When large language models participate in scientific inquiry, the wording of a request helps shape the knowledge claims presented for appraisal. This conceptual article examines the interpretive demands that follow. Technical improvements cannot remove all such demands: even accurate responses require framing appropriate to the inquiry, and generated text can sound authoritative or cautious without adequate evidential support. We propose four interpretive competencies: framing awareness, uncertainty detection, evidential coherence judgment, and interactional reflexivity. The framework organizes them by what users examine—their own contribution or the generated representation—and whether they examine a bounded instance or relations among instances. Medicine and biomedical research provide the worked examples. Illustrative runs with three language models show how several vulnerabilities appear in generated responses; they do not validate the proposed competencies. We distinguish the framework from established approaches to evidence appraisal and AI literacy by its object and level of analysis, and outline tasks for cultivating and assessing the competencies. We argue that interpretive competence can become part of the epistemic infrastructure of AI-mediated knowledge production when embedded in shared training, appraisal, and assessment practices.
Artificial intelligence (AI) is reshaping library services worldwide, yet little is known about its integration into South African academic libraries. This study analyzed AI-assisted services and emerging practices in South African university libraries through a qualitative content analysis of the literature and institutional reports. The findings revealed five key areas of AI application: virtual reference chatbots, automated cataloging and metadata creation, recommendation systems, generative AI, and digital preservation tools. These services may improve resource discovery, operational efficiency, and user engagement, but their adoption is constrained by funding limitations, skills shortages, infrastructure gaps, and ethical concerns. Although some libraries promote AI literacy and policy awareness through training and advocacy, few have implemented advanced AI tools on a broad scale. The study concludes that South African academic libraries remain at an early stage of AI adoption and recommends ethical governance frameworks, infrastructure investment, staff development, and inter-institutional collaboration to support responsible and effective implementation.
Generative AI (genAI) is increasingly being integrated into critical appraisal and knowledge synthesis of research, with growing discourse on its value for qualitative research. Moreover, ChatGPT covers a major portion of the market share and is widely used by researchers and academics. However, a major gap remains in genAI’s ability to assess the reporting quality of qualitative research studies. This pilot study aimed to determine whether GPT-5 could reliably assess the reporting quality of health-related qualitative research studies. The Standards for Reporting Qualitative Research (SRQR) checklist was used as a framework for completing both human and AI chatbot assessments. The SRQR is a widely used validated checklist for enhancing the reporting of qualitative research. Overall, 68 peer-reviewed studies were selected for testing, comparing human SRQR assessments with GPT-5 assessments. Findings revealed a wide variability in agreement between human and GPT-5 assessments, with crude agreement ranging between 60 and 100% and interrater reliability ranging between 0 and 0.85 across checklist items. Simpler, objective checklist items such as study purpose were often correctly identified and had stronger agreement compared to nuanced items. The model struggled with the following SRQR items leading to an “Unclear” response: research design/paradigm (41%), researcher reflexivity (41%), data processing (27%), and trustworthiness (46%). These preliminary results suggest that GPT-5 may struggle with tasks requiring nuance and interpretation of qualitative research, which is rich in complex methods and differing worldviews. Currently, GPT-5 and other AI chatbots should be used with caution and alongside human oversight when assessing the reporting quality of qualitative research within public health and healthcare.
The COVID-19 pandemic and subsequent Emergency Remote Teaching (ERT) response by universities led to the rapid and widespread adoption of learning technologies. There is much to learn about the role of the pandemic as a disruptive influence on technology adoption in higher education. However, despite a great deal of research conducted both during and immediately after the ERT period, there has been limited attention to its longer-term effects on technology usage. This paper addresses a gap in the literature by exploring the lasting influence of ERT on practices at the University of York (UK). Taking a qualitative approach and socio-cultural orientation, we undertook semi-structured interviews with teaching leaders in thirteen departments across a wide range of disciplines. Lasting changes in the adoption of digital tools following ERT were evident in all departments, but the nature and impact of these changes were shaped by longer-term influences, often predating ERT and further shaped by subsequent changes and challenges such as increased student numbers and the widespread adoption of Generative Artificial Intelligence. We conclude that the impact of ERT on technology adoption needs to be placed in context as one influence among other factors in an evolutionary process of pedagogical change.
Purpose: Competency-Based Education (CBE) represents a fundamental shift from traditional credit-hour systems, emphasizing mastery of defined skills and knowledge outcomes over time-based seat requirements. Despite growing institutional adoption, a comprehensive synthesis of CBE’s implementation frameworks, outcome evidence, and equity implications in the post-2015 context remains limited. Prior systematic reviews of CBE either predate the digital transformation era, focus on single disciplines, or examine only specific implementation dimensions. This review addresses those gaps by synthesizing the full breadth of CBE evidence published between 2015 and December 2025. Methods: This systematic review adheres to PRISMA 2020 guidelines. Four databases (Google Scholar, ERIC, Scopus, and institutional case-study repositories) were searched using four keyword clusters: “Competency-Based Education,” “Traditional Teaching and Students’ Competencies,” “Credit System and Students’ Achievement Measures,” and “Competency-Based Education and Workforce. After removing 125 duplicates and applying eligibility criteria (2015–December 2025; post-secondary focus), 73 sources were retained: 68 peer-reviewed articles and 5 accredited institutional case-study reports. A six-theme thematic synthesis was conducted following the work by Braun and Clarke; inter-rater reliability was κ = 0.79 on a 20% subsample (n = 15). Results: Six themes emerged: (1) Student-Centered Learning Philosophy, (2) Outcome-Based Assessment, (3) Flexible Pacing and Mastery Standards, (4) Implementation Frameworks, (5) Institutional Case Studies (University of Wisconsin Flexible Option, SNHU College for America, Purdue Global ExcelTrack, Northeastern Align, and Western Governors University), and (6) Challenges and Benefits of CBE. Evidence suggests that CBE is associated with improved adult-learner retention, workforce development alignment, and recognition of prior learning; however, these benefits are methodologically constrained, and equity implications remain structurally plausible but empirically unconfirmed. Resistance within institutions, misalignment with accreditation standards, and resource demands are the primary barriers to implementation. Conclusions: CBE provides a credible alternative to credit-hour systems for post-secondary institutions serving diverse learner populations, supported by a growing but methodologically constrained evidence base in which selection bias cannot be excluded as a contributing explanation for observed outcome advantages. Successful implementation requires phased institutional change, comprehensive faculty development, and proactive engagement with accrediting bodies. Future research should prioritize longitudinal outcome data, equity analyses by learner subgroup, and AI-driven adaptive assessments within CBE frameworks. Equity benefits are structurally plausible by design but remain empirically unconfirmed; no included study provides demographic subgroup data sufficient to verify equitable distribution of outcomes.
