Deliberate System-Side Errors as a Potential Pedagogic Strategy for Exploratory Virtual Learning Environments

This paper describes an exploratory study of system-side errors (i.e. expectation- or rule-violations) in a virtual environment (VE), and the subsequent reactions of young children with autism spectrum conditions (ASC). Analysis of existing video from 8 participants interacting with the ECHOES VE showed that they frequently detected and reacted to system-side errors, engaging in social and communicative behaviours targeted by ECHOES. Detecting errors requires children to compare the VE's state to their mental model of its behaviour, determining where the two are discrepant. This is equivalent to learners identifying mistakes in their own knowledge and then re-aligning with the system-as-expert. This paper explores the implications of these results, proposing a taxonomy of discrepant event types, and discussing their location with respect to the learner and/or system. In addition to considering these results' significance for this user group and context, it relates the research to existing work that uses erroneous examples. © 2013 Springer-Verlag Berlin Heidelberg

Short report: Evaluation of wider community support for a neurodiversity teaching programme designed using participatory methods

Children with diagnoses such as autism, attention-deficit/hyperactivity disorder (ADHD), dyslexia and so on often experience bullying at school. This group can be described as neurodivergent, meaning they think and process information differently from most people. Previous research suggests that increasing people's knowledge can be an effective way to reduce stigma and bullying. Therefore, we decided to create a primary school resource to teach about neurodiversity - the concept that all humans vary in how our brains work. Working with educators, our research team - which included neurodivergent people - developed plans for a teaching programme called Learning About Neurodiversity at School (LEANS). Next, we wanted to know whether these plans, developed by our small neurodiverse team, would be endorsed by the wider community. To find out, we conducted an online feedback survey about our plans for the resource. We analysed feedback from 111 people who participated. Most of them identified as neurodivergent (70%) and reported being familiar with neurodiversity (98%), meaning they could provide an informed opinion on our plans. Over 90% of people expressed support for the planned programme content described in the survey, and 73% of them approved our intended definition of the resource's core concept, neurodiversity. From these results, we concluded that there was a high level of support for the planned LEANS programme content across those from the wider community who completed the survey. Consequently, we continued developing the LEANS programme in line with the initial plans from our neurodiverse team. The completed resource is now available as a free download

Evaluating the impact of voice activity detection on speech emotion recognition for autistic children

Individuals with autism are known to face challenges with emotion regulation, and express their affective states in a variety of ways. With this in mind, an increasing amount of research on automatic affect recognition from speech and other modalities has recently been presented to assist and provide support, as well as to improve understanding of autistic individuals' behaviours. As well as the emotion expressed from the voice, for autistic children the dynamics of verbal speech can be inconsistent and vary greatly amongst individuals. The current contribution outlines a voice activity detection (VAD) system specifically adapted to autistic children's vocalisations. The presented VAD system is a recurrent neural network (RNN) with long short-term memory (LSTM) cells. It is trained on 130 acoustic Low-Level Descriptors (LLDs) extracted from more than 17 h of audio recordings, which were richly annotated by experts in terms of perceived emotion as well as occurrence and type of vocalisations. The data consist of 25 English-speaking autistic children undertaking a structured, partly robot-assisted emotion-training activity and was collected as part of the DE-ENIGMA project. The VAD system is further utilised as a preprocessing step for a continuous speech emotion recognition (SER) task aiming to minimise the effects of potential confounding information, such as noise, silence, or non-child vocalisation. Its impact on the SER performance is compared to the impact of other VAD systems, including a general VAD system trained from the same data set, an out-of-the-box Web Real-Time Communication (WebRTC) VAD system, as well as the expert annotations. Our experiments show that the child VAD system achieves a lower performance than our general VAD system, trained under identical conditions, as we obtain receiver operating characteristic area under the curve (ROC-AUC) metrics of 0.662 and 0.850, respectively. The SER results show varying performances across valence and arousal depending on the utilised VAD system with a maximum concordance correlation coefficient (CCC) of 0.263 and a minimum root mean square error (RMSE) of 0.107. Although the performance of the SER models is generally low, the child VAD system can lead to slightly improved results compared to other VAD systems and in particular the VAD-less baseline, supporting the hypothesised importance of child VAD systems in the discussed context

Blending human and artificial intelligence to support autistic children’s social communication skills

This paper examines the educational efficacy of a learning environment in which children diagnosed with Autism Spectrum Conditions (ASC) engage in social interactions with an artificially intelligent (AI) virtual agent and where a human practitioner acts in support of the interactions. A multi-site intervention study in schools across the UK was conducted with 29 children with ASC and learning difficulties, aged 4-14 years old. For reasons related to data completeness and amount of exposure to the AI environment, data for 15 children was included in the analysis. The analysis revealed a significant increase in the proportion of social responses made by ASC children to human practitioners. The number of initiations made to human practitioners and to the virtual agent by the ASC children also increased numerically over the course of the sessions. However, due to large individual differences within the ASC group, this did not reach significance. Although no evidence of transfer to the real-world post-test was shown, anecdotal evidence of classroom transfer was reported. The work presented in this paper offers an important contribution to the growing body of research in the context of AI technology design and use for autism intervention in real school contexts. Specifically, the work highlights key methodological challenges and opportunities in this area by leveraging interdisciplinary insights in a way that (i) bridges between educational interventions and intelligent technology design practices, (ii) considers the design of technology as well as the design of its use (context and procedures) on par with one another, and (iii) includes design contributions from different stakeholders, including children with and without ASC diagnosis, educational practitioners and researchers

Applications of Avatar Mediated Interaction to Teaching, Training, Job Skills and Wellness

The focus of this chapter is on the application of a framework for remotely delivering role-playing experiences that afford users the opportunity to practice real-world skills in a safe virtual setting. The framework, AMITIES, provides a single individual the capabilities to remotely orchestrate the performances of multiple virtual characters. We illustrate this by introducing avatar– enabled scenarios that range from teacher preparation to effectively dealing with complex interpersonal situations such as resistance to peer pressure and participation in job interviews (either as the interviewer or the interviewee)

Non-participatory user-centered design of accessible teacher-teleoperated robot and tablets for minimally verbal autistic children

Autistic children with limited language ability are an important but overlooked community. We develop a teacher-teleoperated robot and tablet system, as well as learning activities, to help teach facial emotions to minimally verbal autistic children. We then conduct user studies with 31 UK and Serbia minimally verbal autistic children to evaluate the system's accessibility. Results showed minimally verbal autistic children could use the tablet interface to control or respond to a humanoid robot and could understand the face learning activities. We found that a flexible and powerful wizard-of-oz tablet interface respected the needs of the children and their teachers. Our work suggests that a non-participatory, user-centered design process can create a robot and tablet system that is accessible to many autistic children

Predictable robots for autistic children: variance in robot behaviour, idiosyncrasies in autistic children's characteristics, and child–robot engagement

Predictability is important to autistic individuals, and robots have been suggested to meet this need as they can be programmed to be predictable, as well as elicit social interaction. The effectiveness of robot-assisted interventions designed for social skill learning presumably depends on the interplay between robot predictability, engagement in learning, and the individual differences between different autistic children. To better understand this interplay, we report on a study where 24 autistic children participated in a robot-assisted intervention. We manipulated the variance in the robot’s behaviour as a way to vary predictability, and measured the children’s behavioural engagement, visual attention, as well as their individual factors. We found that the children will continue engaging in the activity behaviourally, but may start to pay less visual attention over time to activity-relevant locations when the robot is less predictable. Instead, they increasingly start to look away from the activity. Ultimately, this could negatively influence learning, in particular for tasks with a visual component. Furthermore, severity of autistic features and expressive language ability had a significant impact on behavioural engagement. We consider our results as preliminary evidence that robot predictability is an important factor for keeping children in a state where learning can occur