, such as applied behavioral analysis, augmentative and alternative communication, and the picture exchange communication system to support the social and communication skills of people with ASD. Even though there are many techniques devised to enhance communication, these techniques are not considered in existing social media apps for people with ASD. ObjectiveThis study aimed to investigate the effect of adding accessibility features, such as text-to-speech (TTS), speech-to-text (STT), and communication symbols (CS), to a messaging app (MAAN). We hypothesized that these accessibility features can enhance the social and communication skills of adults with ASD. We also hypothesized that usage of this app can reduce social loneliness in adults with ASD. MethodsSemistructured interviews were conducted with 5 experts working in fields related to ASD to help design the app. Seven adults with ASD participated in the study for a period of 10 to 16 weeks. Data logs of participants’ interactions with the app were collected. Additionally, 6 participants’ parents and 1 caregiver were asked to complete a short version of the Social and Emotional Loneliness Scale for Adults (SELSA-S) questionnaire to compare pre-post study results. The Mobile Application Rating Scale: user version questionnaire was also used to evaluate the app’s usability. Following the study, interviews were conducted with participants to discuss their experiences with the app. ResultsThe SELSA-S questionnaire results showed no change in the family subscale; however, the social loneliness subscale showed a difference between prestudy and poststudy. The Wilcoxon signed-rank test indicated that poststudy SELSA-S results were statistically significantly higher than prestudy results (z=−2.047; P=.04). Point-biserial correlation indicated that the SELSA-S rate of change was strongly related to usage of the TTS feature (r=0.708; P=.04) and CS feature (r=−0.917; P=.002), and moderately related to usage of the STT feature (r=0.428; P=.17). Lastly, we adopted grounded theory to analyze the interview data, and the following 5 categories emerged: app support, feature relevance, user interface design, overall feedback, and recommendations. ConclusionsThis study discusses the potential for improving the communication skills of adults with ASD through special features in mobile messaging apps. The developed app aims to support the inclusion and independent life of adults with ASD. The study results showed the importance of using TTS, STT, and CS features to enhance social and communication skills, as well as reduce social loneliness in adults with ASD

Face-Based Attention Recognition Model for Children with Autism Spectrum Disorder

Attention recognition plays a vital role in providing learning support for children with autism spectrum disorders (ASD). The unobtrusiveness of face-tracking techniques makes it possible to build automatic systems to detect and classify attentional behaviors. However, constructing such systems is a challenging task due to the complexity of attentional behavior in ASD. This paper proposes a face-based attention recognition model using two methods. The first is based on geometric feature transformation using a support vector machine (SVM) classifier, and the second is based on the transformation of time-domain spatial features to 2D spatial images using a convolutional neural network (CNN) approach. We conducted an experimental study on different attentional tasks for 46 children (ASD n=20, typically developing children n=26) and explored the limits of the face-based attention recognition model for participant and task differences. Our results show that the geometric feature transformation using an SVM classifier outperforms the CNN approach. Also, attention detection is more generalizable within typically developing children than within ASD groups and within low-attention tasks than within high-attention tasks. This paper highlights the basis for future face-based attentional recognition for real-time learning and clinical attention interventions.Other Information Published in: Journal of Healthcare Informatics Research License: https://creativecommons.org/licenses/by/4.0See article on publisher's website: http://dx.doi.org/10.1007/s41666-021-00101-y</p

Impact of mainstream classroom setting on attention of children with autism spectrum disorder: an eye-tracking study

It has long been reported that children with autism spectrum disorder (ASD) exhibit attention difficulties while learning. They tend to focus on irrelevant information and can easily be distracted. As a result, they are often confined to a one-to-one teaching environment, with fewer distractions and social interactions than would be present in a mainstream educational setting. In recent years, inclusive mainstream schools have been growing in popularity due to government policies on equality rights. Therefore, it is crucial to investigate attentional patterns of children with ASD in mainstream schools. This study aims to explore the attentional behaviors of children with ASD in a virtual reality simulated classroom. We analyzed four eye-gaze behaviors and performance scores of 45 children: children with ASD (ASD n = 20) and typically developing children (TD n = 25) when performing attention tasks. The gaze behaviors included time to first fixate (TTFF), first fixation duration (FFD), average fixation duration (AFD) and the sum of fixation count (SFC) on fourteen areas of interest (AOIs) in the classroom. Our results showed that children with ASD exhibit similar gaze behaviors to TD children, but with significantly lower performance scores and SFC on the target AOI. These findings showed that classroom settings can influence attentional patterns and the academic performance of children with ASD. Further studies are needed on different modalities for supporting the attention of children with ASD in a mainstream setting.Other Information Published in: Universal Access in the Information Society License: https://creativecommons.org/licenses/by/4.0See article on publisher's website: http://dx.doi.org/10.1007/s10209-020-00749-0</p

Augmented reality for learning of children and adolescents with autism spectrum disorder (ASD): A systematic review

This paper presents a systematic review of relevant primary studies on the use of augmented reality (AR) to improve various skills of children and adolescents diagnosed with autism spectrum disorder (ASD) from years 2005 to 2018 inclusive in eight bibliographic databases. This systematic review attempts to address eleven specific research questions related to the learing skills, participants, AR technology, research design, data collection methods, settings, evaluation parameters, intervention outcomes, generalization, and maintenance. The social communication skill was the highly targeted skill, and individuals with ASD were part of all the studies. Computer, smartphone, and smartglass are more frequently used technologies. The commonly used research design was pre-test and post-test. Almost all the studies used observation as a data collection method, and classroom environment or controlled research environment were used as a setting of evaluation. Most of the evaluation parameters were human-assisted. The results of the studies show that AR benefited children with ASD in learning skills. The generalization test was conducted in one study only, but the results were not reported. The results of maintenance tests conducted in five studies during a short-term period following the withdrawal of intervention were positive. Although the effect of using AR towards the learning of individuals was positive, given the wide variety of skills targeted in the studies, and the heterogeneity of the participants, a summative conclusion regarding the effectiveness of AR for teaching or learning of skills related to ASD based on the existing literature is not possible. The review also proposes the research taxonomy for ASD. Future research addressing the effectiveness of AR among more participants, different technologies supporting AR for the intervention, generalization, and maintenance of learning skills, and the evaluation in the inslusive classroom environment and other settings is warrante