John Domingue

Littoral zones are not merely boundaries between land and water; they are dynamic bridges and buffers, fundamentally shaped by shifting environmental forces. The intangible cultural heritage embedded within these coastal zones (encompassing generational practices of resilience, navigation, and ecological adaptation) is highly vulnerable to loss. Current information systems are poorly equipped to safeguard these living practices. Historically grounded in "urban ontologies," digital architectures inherently favour stability, permanent landmarks, and predictable spatial control. This paper presents a gap analysis examining how existing information systems, from geo-spatial hypermedia to immersive media, fail to capture the fluid, cyclical, and seasonally contingent nature of littoral heritage. By contrasting the performative, ultra-local realities of the coast against the static nature of traditional archives, we outline the structural limitations of current digitisation efforts. Finally, we propose conceptual foundations for future interactive architectures capable of supporting the active transmission, continuous evolution, and structural fluidity required to truly preserve littoral knowledge.

As GenAI tools become more pervasive in learning, the ways in which educators can influence the behaviour of these systems should be a focus for research and innovation. As part of designing and trialling an institutional AI digital assistant for study, we are exploring where and how educators’ technological, pedagogical and content expertise could be brought to bear in the way in which the assistant behaves. In this paper, we draw on feedback from 20 educators and education researchers who took part in a trial of a prototype of the system and reflect on the process of designing the system to be embedded into the study of online courses. We highlight six methods through which educators can exert influence over the behaviour of such assistants and reflect on the alignment, possibilities and limitations of these to meet educator goals. This contributes to opening up a space for educators to think strategically and creatively about teaching with AI.

In Industry 5.0 it is essential that humans are in the loop of technology integration of industry processes. With the advancements of Generative Artificial Intelligence (GenAI), a lot of new opportunities and challenges for learning and teaching are present. Many students already use publicly available AI Digital Assistants (p-AIDA) like ChatGPT for academic purposes. However, there are concerns around the use of such p-AIDA tools, particularly in terms of academic integrity, data privacy, intellectual property, and the impact on the quality of education. Furthermore, many higher education institutions have substantial learning materials and data about students that they may not want to share with p-AIDA. Therefore, using the Technology Acceptance Model (TAM) and following a Design-Based Research (DBR) approach, we explored the perspectives and experiences of a beta-test of an institutionally developed AIDA (i-AIDA) with 18 UK students using multiple methods and data sources (including pre-post-test, interviews, think-aloud, and prompt analysis). Our research underscores the potential benefits and limitations of in-house i-AIDA in enhancing learning experiences without compromising academic integrity or privacy, and how higher education institutions can prepare themselves for Industry 5.0.

The rapid evolution of Generative AI (Gen AI) and its integration into higher education necessitates a critical evaluation of teaching and learning practices. Many students already use publicly available AI Digital Assistants (p-AIDA) such as ChatGPT for academic purposes. However, there are concerns around the use of such p-AIDA tools, in particular in terms of data privacy, academic integrity, Intellectual Property (IP), and the impact on the quality of education. We therefore explored the perspectives of AI usage with 106 students in two consecutive studies using a Design-Based Research (DBR) approach. Study 1 explored the lived experiences a demoed prototype of an institutional AI Digital Assistant (i-AIDA) with ten distance learning students. Building on these findings, in Study 2 the preliminary results were validated with a cohort of 96 students using an online survey on whether the development of an i-AIDA might mitigate some of these concerns. By comparing i-AIDA with p-AIDA in two studies our research underscores the potential benefits and limitations of in-house Gen AI tools in enhancing learning experiences without compromising academic integrity, IP or privacy.

This mixed methods study explores 315 distance learning students’ perceptions of artificial intelligence digital assistants (AIDAs) over an 11-month period in three distinct studies. The research investigates student perspectives on both a publicly available AI digital assistant (p-AIDA), such as ChatGPT, and a potential institutionally developed AI digital assistant (i-AIDA). Findings indicate that students highly valued 24/7 immediate academic feedback and the personalisation of the i-AIDA, and these perspectives remained largely stable across the three studies. However, concerns about academic integrity, data privacy, and ethical implications persisted across the studies. A cluster analysis identified three distinct student groups of highly critical, supportive, and keenly supportive learners, with key differences based on prior GenAI experience, educational background, and age. This study underscores both the potential and challenges of developing institutional AI solutions to enhance student learning while addressing privacy and ethical concerns.

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.