Ethics in AIED: Who cares?

The field of AIED raises far-reaching ethical questions with important implications for students and educators. However, most AIED research, development and deployment has taken place in what is essentially a moral vacuum (for example, what happens if a child is subjected to a biased set of algorithms that impact negatively and incorrectly on their school progress?). Around the world, virtually no research has been undertaken, no guidelines have been provided, no policies have been developed, and no regulations have been enacted to address the specific ethical issues raised by the use of Artificial Intelligence in Education. This workshop, ETHICS in AIED: Who Cares?, is proposed as a first step towards addressing this critical problem for the field. It will be an opportunity for researchers who are exploring ethical issues critical for AIED to share their research, to identify the key ethical issues, and to map out how to address the multiple challenges, towards establishing a basis for meaningful ethical reflection necessary for innovation in the field of AIED. The workshop will be in three parts. It will begin with ETHICS in AIED: What’s the problem?, a round-table discussion introduced and led by Professor Beverly Woolf, one of the world’s most accomplished AIED researchers. This will be followed by Mapping the Landscape, in which up to six AIED conference participants will each give a five-minute ‘lightning’ presentation on ethics in AIED research. The workshop will conclude with Addressing the Challenges, a round-table discussion session in which we will agree on a core list of ethical questions/areas of necessary research for the field of AIED, and will set out to identify next steps

Artificial Intelligence And Big Data Technologies To Close The Achievement Gap.

We observe achievement gaps even in rich western countries, such as the UK, which in principle have the resources as well as the social and technical infrastructure to provide a better deal for all learners. The reasons for such gaps are complex and include the social and material poverty of some learners with their resulting other deficits, as well as failure by government to allocate sufficient resources to remedy the situation. On the supply side of the equation, a single teacher or university lecturer, even helped by a classroom assistant or tutorial assistant, cannot give each learner the kind of one-to-one attention that would really help to boost both their motivation and their attainment in ways that might mitigate the achievement gap. In this chapter Benedict du Boulay, Alexandra Poulovassilis, Wayne Holmes, and Manolis Mavrikis argue that we now have the technologies to assist both educators and learners, most commonly in science, technology, engineering and mathematics subjects (STEM), at least some of the time. We present case studies from the fields of Artificial Intelligence in Education (AIED) and Big Data. We look at how they can be used to provide personalised support for students and demonstrate that they are not designed to replace the teacher. In addition, we also describe tools for teachers to increase their awareness and, ultimately, free up time for them to provide nuanced, individualised support even in large cohorts

Coherence compilation: applying AIED techniques to the reuse of educational resources

The HomeWork project is building an exemplar system to provide individualised experiences for individual and groups of children aged 6-7 years, their parents, teachers and classmates at school. It employs an existing set of broadcast video media and associated resources that tackle both numeracy and literacy at Key Stage 1. The system employs a learner model and a pedagogical model to identify what resource is best used with an individual child or group of children collaboratively at a particular learning point and at a particular location. The Coherence Compiler is that component of the system which is designed to impose an overall narrative coherence on the materials that any particular child is exposed to. This paper presents a high level vision of the design of the Coherence Compiler and sets its design within the overall framework of the HomeWork project and its learner and pedagogical models

Beetle II: an adaptable tutorial dialogue system

We present BEETLE II, a tutorial dialogue system which accepts unrestricted language input and supports experimentation with different dialogue strategies. Our first system evaluation compared two dialogue policies. The resulting corpus was used to study the impact of different tutoring and error recovery strategies on user satisfaction and student interaction style. It can also be used in the future to study a wide range of research issues in dialogue systems.

Exploring User Satisfaction in a Tutorial Dialogue System

Abstract User satisfaction is a common evaluation metric in task-oriented dialogue systems, whereas tutorial dialogue systems are often evaluated in terms of student learning gain. However, user satisfaction is also important for such systems, since it may predict technology acceptance. We present a detailed satisfaction questionnaire used in evaluating the BEETLE II system (REVU-NL), and explore the underlying components of user satisfaction using factor analysis. We demonstrate interesting patterns of interaction between interpretation quality, satisfaction and the dialogue policy, highlighting the importance of more finegrained evaluation of user satisfaction

Educational Technology: The influence of theory

In this paper we explore the role of theories in current practice in educational technology. We review a range of writings from the past 30 years on the nature of learning technology research. We discuss influences on learning technologies from the related fields of Artificial Intelligence in Education (AIED) and Human-Computer Interaction (HCI). We identify two groups of theories which have been used. The first group are related to principled decisions about the design of learning materials. The second group influence the ways in which we frame our research on learning. Research in learning technologies in the future will need to draw on both groups of theories. In this paper, we draw on our own experiences as educational technologists and the purpose of the paper is to encourage other educational technologists to join with us in reflecting on their own use of theories

Language Learning and Interactive TV

The integration of engaging TV style content with the individualization and ‘intelligent’ content management offered by techniques from AI has the potential to provide learning environments that are both highly motivating and educationally sound. This paper describes why the area of language learning would be a particularly appropriate domain for interactive educational television to focus on. It also indicates some of the criteria to be fulfilled in order to provide optimal language learning conditions and how these might be satisfied using TV/Film content and techniques from AIED