A comparison of humanoid and non-humanoid robots in supporting the learning of pupils with severe intellectual disabilities

Previous research has shown that the humanoid NAO robot can enhance learning as well as improve communication in children with intellectual disabilities. However, most special needs schools cannot afford the humanoid NAO robot due to high costs. Could a cheaper nonhumanoid Lego Mindstorm robot be an alternative way of achieving the same learning objectives as the humanoid NAO robot? A single case study experimental ABAB design was used consisting of 16 sessions over 5 weeks: eight with the humanoid and eight with the non-humanoid robot. All sessions were video recorded and analysed for percentage engagement and percentage errors made by each of four students. For each student individually, these outcome measures were then compared between the two conditions. The teachers were interviewed at the end of the study. Three out of four students were significantly more engaged with the non-humanoid robot than the humanoid robot, whilst one student was found to be equally engaged with both robots. There was no significant difference between the two robots in terms of percentage errors for all four participants who managed to complete the study

Enhancing biofeedback-driven self-guided virtual reality exposure therapy through arousal detection from multimodal data using machine learning

Abstract Virtual reality exposure therapy (VRET) is a novel intervention technique that allows individuals to experience anxiety-evoking stimuli in a safe environment, recognise specific triggers and gradually increase their exposure to perceived threats. Public-speaking anxiety (PSA) is a prevalent form of social anxiety, characterised by stressful arousal and anxiety generated when presenting to an audience. In self-guided VRET, participants can gradually increase their tolerance to exposure and reduce anxiety-induced arousal and PSA over time. However, creating such a VR environment and determining physiological indices of anxiety-induced arousal or distress is an open challenge. Environment modelling, character creation and animation, psychological state determination and the use of machine learning (ML) models for anxiety or stress detection are equally important, and multi-disciplinary expertise is required. In this work, we have explored a series of ML models with publicly available data sets (using electroencephalogram and heart rate variability) to predict arousal states. If we can detect anxiety-induced arousal, we can trigger calming activities to allow individuals to cope with and overcome distress. Here, we discuss the means of effective selection of ML models and parameters in arousal detection. We propose a pipeline to overcome the model selection problem with different parameter settings in the context of virtual reality exposure therapy. This pipeline can be extended to other domains of interest where arousal detection is crucial. Finally, we have implemented a biofeedback framework for VRET where we successfully provided feedback as a form of heart rate and brain laterality index from our acquired multimodal data for psychological intervention to overcome anxiety

Towards Explainable and Privacy-Preserving Artificial Intelligence for Personalisation in Autism Spectrum Disorder

Autism Spectrum Disorder (ASD) is a growing concern worldwide. To date there are no drugs that can treat ASD, hence the treatments that can be administered are mainly supportive in nature and aim to reduce, as much as possible, the symptoms induced by the disorder. However, diagnosis and related treatments in terms of improving communication, social and behavioural skills are very challenging due to the heterogeneity of the disorder and are amongst the largest barriers in supporting people with ASD. Thanks to the recent development in artificial intelligence (AI) and machine learning (ML) techniques, ASD can now be aimed to be detected at an early age. Also, these novel techniques can facilitate administering personalised treatments including cognitive-behavioural therapies and educational interventions. These systems aim to improve the personalised experience for the people with ASD. Acknowledging the existing challenges, this paper summarises the multitudes of ASD, the advancement of AI and ML-based methods in the detection and support of people with ASD, the progress of explainable AI and federated learning to deliver explainable and privacy-preserving systems targeting ASD. Towards the end, some open challenges are identified and listed

Examining the potential impact of digital game making in curricula based teaching: initial observations

Digital game making is becoming an increasingly common means of learning in schools due to the appeal of delivering curriculum-based learning objectives while tapping into the popularity of videogames. Indeed, research suggests that digital game making may improve cognitive and behavioral skills in learners and this may have significant impact on learners with special education needs and disabilities (SEND). However, past work in digital game making has limited involvement with learners with SEND, focuses on short-term evaluations and is utilised during extra-curricular sessions with few studies using an action-based field research approach. Furthermore, there is little quantitative data from defined methodologies that demonstrate the impact of digital game-making on learning. This paper presents results from two field trials examining the use of digital game making in two schools (one mixed ability primary and one special school) to deliver national curriculum-based content over 8-weeks. Results from a feasibility trial informed a pedagogical design and identified evaluation metrics for a subsequent longer trial. Evaluation metrics included learner engagement and collaboration with peers as suitable indicators of inclusive learning. Impact on these metrics was measured using an in-class observation tool that sampled learner behavior yielding quantitative data and follow up interviews with teachers yielding qualitative data. Results suggest that digital game making is at least as effective in encouraging engagement and collaboration in learners when compared to traditional methods, with it being more engaging for learners with special needs. Contributions from this paper provide quantifiable evidence for the perceived benefits of using digital game making and a methodology for evaluating engagement and collaboration through classroom observation. Recommendations for further work and refinements of the pedagogical implementation that builds on these findings are presented

The Cambridge History of Welsh Literature

This is the first comprehensive, single-volume history of the literature of Wales. The volume contains chapters covering the whole range of Welsh literature, from post-Roman Britain to post-devolution Wales, with many of the later chapters providing holistic accounts of literature in Welsh and literature in English within a single genre or a single period of literary production