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.
Human-Centered AI (HCAI) has emerged as a guiding paradigm for designing AI systems that align with human values, needs, and well-being, yet the field lacks consensus on what constitutes human-centeredness. This study addresses that gap through a four-phase sequential mixed-methods design: (1) thematic analysis of 81 HCAI definitions from academic, institutional, and industry sources, yielding 78 keywords; (2) frequency-based statistical categorization; (3) expert evaluation producing a final inventory of 26 attributes; and (4) a cross-sectional survey (N = 145), predominantly drawn from the Asia-Pacific region (77.2%, with Myanmar, Singapore, and Thailand most represented), in which practitioners, academics, and students rated each attribute on a 7-point Likert scale, complemented by a reflexive thematic analysis of open-ended responses. The 26-item scale demonstrated excellent internal consistency. Trust, values, benefits, needs, and usability were rated most highly, while affective and cognitive attributes—emotions, behaviours, and empathy—were consistently rated lower, a pattern the qualitative data suggest reflects perceived intractability rather than indifference. Inter-attribute correlations revealed interpretable substructures, including an experience/usability cluster, an emotion/empathy cluster, and a participatory engagement cluster, while human control operated as a conceptually independent dimension. Five qualitative themes provided interpretive context: user needs and augmentation as design drivers, ethical foundations and value alignment, trust as a relational outcome contingent on transparency, the complexity of human experience as a design challenge, and structural barriers including corporate incentives, regulatory gaps, and resource constraints. In this predominantly Southeast Asian sample, all three stakeholder groups showed substantial agreement on which attributes matter most and least. The primary divergence ran between academics and students: academics assigned higher importance to participatory and process-oriented attributes, while students emphasized tangible outcomes. Practitioners occupied an intermediate position, with a distinctive emphasis on ethical values. These findings offer an empirically grounded vocabulary for human-centeredness, positioned as an exploratory foundation for future psychometric refinement, with implications for HCAI design practice, education, and cross-stakeholder dialogue.
Large Language Models (LLMs) have materialised as revolutionary tools across various do- mains, showcasing exceptional capabilities in natural language processing and generation. However, their reliance on static pre-training data limits their ability to access up-to-date and domain-specific information. The existing research often treats augmentation strategies in isolation, and limited efforts have been made to systematically compare them through the lens of information integrity. This review focuses specifically on Retrieval-Augmented Generation (RAG) and Fine-tuning, identifying them as the two dominant paradigms for integrating external knowledge: RAG for retrieval-based context injection and Fine-tuning for parametric knowledge adaptation. While existing surveys predominantly focus on performance metrics like accuracy or latency, this paper addresses the critical gap of data fidelity-the preservation of truthfulness, integrity, and fairness during augmentation. We systematically synthesise empirical findings from diverse methodologies to determine how each approach mitigates hallucinations and bias. By comparing the trade-offs between retrieval-based context injection and parametric knowledge adaptation, this survey brings unique value to readers by providing a structured taxonomy, a unified evaluation frame- work, and actionable insights to guide future research and practical deployment of robust, high-fidelity LLMs.
Online graduate micro-credentials are promoted both as flexible learning pathways for working professionals and as portable signals of capability for employers and professional communities. Yet, scholarship on these credentials is dispersed across policy, education, technology, and workforce literatures, making it difficult to see how the field is framed and where evidence is accumulating. This study uses OpenAlex to build an updateable evidence map of online graduate micro-credentialing. A total of 2535 records (2010–2026) were retrieved and deduplicated to 2150 works. The corpus was annotated with a transparent seedless triage step. A conservatively revised keyword typology was then applied to a typology-eligible subset, and topic modeling was used to surface candidate themes. Within the typology-eligible subset, 223 records were classifiable. Learning-first framings (66.8%) and stackable framings (58.7%) remained more common, and a 100-record hand-coded audit supported the revised rules (80.0% full-quadrant agreement). Large thematic clusters concern workforce/economic skills, engagement-oriented digital learning, and broad online teaching/learning, while smaller badge-related, infrastructure, and adjacent-domain clusters require cautious interpretation. The map points to a literature still weighted toward pathway design and implementation, but typology validation also indicates that structural framing is more mixed than the earlier always-assigned counts suggested. By making the search space and annotation logic transparent, this study provides a rerunnable baseline for cumulative qualitative synthesis and a clearer agenda for future research on how online graduate micro-credentials function as both learning experiences and credential signals.
Introductory Java programming requires learners to reason about abstract computational concepts such as program state, control flow, and execution order, which often present substantial difficulties for novice programmers. These challenges may be further intensified for collegiate student athletes when programming instruction remains disconnected from the domain knowledge that shapes their prior experiences. This paper proposes a wrestling-inspired, state-based pedagogical framework that leverages the rule system of National Collegiate Athletic Association (NCAA) wrestling as an analogical knowledge domain for introducing foundational Java programming concepts. Within this framework, wrestling match states and scoring actions are systematically mapped to core programming constructs, which include variable assignment, conditional branching, loops, method invocation, and program termination. This paper is positioned as a conceptual and pedagogical framework study rather than an empirical intervention study. It focuses on the theoretical rationale, conceptual alignment, instructional mappings, and classroom implementation possibilities of a wrestling-inspired approach. This paper does not report participant data, learning assessments, or comparative outcome measures. Instead, it illustrates how sport-specific mental models can be transformed into structured instructional representations that may support learners’ reasoning about program execution. By integrating domain-aligned cognitive schemas with programming instruction, the proposed framework offers a structured knowledge scaffolding approach that is designed to support novice understanding of computational processes in introductory programming education.
We study a minimal quantum pre-processing filter for image feature extraction built from angle embeddings and two Control-NOT (CNOT) gates. Our goal is to assess whether such a lightweight quantum front-end can benefit classical classifiers and to investigate whether its induced entanglement—measured via average single-qubit von Neumann entropy—relates to predictive performance. The circuit admits three spatially symmetric layouts (diagonal, vertical, and horizontal), each producing distinct feature transformations. Experiments show that the filter can provide modest gains in shallow learning settings, but it does not consistently outperform strong classical baselines. Notably, we find no reliable relationship between entanglement and classification accuracy: variations in average entropy fail to consistently track performance. These results suggest that the utility of simple quantum filters is determined more by dataset structure and model capacity than by entanglement magnitude, offering practical guidance for the design of hybrid quantum–classical learning pipelines.
This study examined how human resource management (HRM) training and development practices contribute to strengthening knowledge absorptive and protective capacities within South African state-owned enterprises (SOEs). Using an exploratory mixed-methods approach, this research was conducted in two phases. Firstly, 20 HR managers were interviewed, and annual reports from nine SOEs were reviewed. Thematic analysis, supported by Atlas.ti, revealed key insights that informed the design of a survey used in the second phase. In the second phase, the survey was administered to 585 randomly selected employees across three SOEs, achieving a 25% response rate. Data analysis carried out with Statistical Analysis Software (SAS) version 8.4 showed strong reliability, with a Cronbach’s alpha of 0.94. Findings indicate that training and development initiatives play a significant role in building absorptive capacity as they enhance knowledge acquisition and the ability to integrate new skills. These practices also reinforced employees’ tacit knowledge base, particularly through job-specific training and skills development. However, whilst HRM practices were effective in knowledge absorption, they were less successful in safeguarding and protecting critical tacit knowledge against potential loss. This study highlights the dual challenge facing SOEs: advancing employees’ capacity to absorb knowledge whilst also developing stronger mechanisms to protect valuable expertise.
This study examines the factors driving perceived Study Efficiency and Exam Readiness associated with ChatGPT use among STEM students in higher education. Although prior research on generative artificial intelligence (GenAI) has largely focused on adoption and attitudes using descriptive or linear statistical approaches, limited empirical work has explored how students’ interactions with such tools relate to learning-related outcomes. To address this gap, this study applies an interpretable machine learning (ML) framework to identify key predictors of learning gains from ChatGPT use. Data were obtained from a large-scale global survey of STEM students (n = 10,525) across 109 countries and territories, capturing usage patterns, perceived capabilities, satisfaction, and academic outcomes. Two eXtreme Gradient Boosting (XGBoost)-based ML classification models were developed to predict Study Efficiency and Exam Readiness, and SHapley Additive exPlanations (SHAP) were used to interpret feature-level contributions. The models achieved strong predictive performance for the high-gain class, with an accuracy of 0.93 (F1 = 0.96) for Study Efficiency and 0.86 (F1 = 0.92) for Exam Readiness. Results indicate that motivation, personalized learning support, improved access to knowledge, facilitation of study activities, and exam-focused study assistance are key predictors of learning gains. These findings offer empirical and practical insights for educators and policymakers seeking to design effective and pedagogically sound AI-assisted learning environments in STEM education.
This paper examines the intersection of entrepreneurship, innovation, and sustainability in the tourism sector through the lens of knowledge creation and transfer. It focuses on the Tourism Creative Factory (TCF) ideation programme, developed under Turismo de Portugal’s Fostering Innovation in Tourism 2.0 initiative. Using a case study methodology, the research situates the 2021–2022 “RESTART” edition of TCF within broader theoretical frameworks of knowledge-based development and organisational learning. This study highlights the programme’s role in facilitating knowledge exchange among participants, mentors, and institutional actors, thereby enhancing entrepreneurial readiness and resilience in a post-pandemic context. Emphasis is placed on mentorship, capacity-building, and experiential learning as mechanisms for knowledge management, enabling the 39 selected participants to develop sustainable business models and Minimum Viable Products (MVPs), with the 16 most innovative being selected for a final pitch presentation to a panel of experts representing diverse sectors of the entrepreneurial ecosystem. The findings underscore the transferability of TCF’s methodology to other knowledge-intensive sectors and contribute to advancing theoretical and practical understanding of how structured ideation programmes function as knowledge systems within tourism and beyond.
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.
Complexity science studies systems in which properties and behaviors emerge at meso- and macroscales that are difficult to predict and model by observing the properties and behaviors exhibited by the system’s components at smaller scales. The set of relationships that exist among post-secondary school curricula and job markets is one example of such a system. Prior work has undertaken the challenge of modeling this system for several purposes, one of which has been to develop retrieval and ranking algorithms in the education–career domain. A particular emergent property that is closely bound up with this prior work, and that is the focus of the present work, is the salience of a course with respect to a specific objective. The specific objective that we are interested in here is career usefulness, which means that our overall task is to rank order courses based on their usefulness in helping a student to obtain and perform a specific job. One aspect of this overall task that remains understudied concerns how it can best be performed in an interpretable manner and whether existing interpretable methods that may be applied to it, such as text-based similarity measures and document-ranking functions, represent workable solutions or whether an approach involving more detailed modeling of the underlying complex system may prove more effective. The purpose of this article is to answer this question, and, in order to do this, most of this article’s content is devoted to the latter kind of approach, because the former kind is described in detail in the existing literature. The specific approach of the latter kind that we investigate is based on, first, developing a heterogeneous knowledge graph model of the overall complex system, and, second, developing a procedure that quantifies salience using the strength of the skill-dependency chains that link a course to a specified job. To evaluate our methods, we perform a human subjects study in which we leverage the domain expertise of fifty participants. The results of the study demonstrate that the latter approach produces career-motivated course recommendations, as well as accompanying explanations, which systematically exceed those produced by the former approach, in terms of both their quality and usability.
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.
The use of Artificial Intelligence (AI) in learning is expanding globally; however, the full potential of AI tools in the Open and Distance Learning (ODL) context, particularly at the Institute of Adult Education (IAE), remains underexplored. This study examined the IAE ODL students’ perspectives on the use of AI tools in learning. Specifically, it investigated ODL students’ familiarity with AI, AI preferences and use in learning, and perspectives on AI tool use in ODL. The study employed a mixed-methods approach, utilising a convergent parallel design to collect data from 93 second- and third-year ODL students at the Dar es Salaam and Morogoro Campuses. The findings revealed that 94.7% of students were familiar with AI, mainly after beginning their studies; 87% used ChatGPT for learning, and 57% used AI to answer their questions. In addition, 98% of students argued that the utilisation of AI in ODL is inevitable, citing its role in enhancing self-learning, improving access to learning materials, and saving time. Based on the findings, the study suggests that enhanced access to and awareness of diverse AI tools may help maximise their potential benefits in learning. The study also calls for academic integrity, ethical use, peer learning, and human-AI interaction among ODL students and institutions for the effective utilisation of AI in ODL.
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.
This study conducts a systematic bibliometric review of artificial intelligence (AI)-based approaches to tacit knowledge extraction and management. Drawing on data retrieved from Scopus and Web of Science, this study analyzes 126 publications published between 1985 and 2025 using VOSviewer and Biblioshiny to map citation networks, keyword co-occurrence patterns, and thematic evolution. The results identify nine major clusters spanning machine learning, natural language processing, semantic modeling, expert systems, knowledge-based decision support, and emerging hybrid techniques. Collectively, these findings indicate a field-wide shift from manual codification toward scalable, context-aware, and semantically enriched approaches that better support tacit knowing in organizational practice. Building on these insights, the paper introduces the AI–Tacit Knowledge Co-Evolution Model, which situates AI as an epistemic partner—augmenting human interpretive processes rather than merely codifying experience. The framework integrates Polanyi’s concept of tacit knowing, Nonaka’s SECI model, and sociotechnical learning theories to elucidate how human–AI interaction transforms the dynamics of knowledge creation. The review consolidates fragmented research streams and provides a conceptual foundation for guiding future methodological development in AI-enabled tacit knowledge management.
Artificial Intelligence, now commonly called AI, is having an increasingly big impact on society. There are fears that may be negatives or downsides, especially when Artificial Intelligence is used unethically. But how are humans guiding these machines to know whether the choice, the decision, is ethical? Since 2007, one way to check the ethicality of any choice has been to apply the JUSTICE model. This framework helps practitioners decide whether a specific action is or is not ethical by looking through one or more of the seven JUSTICE lenses: Justice, Utilitarian, Spiritual Values, TV rule or Transparency, Influence, Core, and Emergency. Now, in this era of increasing prevalence of Artificial Intelligence, with humans making decisions often together with machines, can the JUSTICE framework still be useful? Yes, it can. We look at each of those seven components. Each may give guidance in some situations. Of the seven, it seems that T or the TV test is most likely to give guidance in this new era.
Small- and medium-sized enterprises (SMEs) are increasingly embracing digital transformation (DT) to remain competitive; however, the enabling role of knowledge management (KM) remains underexplored. This systematic literature review investigates how KM supports DT in SMEs, focusing on strategic processes, tools, barriers, and policy contexts. A structured search was conducted in Google Scholar, Scopus, and Web of Science using the string: (“knowledge management” OR “KM”) AND (“digital transformation” OR “DT”) AND (“small and medium enterprises” OR “SME”). The search yielded 32,547 results, from which 19 studies met the eligibility criteria (English, 2020–2025, KM–DT focus, clear methodology). Results indicate that KM supports DT primarily through change management (31.58%), innovation enablement (21.05%), as well as improved decision-making and agility (15.79%). The most cited tools include KM systems, AI/analytics, and collaborative platforms. Major barriers include limited resources, lack of digital skills, and poor KM culture. Critical success factors identified are leadership commitment (26.32%) and strategic alignment (21.05%). Theoretical foundations are dominated by the Resource-Based View and Dynamic Capabilities Theory. While KM is proven to be a strategic driver of DT in SMEs, more empirical and policy-grounded studies are needed. This review provides a framework to guide future research and inform SME practitioners and policymakers.
Almost all societal grand challenges, whether concerning the environment, health, well-being, or the development of sustainable economic models, have at their heart a need to understand people’s behaviour. However, uniting data and insights across disparate fields requires an explicit and shared understanding of concepts, variables, and ideas (e.g., how to characterise and differentiate behaviours). Ontologies provide a mechanism for creating this explicit and shared understanding and are starting to be developed and used in the social and behavioural sciences. This paper proposes an online co-design approach to use and develop ontologies of behaviour to specify the characteristics of behaviour (e.g., habitual, changeable, effortless) and studies that investigate behaviour as part of a project designed to understand how behaviours are related. We report on our experience of collaborative co-development of ontologies using real-time interactive tools and reflect on the benefits and challenges of our approach. We also offer a set of recommendations for researchers interested in applying such methods to co-develop ontologies. The work contributes to efforts to understand the characteristics of behaviour and enable these to be used to understand questions about behaviour (e.g., is poor sleep associated with greater engagement in habitual behaviours?).
This article presents a case study showing the development of a chatbot, named Selene, in a Software-as-a-Service platform for behavioral analysis using Retrieval-Augmented Generation (RAG) integrating domain-specific knowledge and enforcing adherence to organizational rules to improve response quality. Selene is designed to provide deep analyses and practical recommendations that help users optimize organizational behavioral development. To ensure that the RAG pipeline had updated information, we implemented an Extract, Transform, and Load process that updated the knowledge base of the pipeline daily and applied prompt engineering to ensure compliance with organizational rules and directives, using GPT-4 as the underlying language model of the chatbot, which was the state-of-the-art model at the time of deployment. We followed the Generative AI Project Life Cycle Frameworkas the basic methodology to develop this system. To evaluate Selene, we used the DeepEval library, showing that it provides appropriate responses and aligning with organizational rules. Our results show that the system achieves high answer relevancy in 78% of the test cases achieved and a complete absence of bias and toxicity issues. This work provides practical insights for organizations deploying similar knowledge-based chatbot systems.
