Infectious diseases, as COVID-19 is proving, pose a global health threat in an interconnected world. In the last 20 years, resistant infectious diseases such as severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), H1N1 influenza (swine flu), Ebola virus, Zika virus, and now COVID-19 have been impacting global health defences, and aggressively flourishing with the rise of global travel, urbanization, climate change, and ecological degradation. In parallel, this extraordinary episode in global human health highlights the potential for artificial intelligence (AI)-enabled disease surveillance to collect and analyse vast amounts of unstructured and real-time data to inform epidemiological and public health emergency responses. The uses of AI in these dynamic environments are increasingly complex, challenging the potential for human autonomous decisions. In this context, our study of qualitative perspectives will consider a responsible AI framework to explore its potential application to disease surveillance in a global health context. Thus far, there is a gap in the literature in considering these multiple and interconnected levels of disease surveillance and emergency health management through the lens of a responsible AI framework.

Engaging citizens with digital technology to co-create data, information and knowledge has widely become an important strategy for informing the policy response to COVID-19 and the ‘infodemic’ of misinformation in cyberspace. This move towards digital citizen participation aligns well with the United Nations’ agenda to encourage the use of digital tools to enable data-driven, direct democracy. From data capture to information generation, and knowledge co-creation, every stage of the data lifecycle bears important considerations to inform policy and practice. Drawing on evidence of participatory policy and practice during COVID-19, we outline a framework for citizen ‘e-participation’ in knowledge co-creation across every stage of the policy cycle. We explore how coupling the generation of information with that of social capital can provide opportunities to collectively build trust in institutions, accelerate recovery and facilitate the ‘e-society’. We outline the key aspects of realising this vision of data-driven direct democracy by discussing several examples. Sustaining participatory knowledge co-creation beyond COVID-19 requires that local organisations and institutions (e.g. academia, health and welfare, government, business) incorporate adaptive learning mechanisms into their operational and governance structures, their integrated service models, as well as employing emerging social innovations.

The current global pandemic crisis has unquestionably disrupted the higher education sector, forcing educational institutions to rapidly embrace technology-enhanced learning. However, the COVID-19 containment measures that forced people to work or stay at home, have determined a significant increase in the Internet traffic that puts tremendous pressure on the underlying network infrastructure. This affects negatively content delivery and consequently user perceived quality, especially for video-based services. Focusing on this problem, this paper proposes a machine learning-based resource allocation solution that improves the quality of video services for increased number of viewers. The solution is deployed and tested in an educational context, demonstrating its benefit in terms of major quality of service parameters for various video content, in comparison with existing state of the art. Moreover, a discussion on how the technology is helping to mitigate the effects of massively increasing Internet traffic on the video quality in an educational context is also presented.

The recent global pandemic has transformed the way education is delivered, increasing the importance of video-based online learning. However, this puts a significant pressure on the underlying communication networks and the limited available bandwidth needs to be intelligently allocated to support a much higher transmission load, including video-based services. In this context, this paper proposes a Machine Learning (ML)-based solution that dynamically prioritizes content viewers with heterogeneous video services to increase their Quality of Service (QoS) and perceived Quality of Experience (QoE). The proposed approach makes use of the novel Prioritized Multi-Agent Reinforcement Learning solution (PriMARL) to decide the prioritization order of the video-based services based on networking conditions. However, the performance in terms of QoS and QoE provisioning to learners with different profiles and networking conditions depends on the type of scheduler employed in the frequency domain to conduct the scheduling and the radio resource allocation. To decide the best approach to be followed, we employ the proposed PriMARL solution with different types of scheduling rules and compare them with other state-of-the-art solutions in terms of throughput, delay, packet loss, Peak Signal-to-Noise Ratio (PSNR), and Mean Opinion Score (MOS) for different traffic loads and characteristics. We show that the proposed solution achieves the best user QoE results.

During the COVID-19 pandemic, technology has been used to a lesser or greater extent to facilitate learning and has become an instrumental part of ensuring continuity of education. Students had no choice but to engage in online learning during periods of lockdowns. The quick transition to online learning had the potential to significantly affect the student learning experience and, as a result, their attitudes to studying at university. In this study, we examined self-reported students’ attitudes on their transition from face-to-face to online learning. Second-year students from Swinburne University of Technology in Melbourne, Australia, were invited to complete the questionnaire. Students reported a range of attitudes and experiences from very positive and enjoying discovering the new study mode to the very negative where motivation to study was lost and they were considering deferring or quitting their studies. Approximately half of the students discovered the benefits of both online and in-person education and expressed a preference for learning in a blended learning environment. The study results demonstrate the importance of digital technologies that provide flexible and agile educational opportunities with many students being open to new learning experiences despite missing the traditional approaches to education. The results of this study could inform further educational interventions when there is a need to move to online learning with little notice.