Planning-based Social Partners for Children with Autism

This paper describes the design and implementation of a planning-based socially intelligent agent built to help young children with Autism Spectrum Conditions acquire social communication skills. We explain how planning technology allowed us to satisfy agent’s design requirements that we identified through our consultations with children and carers and through a review of best practices for autism intervention.We discuss the design principles implemented, the engineering challenges faced and the lessons learned from building our pedagogical agent. We conclude by presenting substantial experimental results concerning the agent’s efficacy

Influence of Situational Context on Language Production: Modelling Teachers’ Corrective Responses.

Institute for Communicating and Collaborative SystemsNatural language is characterised by enormous linguistic variation (e.g., Fetzer (2003)). Such variation is not random, but is determined by a number of contextual factors. These factors encapsulate the socio-cultural conventions of a speech community and dictate the socially acceptable, i.e. polite, use of language. Producing polite language may not always be a trivial task. The ability to assess a situation with respect to a hearer’s social, cultural or emotional needs constitutes a crucial facet of a speaker’s social and linguistic competence. It is surprising then that it is also a facet which, to date, has received very little attention from researchers in the natural language generation community. Linguistic variation occurs in all linguistic sub-domains including the language of education (Person et al., 1995). Thanks to being relatively more constrained (and hence more predictable with respect to its intentional aspects than normal conversations), teachers’ language is taken in this thesis as a starting point for building a formal, computational model of language generation based on the theory of linguistic politeness. To date, the most formalised theory of linguistic politeness is that by Brown and Levinson (1987), in which face constitutes the central notion. With its two dimensions of Autonomy and Approval, face can be used to characterise different linguistic choices available to speakers in a systematic way. In this thesis, the basic idea of face is applied in the analysis of teachers’ corrective responses produced in real one-to-one and classroom dialogues, and it is redefined to suit the educational context. A computational model of selecting corrective responses is developed which demonstrates how the two dimensions of face can be derived from a situation and how they can be used to classify the many linguistic choices available to teachers. The model is fully implemented using a combination of naive Bayesian Networks and Case-Based Reasoning techniques. The evaluation of the model confirms the validity of the model, by demonstrating that politeness-based natural language generation in the context of teachers’ corrective responses can be used to model linguistic variation and that the resulting language is not singnificantly different from that produced by a human in identical situations

AI in Education needs interpretable machine learning: Lessons from Open Learner Modelling

Interpretability of the underlying AI representations is a key raison d'\^{e}tre for Open Learner Modelling (OLM) -- a branch of Intelligent Tutoring Systems (ITS) research. OLMs provide tools for 'opening' up the AI models of learners' cognition and emotions for the purpose of supporting human learning and teaching. Over thirty years of research in ITS (also known as AI in Education) produced important work, which informs about how AI can be used in Education to best effects and, through the OLM research, what are the necessary considerations to make it interpretable and explainable for the benefit of learning. We argue that this work can provide a valuable starting point for a framework of interpretable AI, and as such is of relevance to the application of both knowledge-based and machine learning systems in other high-stakes contexts, beyond education.Comment: presented at 2018 ICML Workshop on Human Interpretability in Machine Learning (WHI 2018), Stockholm, Swede

Technology to provide educational practitioners with the expertise they need

The book brings together researchers, technologists and educators to explore and show how technology can be designed and used for learning and teaching to best effect

Knowledge Elicitation Methods for Affect Modelling in Education

Research on the relationship between affect and cognition in Artificial Intelligence in Education (AIEd) brings an important dimension to our understanding of how learning occurs and how it can be facilitated. Emotions are crucial to learning, but their nature, the conditions under which they occur, and their exact impact on learning for different learners in diverse contexts still needs to be mapped out. The study of affect during learning can be challenging, because emotions are subjective, fleeting phenomena that are often difficult for learners to report accurately and for observers to perceive reliably. Context forms an integral part of learners’ affect and the study thereof. This review provides a synthesis of the current knowledge elicitation methods that are used to aid the study of learners’ affect and to inform the design of intelligent technologies for learning. Advantages and disadvantages of the specific methods are discussed along with their respective potential for enhancing research in this area, and issues related to the interpretation of data that emerges as the result of their use. References to related research are also provided together with illustrative examples of where the individual methods have been used in the past. Therefore, this review is intended as a resource for methodological decision making for those who want to study emotions and their antecedents in AIEd contexts, i.e. where the aim is to inform the design and implementation of an intelligent learning environment or to evaluate its use and educational efficacy

Building Autonomous Social Partners for Autistic Children

We present the design and implementation of an autonomous virtual agent that acts as a credible social partner for children with Autism Spectrum Conditions and supports them in acquiring social communication skills. The agent’s design is based on principles of best autism practice and input from users. Initial experimental results on the efficacy of the agent show encouraging tendencies for a number of children

Who's afraid of job interviews? Definitely a question for user modelling

We define job interviews as a domain of interaction that can be modelled automatically in a serious game for job interview skills training. We present four types of studies: (1) field-based human-to-human job interviews, (2) field-based computer-mediated human-to-human interviews, (3) lab-based wizard of oz studies, (4) field-based human-to agent studies. Together, these highlight pertinent questions for the user modelling eld as it expands its scope to applications for social inclusion. The results of the studies show that the interviewees suppress their emotional behaviours and although our system recognises automatically a subset of those behaviours, the modelling of complex mental states in real-world contexts poses a challenge for the state-of-the-art user modelling technologies. This calls for the need to re-examine both the approach to the implementation of the models and/or of their usage for the target contexts

Social communication between virtual characters and children with autism

Children with ASD have difficulty with social communication, particularly joint attention. Interaction in a virtual environment (VE) may be a means for both understanding these difficulties and addressing them. It is first necessary to discover how this population interacts with virtual characters, and whether they can follow joint attention cues in a VE. This paper describes a study in which 32 children with ASD used the ECHOES VE to assist a virtual character in selecting objects by following the character’s gaze and/or pointing. Both accuracy and reaction time data suggest that children were able to successfully complete the task, and qualitative data further suggests that most children perceived the character as an intentional being with relevant, mutually directed behaviour

On the sui generis value capture of new digital technologies: The case of AI

Much of the academic interest surrounding the emergence of new digital technologies has focused on forwarding the engineering literature, concentrating on the potential opportunities (economic, innovation, etc.) and harms (ethics, climate, etc.), with less focus on the foundational and theoretical shifts brought about by these technologies (e.g., what are “digital things”? What is the ontological nature and state of phenomena produced by and expressed in terms of digital products? Are there distinctions between the traditional conceptions of digital and non-digital technologies?. We investigate the question of what value is being expressed by an algorithm, which we conceptualize in terms of a digital asset, defining a digital asset as a valued digital thing that is derived from a particular digital technology (in this case, an algorithmic system). Our main takeaway is to invite the reader to consider artificial intelligence as a representation of the capture of value sui generis and that this may be a step change in the capture of value vis à vis the emergence of digital technologies

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