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This paper introduces the concept of digital structural violence and examines the negative role it could have in future learning systems. To address it, we propose a new interdisciplinary research agenda at the intersection of three current but disparate lines of work that: (1) Use the concept of epistemic privilege to theorise the inclusion of marginalised learners in the design of learning systems, and utilise participatory action research and emancipatory methodologies to pragmatically ensure this happens; (2) Support young learners and teachers to understand and build their own artificial intelligence algorithms; and (3) Develop sustainable interdisciplinary links with computer science to address digital structural violence at the algorithmic level and to make its societal implications and underlying processes more widely understood, especially by teachers. Taken together, these provide for a material form of resistance to digital structural violence and a theoretically and methodologically coherent future research agenda for building just learning systems.

Digital skills are an important aspect of ensuring that all young people are digitally included. Yet, there tends to be an assumption in popular discourse that young people can simply learn these skills by themselves. While experience of technologies forms an important part of the learning process, other resources (i.e., access to technology and support networks) plus clear motivations are required. Through in-depth interviews with 20 young people who are digitally excluded, this paper highlights the kinds of digital skills these young people find problematic, and the reasons why they find developing these skills so challenging. We demonstrate how poor access to technology, limited support networks and their current situation prevent these young people from gaining the experiences they need to support the development of their digital skills; and how lack of experience and inadequate skills limit the extent to which they perceive the internet to be valuable in their lives. These individual experiences, shaped very much by the wider social structure of which they are part, show how young people cannot simply be left to learn digital skills by themselves and that intervention is required to try to address some of the digital inequalities apparent in younger generations.

Background: Despite the growth in mobile technologies (mHealth) to support Community Health Worker (CHW) supervision, the nature of mHealth-facilitated supervision remains underexplored. One strategy to support supervision at scale could be artificial intelligence (AI) modalities, including machine learning. We developed an open access, machine learning web application (CHWsupervisor) to predictively code instant messages exchanged between CHWs based on supervisory interaction codes. We document the development and validation of the web app and report its predictive accuracy. Methods: CHWsupervisor was developed using 2187 instant messages exchanged between CHWs and their supervisors in Uganda. The app was then validated on 1242 instant messages from a separate digital CHW supervisory network in Kenya. All messages from the training and validation data sets were manually coded by two independent human coders. The predictive performance of CHWsupervisor was determined by comparing the primary supervisory codes assigned by the web app, against those assigned by the human coders and calculating observed percentage agreement and Cohen's kappa coefficients. Results: Human inter-coder reliability for the primary supervisory category of messages across the training and validation datasets was 'substantial' to 'almost perfect', as suggested by observed percentage agreements of 88–95% and Cohen's kappa values of 0.7–0.91. In comparison to the human coders, the predictive accuracy of the CHWsupervisor web app was 'moderate', suggested by observed percentage agreements of 73–78% and Cohen's kappa values of 0.51–0.56. Conclusions: Augmenting human coding is challenging because of the complexity of supervisory exchanges, which often require nuanced interpretation. A realistic understanding of the potential of machine learning approaches should be kept in mind by practitioners, as although they hold promise, supportive supervision still requires a level of human expertise. Scaling-up digital CHW supervision may therefore prove challenging. Trial registration: This was not a clinical trial and was therefore not registered as such. [ABSTRACT FROM AUTHOR]

This article focuses on strategies of international news organizations (INOs) in low- and middle-income countries (LMICs). In particular, I examine the potential of smartphone platforms in LMICs to reach and connect with new, younger audiences. Insights suggest that chat apps (a) tend to attract new users, often a younger audience, and (b) are accessible for low-income semiliterate or illiterate people, as they allow for dissemination of video clips and images. However, despite identifying an increasing number of pilot studies and initiatives by INOs to harness chat apps to engage with these audiences, the article uncovers, through a systematic approach, a substantial gap in research regarding the effectiveness of these strategies [ABSTRACT FROM AUTHOR]