While Artificial Intelligence in Education (AIED) research has at its core the desire to support student learning, experience from other AI domains suggest that such ethical intentions are not by themselves sufficient. There is also the need to consider explicitly issues such as fairness, accountability, transparency, bias, autonomy, agency, and inclusion. At a more general level, there is also a need to differentiate between doing ethical things and doing things ethically, to understand and to make pedagogical choices that are ethical, and to account for the ever-present possibility of unintended consequences. However, addressing these and related questions is far from trivial. As a first step towards addressing this critical gap, we invited 60 of the AIED community’s leading researchers to respond to a survey of questions about ethics and the application of AI in educational contexts. In this paper, we first introduce issues around the ethics of AI in education. Next, we summarise the contributions of the 17 respondents, and discuss the complex issues that they raised. Specific outcomes include the recognition that most AIED researchers are not trained to tackle the emerging ethical questions. A well-designed framework for engaging with ethics of AIED that combined a multidisciplinary approach and a set of robust guidelines seems vital in this context.

Taking a process-orientated, social constructivist lens, we examine the case of a digital game called Words Matter. The game was designed for children with dyslexia and was informed by principles from casual games and evidence-based practice from special education. Focusing on the game play of two groups of children, we employ a systematic thematic analytic approach on videos of children's verbal and non-verbal interaction triangulated with their game logs, concentrating on the nature of student-student as well as student-tutor social interactions. Our findings show that children spontaneously engage in 'game talk' regarding game performance, content, actions and experiences. While this game talk facilitates a strong sense of social engagement and playfulness, it also caters to a variety of new opportunities for learning by sparking tutor and student-initiated interventions. Alongside its social theoretical lens on digital games-based learning, the paper analyses game-based social interactions in tandem with game design decisions enabling additional implications to be drawn for practice and game design.

Information on the total dry-matter intake (TDMI), concentrates supplied (C), live weight (LW), week of lactation (WL), milk yield (MY) and composition, quality of forage fed to, and parities of, 385 cows from five different sources were assembled to develop appetite prediction equations. The equations were based on multiple regression and least squares constants and were calculated using the data from each source and the data pooled from all sources. The major factors affecting total dry-matter intake (TDMI) were C, LW, WL and MY and for the pooled data these factors explained 73-76% (iJ 2 ) of the total variation in TDMI. The predictive values of some of the equations were tested against independent sets of data and for groups of cows and individual cows, the equations predicted TDMI within +1-1 and 1-6 kg, respectively. The application of the equations in the field is discussed and some suggestions made for increasing the R 2 of future appetite equations.

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Authenticating the students' identity and authenticity of their work is increasingly important to reduce academic malpractices and for quality assurance purposes in Education. There is a growing body of research about technological innovations to combat cheating and plagiarism. However, the literature is very limited on the impact of e-authentication systems across distinctive end-users because it is not a widespread practice at the moment. A considerable gap is to understand whether the use of eauthentication systems would increase trust on e-assessment, and to extend, whether students' acceptance would vary across gender, age and previous experiences. This study aims to shed light on this area by examining the attitudes and experiences of 328 students who used an authentication system known as adaptive trust-based e-assessment system for learning (TeSLA). Evidence from mixed-method analysis suggests a broadly positive acceptance of these e-authentication technologies by distance education students. However, significant differences in the students' responses indicated, for instance, that men were less concerned about providing personal data than women; middle-aged participants were more aware of the nuances of cheating and plagiarism; while younger students were more likely to reject e-authentication, considerably due to data privacy and security and students with disabilities due to concerns about their special needs.