Location-based augmented reality games are becoming increasingly popular, which causes a variety of tensions among players as well as between players and non-players. Subsequently, numerous ethical issues and challenges in designing and playing these games emerged. We conducted an online survey and analyzed 2,023 responses to better understand users’ participation in various game actions in location-based augmented reality games, and their perspectives on how ethical the actions are and for what reasons. We provide descriptive statistics showing people’s participation in these actions and analyze users’ expressed reasons as to why they think these actions are ethical or not. We use the theoretical lens of three ethical traditions–consequentialism, deontology, and virtue ethics–in our analysis, showing the dominance of consequentialism and deontology in people’s ethical judgment related to playing the game, and discuss implications and future consideration for game design.

Philosophers have recently put forward the possibility of achieving moral enhancement through artificial intelligence (e.g., Giubilini and Savulescu's version [32]), proposing various forms of "artificial moral advisor" (AMA) to help people make moral decisions without the drawbacks of human cognitive limitations. In this paper, we provide a new perspective on the AMA, drawing on empirical evidence from moral psychology to point out several challenges to these proposals that have been largely neglected by AI ethicists. In particular, we suggest that the AMA at its current conception is fundamentally misaligned with human moral psychology - it incorrectly assumes a static moral values framework underpinning the AMA's attunement to individual users, and people's reactions and subsequent (in)actions in response to the AMA suggestions will likely diverge substantially from expectations. As such, we note the necessity for a coherent understanding of human moral psychology in the future development of AMAs.

As computer supported learning fragments from monolithic platforms into a wide range of disparate learning experiences it is important to develop systems that provide generic services appropriate for a wide array of learning scenarios. The authors have previously developed a platform for deploying metacognitive scaffolding coupled to a web-based adaptive eLearning system, supported by a cognitive model of reading for learning tasks, developed with instructional experts. We have began investigating more generic cognitive task models in order to support a wider variety of learning experiences. Here we discuss models of SRL and their suitability.

Online training is an increasingly important facet of the skills enhancing portfolio of any organization and has an especial place within life-long learning, career development and other self-regulated adult learning. We here present an overview of work towards developing online training, which is augmented by contextual cues, training which is personalized and adapted, in real-time, towards the learning objectives of the user, whilst supporting a wide range of meta-cognitive activities. This work brings technologies developed for the fantastical scenarios of serious gaming into the 'real world' - we also begin to address some of the underlying challenges in this transition.

The mechanisms for personalisation used in web applications are currently the subject of much debate amongst researchers from many diverse subject areas. One of the most contemporary ideas for user modelling in web applications is that of cognitive styles, where a user's psychological preferences are assessed stored in a database and then used to provide personalised content and/or links. We describe user trials of a case study that utilises visual-verbal preferences in an adaptive web-based educational system (AWBES). Students in this trial were assessed by the Felder-Solomon Inventory of Learning Styles (ILS) instrument, and their preferences were used as a means of content personalisation.Contrary to previous findings by other researchers, we found no significant differences in performance between matched and mismatched students. Conclusions are drawn about the value and validity of using cognitive styles as a way of modelling user preferences in educational web applications.