With the advent of Artificial Intelligence for healthcare, data synthesis methods present crucial benefits in facilitating the fast development of AI models while protecting data subjects and bypassing the need to engage with the complexity of data sharing and processing agreements. Existing technologies focus on synthesising real-time physiological and physical records based on regular time intervals. Real health data are, however, characterised by irregularities and multimodal variables that are still hard to reproduce, preserving the correlation across time and different dimensions. This paper presents two novel techniques for synthetic data generation of real-time multimodal electronic health and physical records, (a) the Temporally Correlated Multimodal Generative Adversarial Network and (b) the Document Sequence Generator. The paper illustrates the need and use of these techniques through a real use case, the H2020 GATEKEEPER project of AI for healthcare. Furthermore, the paper presents the evaluation for both individual cases and a discussion about the comparability between techniques and their potential applications of synthetic data at the different stages of the software development life-cycle.

In this paper, we propose a set of best practices for the responsible adoption of Generative Artificial Intelligence (AI) in higher education. These best practices provide a comprehensive framework for higher education institutions to effectively and ethically integrate Generative AI into their teaching and learning practices. The framework prioritises a responsible and human-centred approach, alongside pedagogical soundness, careful planning, transparency, as well as content quality. By exploring the responsible adoption of Generative AI in higher education, we seek to provide scalable, personalised learning experiences for large cohorts of students. Our research focuses on harnessing Generative AI to offer tailored educational content and generate constructive feedback for students. Additionally, by adhering to responsible AI practices, we aim to address challenges such as misinformation, copyright violations, and bias.

When students interact with an online course, the routes they take when navigating through the course can be captured. Learning Analytics is the process of measuring, collecting, recording, and analysing this Student Activity Data. Predictive Learning Analytics, a sub-field of Learning Analytics, can help to identify students who are at risk of dropping out or failing, as well as students who are close to a grade boundary. Course tutors can use the insights provided by the analyses to offer timely assistance to these students. Despite its usefulness, there are privacy and ethical issues with the typically centralised approach to Predictive Learning Analytics. In this positioning paper, it is proposed that the issues associated with Predictive Learning Analytics can be alleviated, in a framework called EMPRESS, by combining 1) self-sovereign data, where data owners control who legitimately has access to data pertaining to them, 2) Federated Learning, where the data remains on the data owner’s device and/or the data is processed by the data owners themselves, and 3) Graph Convolutional Networks for Heterogeneous graphs, which are examples of knowledge graphs.