Retrieval-Augmented Generation (RAG) systems are increasingly deployed in web-based educational environments, yet transparency can be seen as a primarily ethical, and, too often, optional, concern, rather than foundational. This paper presents design patterns for building transparent RAG systems, derived from developing and deploying SAGE-RAI, an advanced multi-purpose RAG system, in an educational context. Through systematic evaluation combining quantitative rating data (n=26, mean rating=4.62/5) and qualitative interviews (n=4), we demonstrate that transparency serves dual pedagogical and ethical functions. Our empirical findings reveal high user satisfaction (92.3% rating 4-5 stars) while identifying critical tensions between AI assistance and learning independence. Our findings suggest that as RAG systems increasingly mediate access to web-based knowledge, transparency must evolve from an optional feature to an architectural requirement.

Understanding the future consequences of artificial intelligence requires a holistic consideration of its cultural dimensions, on par with its technological intricacies and potential applications. Individuals and institutions working closely with AI, and with considerable resources, have significant influence on how impact is considered, particularly with regard to how much attention is paid to epistemic concerns (including issues of bias in datasets or potential misinterpretations of data, for example) versus normative concerns (such as societal and ecological effects of AI in the medium- and long-term). In this paper we review qualitative studies conducted with AI researchers and developers to understand how they position themselves relative to each of these two dimensions of impact, and how geographies and conditions of work influence their positions. Our findings underscore the need to gather more perspectives from low- and middle-income countries, whose notions of impact extend beyond the immediate technical concerns or impacts in the short- to medium-term. Rather, they encapsulate a broader spectrum of impact considerations, including the deleterious effects perpetrated by global corporate entities, the unwarranted influence of wealthy nations, the encroachment of philanthrocapitalism, and the adverse consequences of excluding communities affected by these phenomena from active participation in discussions surrounding impact.

Social Networking Sites are home to different forms of hate, including "Misogynoir", which specifically targets Black women through a combination of racism and sexism. Detecting misogynoir presents challenges due to its subjective nature and the varied interpretations of hate speech. Using annotator justifications from four distinct demographic groups; including Black women, Black men, White women and White men, we seek to gain a deeper understanding of the factors that influence annotators' reasoning process and labelling decisions for potential cases of Misogynoir and Allyship. Given the unique experiences of Black women who face both racism and sexism, the study sought to understand how their intersectional identities shape their perspectives compared to other groups. The research employed a qualitative analysis of responses from participants to identify key themes and patterns. Three significant themes emerged from our in-depth qualitative analysis of these annotator justifications: prior knowledge and experience, the language of the social media post, and its context. Our results revealed that annotators historically at risk of abuse demonstrated a nuanced understanding of how their intersecting identities inform their interpretations and judgement of tweets, drawing on their personal encounters with misogyny and racism compared to their non-target counterparts of this type of hate. This study underscores the significance of diverse annotator perspectives and content comprehension in understanding and addressing hate speech, particularly when it intersects with multiple forms of discrimination. Our study contributes to the methodological advancements in social network analysis and mining, highlighting the importance of considering annotator characteristics in the development of tools and approaches for detecting and addressing intersectional hate.

An increasingly connected society demands people who can design, set up, monitor and maintain networks of computers and devices. Traditional classroom instruction cannot keep pace with demand, and networking hardware costs can be too high for widespread classroom use. This paper presents the Open Networking Lab, a new UK initiative for supporting hands-on vocational learning in computer networking. The Open Networking Lab will facilitate the development of introductory practical networking skills without using hardware, through the provision of a web-based network simulation package integrated into learning resources and activities. These learning resources will be evaluated by students and lecturers from a cluster of Further Education colleges in the UK and will subsequently be made available to learners worldwide via free and open courseware.

Many geographic terms, such as “river” and “lake”, are vague, with no clear boundaries of application. In particular, the spatial extent of such features is often vaguely carved out of a continuously varying observable domain. We present a means of defining vague terms using standpoint semantics, a refinement of the philosophical idea of supervaluation semantics. Such definitions can be grounded in actual data by geometric analysis and segmentation of the data set. The issues raised by this process with regard to the nature of boundaries and domains of logical quantification are discussed. We describe a prototype implementation of a system capable of segmenting attributed polygon data into geographically significant regions and evaluating queries involving vague geographic feature terms.