Adolescents’ self-regulation during job interviews through an AI coaching environment

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

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

Accountability in human and artificial decision-making as the basis for diversity and educational inclusion

Accountability is an important dimension of decision-making in Human and Artificial Intelligence (AI). We argue that it is of fundamental importance to inclusion, diversity and fairness of both the AI-based and human-controlled interactions and any human-facing interventions aiming to change human development, behaviour and learning. Less well debated, however, is the nature and the role of biases that emerge from theoretical or empirical models that underpin AI algorithms and the interventions driven by such algorithms. While, the biases emerging from the theoretical and empirical models also affect human-controlled educational systems and interventions (e.g. hindsight and unconscious biases), the key mitigating difference between AI and human decision-making is that human decisions involve individual flexibility, context-relevant judgements, empathy, as well as complex moral judgements, missing from AI. In this chapter, we argue that our fascination with AI, which pre-dates the current craze by centuries, resides in its ability to act as a ‘mirror’ reflecting our current understandings of human intelligence. Such understandings also inevitably encapsulate the biases emerging from our intellectual and empirical limitations. We make a case for the need for diversity to mitigate against biases becoming inbuilt into systems (in both Education and AI) and, with reference to specific examples of AI approaches and applications, we outline one compelling future for inclusive and accountable AI and Educational research and practice