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The use of Artificial Intelligence in supporting social skills development is an emerging area of interest in education. This paper presents work which evaluated the impact of a situated experience coupled with open learner modelling on 16–18 years old learners’ verbal and non-verbal behaviours during job interviews with AI recruiters. The results revealed significantly positive trends on certain aspects of learners’ verbal and non-verbal performance and on their self-efficacy.
The TARDIS1 project aims to build a scenario-based serious-game simulation platform for young people at risk of exclusion to explore, practice and improve their social skills. This paper presents a model for socio-emotionally realistic virtual agents in the context of job interview simulations.
The number of young people not in employment, education or training is increasing across Europe. These youngsters of- ten lack self-confidence and the essential social skills needed to seek and secure employment. The TARDIS project aims to build a scenario-based serious-game simulation platform for young people at risk of exclusion to improve their so- cial skills. This paper intends to propose a model for a socio-emotionally realistic virtual agent in the context of job interview simulations. Our model of affect is composed of emotions, moods, social attitudes and personality that intends to create a realistic virtual recruiter.
This paper presents an approach that makes use of a virtual character and social signal processing techniques to create an immersive job interview simulation environment. In this environment, the virtual character plays the role of a recruiter which reacts and adapts to the user's behavior thanks to a component for the automatic recognition of social cues (conscious or unconscious behavioral patterns). The social cues pertinent to job interviews have been identified using a knowledge elicitation study with real job seekers. Finally, we present two user studies to investigate the feasibility of the proposed approach as well as the impact of such a system on users.
Previous studies have shown that the success of interper-sonal interaction depends not only on the contents we communicate ex-plicitly, but also on the social signals that are conveyed implicitly. In this paper, we present NovA (NOnVerbal behavior Analyzer), a system that analyzes and facilitates the interpretation of social signals conveyed by gestures, facial expressions and others automatically as a basis for computer-enhanced social coaching. NovA records data of human inter-actions, automatically detects relevant behavioral cues as a measurement for the quality of an interaction and creates descriptive statistics for the recorded data. This enables us to give a user online generated feedback on strengths and weaknesses concerning his social behavior, as well as elaborate tools for offline analysis and annotation.
The use of virtual agents in social coaching has increased rapidly in the last decade. In social coaching, the virtual agent should be able to express different social attitudes to train the user in different situations than can occur in real life. In this paper, we propose a model of social attitudes that enables a virtual agent to reason on the appropriate social attitude to express during the interaction with a user given the course of the interaction, but also the emotions, mood and personality of the agent. Moreover, the model enables the virtual agent to display its social attitude through its non-verbal behaviour. The proposed model has been developed in the context of job interview simulation. The methodology used to develop such a model combined a theoretical and an empirical approach. Indeed, the model is based both on the literature in Human and Social Sciences on social attitudes but also on the analysis of an audiovisual corpus of job interviews and on post-hoc interviews with the recruiters on their expressed attitudes during the job interview.
This demo presents an approach to recognising and interpreting social cues-based interactions in computer-enhanced job interview simulations. We show what social cues and complex mental states of the user are relevant in this interaction context, how they can be interpreted using static Bayesian Networks, and how they can be recognised automatically using state-of-the-art sensor technology in real-time.
We define job interviews as a domain of interaction that can be modelled automatically in a serious game for job interview skills train-ing. We present four types of studies: (1) field-based human-to-human job interviews, (2) field-based computer-mediated human-to-human in-terviews, (3) lab-based wizard of oz studies, (4) field-based human-to-agent studies. Together, these highlight pertinent questions for the user modelling field as it expands its scope to applications for social inclu-sion. 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 mod-elling technologies. This calls for the need to re-examine both the ap-proach to the implementation of the models and/or of their usage for the target contexts.
Evidence-based practice (EBP) is of critical importance in education where emphasis is placed on the need to equip educators with an ability to independently generate and reflect on evidence of their practices in situ – a process also known as praxis. This paper examines existing research related to teachers’ metacognitive skills and, using two exemplar projects, it discusses the utility and relevance of AI methods of knowledge representation and knowledge elicitation as methodologies for supporting EBP. Research related to technology-enhanced communities of practice as a means for teachers to share and compare their knowledge with others is also examined. Suggestions for the key considerations in supporting teachers’ metacognition in praxis are made based on the review of literature and discussion of the specific projects, with the aim to highlight potential future research directions for AIEd. A proposal is made that a crucial part of AIEd’s future resides in its curating the role of AI as a methodology for supporting teacher training and continuous professional development, especially as relates to their developing metacognitive skills in relation to their practices.
Job interviews come with a number of challenges, especially for young people who are out of employment, education, or training (NEETs). This paper presents an approach to a job training simulation environment that employs two virtual characters and social cue recognition techniques to create an immersive interactive job interview. The two virtual characters are created with different social behavior profiles, understanding and demanding, which consequently influences the level of difficulty of the simulation as well as the impact on the user. Finally, we present a user study which investigates the feasibility of the proposed approach by measuring the effect the different virtual characters have on the users.