Knowledge

The paper sought to examine the role of collaboration in sustaining citizen science activities and projects in academic libraries. The study applied a quantitative approach and a survey design to assess knowledge and understanding of citizen science by academic librarians to advance research relevant to SDGs. A standardised questionnaire was distributed to 185 academic librarians affiliated with the Higher Education and Libraries Interest Group (HELIG). The survey yielded a response rate of 34% since only 63 academic librarians volunteered to participate in the completion of the questionnaire. Data was analysed using SPSS version 29. Findings revealed that citizen science is a new concept in academic libraries in South Africa. To advance the use of citizen science in contributing towards SDGs, academic librarians need to raise awareness, foster collaborations, and initiate advocacy efforts to promote and support citizen science activities. The research further revealed that a work-integrated learning and community engagement department should be established within the library to advocate for citizen science activities. There is a need to visit schools to introduce citizen science at the grassroots level to increase the visibility of the field and to lay a foundation for scientific literacy at an early stage. Although the research setting was in academic libraries, for future research, it will be beneficial to conduct such a study in a public library setting to achieve varying perspectives from the community members where the concept of citizen science emanates.

The deployment of autonomous systems in human environments demands sophisticated mechanisms for recognizing and preventing harm. This paper proposes an innovative discovery method for identifying harm-relevant features through the systematic analysis of thick harm verbs—semantically and pragmatically rich linguistic concepts like “puncture”, “crush”, or “poison” that encode both the mechanics and normative evaluations of specific harm types. By analyzing thick harm verbs to extract the information they encode, we can systematically identify the objects, properties, mechanisms, and contextual conditions that autonomous systems need to track to recognize and prevent harm. We demonstrate how this discovery method can be implemented with the support of large language models as analytical assistance tools, showing how human analysts can operationalize the framework with current technology. The resulting feature specifications discovered through this method provide foundations for constructing harm ontologies that bridge abstract ethical principles and concrete system requirements, addressing a critical gap in autonomous systems design while maintaining explanatory transparency essential for safe deployment in human environments.

This research focuses on ontology-driven conversational agents (CAs) that harness large language models (LLMs) and their mediating role in performing collective tasks and facilitating knowledge-sharing capabilities among multiple healthcare stakeholders. The research addresses how CAs can promote a therapeutic working alliance and foster trustful human–AI collaboration between emergency department (ED) stakeholders, thereby supporting collaborative tasks with healthcare professionals (HPs). The research contributes to developing a service-oriented human–AI collaborative framework (SHAICF) to promote co-creation and collaborative learning among patients, CAs, and HPs, and improve information flow procedures within the ED. The research incorporates agile heavy-weight ontology engineering methodology (OEM) rooted in the design science research method (DSRM) to construct an ontological metadata model (PEDology), which underpins the development of semantic artifacts. A customized OEM is used to address the issues mentioned earlier. The shared ontological model framework helps developers to build AI-based information systems (ISs) integrated with LLMs’ capabilities to comprehend, interpret, and respond to complex healthcare queries by leveraging the structured knowledge embedded within ontologies such as PEDology. As a result, LLMs facilitate on-demand health-related services regarding patients and HPs and assist in improving information provision, quality care, and patient workflows within the ED.