Artificial intelligence (AI), particularly generative AI, is rapidly transforming teaching methodology in universities as well as their assessments, research, and academic work, yet there is only limited evidence about how lecturers in sub-Saharan African universities adopt these tools and what professional development they require. This qualitative study explored AI adoption, perceived benefits and risks, and needs for professional development among lecturers and administrators in the Faculty of Education at one private university in Uganda. A convenience sample of 28 participants took part in individual semi-structured interviews during 14-day of campus visit in January, February, and March 2026. The interview guide was pilot tested; while interviews lasted for 38–67 minutes each, meaning saturation was judged to have been reached by the 25th interview, with three additional interviews required to assess the adequacy of the developing coding framework. Reflexive thematic analysis generated 6 themes: pragmatic but uneven adoption; AI as partner for efficiency and creativity; uncertainty about accuracy, authorship, and academic integrity; infrastructural and institutional constraints; demand for practice-based and discipline-relevant professional development; and the need for governance, communities of practice, and protected learning time. Lecturers more often framed AI through teaching, assessment, and workload concerns, whereas administrators more often foregrounded policy consistency, governance, and institutional support. Participants reported using AI frequently for lesson planning, summarizing, language editing, idea generation, assessment preparation, research support, and routine administration. However, adoption was constrained by unreliable connectivity, subscription costs, uneven AI literacy, limited policy guidance, privacy concerns, and fear of students’ overreliance. The study proposed a contextualized professional-development model combining foundational AI literacy, pedagogical design, research integrity, data protection, assessment redesign, peer mentoring, and continuing technical support. The findings provide a situated account of responsible AI adoption in an East African Faculty of Education and should not be interpreted as representative of Ugandan higher education as a whole.
University traffic safety management acceleratingly relies on digital systems, yet frontline decisions continue to pose challenges when rules are dispersed across independent departments. Situations are composite as responsibilities often overlap. This study developed and analytically assessed an institutional knowledge embedding (IKE) framework for generative artificial intelligence (AI)-assisted decision support in university traffic safety management. A design-oriented conceptual research method was adopted, combining an integrative synthesis of higher education governance, institutional theory, organizational knowledge, socio-technical systems, accountability, and retrieval-augmented generation (RAG) research with structured scenario analysis. The analysis produced five linked governance mechanisms: normative encoding, contextual qualification, responsibility allocation, evidentiary inscription, and reflexive updating. These mechanisms were translated into a structured institutional knowledge unit and a RAG-enabled multi-agent architecture in which AI supported retrieval, comparison, workflow drafting, and evidence organization, while authorized human actors retained final judgment. Structured comparison across four university traffic-safety scenarios demonstrated that the framework became most valuable not when it automated decisions, but when it made explicit rule validity, contextual conditions, responsible roles, evidentiary requirements, and escalation points. The study further derived an institutional non-closure rule: when valid grounds, applicable rules, or authorized responsibility could not be established, the system should abstain from determinate recommendations and transfer the case to human review. The framework contributes to further research on educational technology, university governance, and campus safety management by linking generative AI adoption to the improvement of institutional accountability, procedural traceability, and controlled organizational learning.
The rapid advancement of educational technologies has created new opportunities to integrate language learning with education of global sustainability. This study explored how the metaverse could help enhance students’ environmental awareness of climate change via the perspectives of instructors teaching English as a foreign language (EFL). Specifically, it examined instructors’ knowledge of the metaverse, their perceptions of its educational potential, and the challenges associated with its implementation. A mixed-method research design was employed using an online questionnaire completed by 70 EFL instructors from Kosovo, Montenegro, North Macedonia, Albania, Turkey, Poland, and Saudi Arabia. Quantitative data were analyzed using descriptive statistics and a Chi-square test of independence, while qualitative responses were examined through inductive thematic analysis. The findings indicated that most participants considered environmental education a key component of English language teaching and viewed the metaverse as a promising tool for promoting awareness of climate change. Nevertheless, some instructors reported limited familiarity with the technology and identified barriers like insufficient digital infrastructure, infringement of data privacy, unequal access to technological resources, and the need for professional development. The Chi-square analysis further revealed a statistically significant relationship between teaching experience and perceptions of the metaverse, with less experienced instructors expressing greater optimism regarding its educational potential than their more experienced counterparts. The findings suggested that although EFL instructors demonstrated strong willingness to integrate immersive technologies into language education, successful implementation would depend on targeted teacher training, improved technological infrastructure, and institutional support.
Educational digital transformation has been elevated to a national strategic priority, with the development of teachers’ digital–intellectual thinking emerging as a critical determinant of transformation effectiveness. In practice, however, teachers commonly encounter a dilemma characterized by “learning without application” and “superficial application” when engaging with intelligent technologies. Although teachers perceive themselves as competent in evaluating online information, their actual performance frequently falls short of effective implementation. Furthermore, while self-efficacy in information literacy enhances teaching engagement, the process of technology integration is accompanied by complex emotional experiences. Existing research predominantly examines singular dimensions such as teachers’ information literacy or technology acceptance, thereby failing to uncover the systemic mechanisms underlying retardant effects. By introducing cognitive ecology theory and employing literature analysis, questionnaire surveys, and in-depth interviews, the retardant effects impeding the development of teachers’ digital–intellectual thinking were systematically diagnosed. The findings indicate that these retardant effects manifest primarily in four forms: cognitive inertia fixation, data–meaning construction fracture, human–machine collaborative perception misalignment, and professional identity narrative disruption. These manifestations are rooted in a structural imbalance among individual cognitive schemas, technological environment provision, and community-of-practice ecologies. Accordingly, an ecological transition pathway was designed, encompassing “cognitive restructuring—environmental enabling—ecological synergy.” This study deepens the understanding of the developmental patterns of teachers’ digital–intellectual thinking, extends the applicability of cognitive ecology theory within the field of educational digitalization, and provides actionable diagnostic tools and practical implementation strategies for teacher training and the construction of school-based digital–intellectual ecologies.
The accelerated digital transformation of higher education institutions (HEIs) has emphasized digital leadership in fostering academic excellence. However, limited empirical evidence existed regarding the mechanisms through which digital leadership influenced academic performance, particularly within the context of higher education in the developing country. This study investigated the direct effect of digital leadership on academic performance as well as the mediating role of knowledge sharing among academic staff in Indonesian HEIs. Grounded in Social Exchange Theory (SET) and the Knowledge-Based View (KBV), the study employed a quantitative and cross-sectional research design, with data collected from 274 academic staff members working in HEIs in Yogyakarta, Indonesia. Data was analyzed using partial least squares structural equation modeling (PLS-SEM). The findings revealed that digital leadership had a significantly positive effect on academic performance (β = 0.229, p < 0.01) and knowledge sharing (β = 0.913, p < 0.001). Knowledge sharing was found to significantly enhance academic performance (β = 0.658, p < 0.001). It also partially mediated the relationship between digital leadership and academic performance, accounting for 72.4% of the total effect. The results indicated that digital leadership primarily improved academic performance by creating an environment that promoted knowledge exchange among academic staff. This study contributes to the digital leadership literature by providing empirical evidence of knowledge sharing as a key mediating mechanism in higher education settings. Practical implications were offered for university leaders and policymakers seeking to improve academic outcomes through digital transformation initiatives and knowledge-sharing ecosystems.
The peer review process is a significant aspect of academic publishing, which shoulders the responsibility to ensure the quality and integrity of scholarly work. However, challenges such as a lack of formal peer review training, scarcity of reviewers, and bias in the selection process have posed daunting challenges. This paper explored the potential of PhD candidates and postdoctoral fellows as peer reviewers through academic partnerships with publishing houses. “AI meta-audit test case” was employed to analyze “if there exists any publishing activities, in particular peer review services, involved in academic partnerships as well as the impact of PhD candidates and postdoctoral fellows on peer review activities”. Our objective is to evaluate the extent to which PhD candidates and postdoctoral fellows contribute to academic publishing activities through peer review services facilitated by academic partnerships. The project team defined key metrics in a set scope, determined data sources, designed AI-powered analysis approach, proposed hypotheses, identified risks and challenges, and above all, provided evaluation and recommendations. The study highlights the demand for structured peer review training, incentives for early-career researchers, and institutional collaborations to enhance the quality and efficiency of peer review process.
Mental health problems like eco-anxiety were caused by the rise of climate change and constituted a major concern within higher education settings. Eco-anxiety, a chronic fear of environmental catastrophe, profoundly impacts students’ psychological well-being and academic engagement. This qualitative case study interviewed five professors teaching English as a Foreign Language (EFL) at the University of Turku in Finland and reported how they perceived eco-anxiety and managed its integration into language education. Data collection consisted of semi-structured interviews, accompanied by eight-hour structured and non-participant classroom observations in five different classes over a full week. To ensure analytical rigor, a 30% independent cross-coding verification protocol was completed on the transcripts, and a matrix analysis was applied to compare stated educational beliefs with field observations. The findings revealed a marked theory-practice gap: while EFL professors demonstrated high conceptual awareness of eco-anxiety and recognized it as a valid student’s response, its active pedagogical integration remained significantly limited. Data from objective observation reported an almost total absence of explicit discourse on climate or eco-anxiety in daily teaching routines. Matrix triangulation substantiated that the goodwill of the individual educator was systematically hampered by severe institutional barriers, primarily curriculum overload, limited teaching time, and a lack of formalized institutional or financial support. When professors addressed isolated sustainability tasks through frameworks such as the United Nations Sustainable Development Goals (SDGs), students experienced an intense emotional trajectory that could temporarily heighten their affective filters and inhibit language production. However, when properly supported, these discussions fostered long-term vocabulary growth and communicative agency. These findings urge curriculum architects to design structured resources and institutional frameworks that seamlessly integrate emotional and environmental education into the core language curriculum.
ChatGPT, a widely used generative AI tool, has incessantly attracted significant attention from researchers seeking to understand the factors that influence its adoption in higher education. This study examined the determinants of ChatGPT adoption among university students in Yogyakarta, Indonesia, one of the largest educational centers with more than 100 higher education institutions. Drawing on the value-based adoption model (VAM), the study incorporated three AI-related factors, i.e., AI self-efficacy (ASE), perceived academic value (PAV), and privacy concerns (PC) to appropriately explain students’ behavior in AI adoption. A hybrid analytical approach combining partial least squares structural equation modeling (PLS-SEM) and machine learning (ML) techniques, including Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Artificial Neural Networks (ANN) with SHapley Additive exPlanations (SHAP)-based interpretation, was employed to test ten hypotheses with survey data collected from 484 students across five selected universities in Yogyakarta. The results indicated that perceived value (PV) (β = 0.453) and adoption intention (β = 0.521) were the strongest predictors of actual ChatGPT usage. Among the benefit-related factors, perceived usefulness (PU), perceived enjoyment (PE), and ASE significantly enhanced PV, whereas PC (β = −0.213) represented the most influential barrier to adoption. The ML models produced consistent findings, with XGBoost achieving the highest predictive performance (AUC = 0.912). SHAP analysis further highlighted PV and PC as the most significant variables. By extending VAM with AI-specific constructs and integrating SEM with ML techniques, this study contributes to an enhanced understanding of generative AI adoption in higher education and offers actionable insights for policymakers and university administrators in support of responsible AI integration in Indonesian universities.
To address the limitations of traditional policy instrument analysis—such as labor-intensive coding, high subjectivity, and time-consuming procedures—this study develops a policy instrument analysis framework that integrates large language models (LLMs) and proposes a LLM-driven analytical workflow comprising six stages: case repository construction, policy instrument selection, content element generation, clause-level coding, reliability and validity testing, and quantitative analysis. Using governance texts on teachers’ ethical misconduct from 27 universities specializing in finance and economics as the empirical context, the study employed DeepSeek-R1 to identify policy instruments, classify content elements, perform clause-level coding, and conduct two-dimensional cross-tabulation analysis. The results indicate that these governance texts exhibit pronounced regulatory, procedural, and accountability-oriented characteristics, while also revealing a structural imbalance marked by strong front-end norm construction and relatively weak back-end remedial mechanisms. Overall, the proposed framework improves the efficiency and consistency of policy text analysis and provides a novel technical pathway for methodological innovation in education policy research.
The aim of the present study is to evaluate the impact of the elements of Information Systems (IS) Success Model i.e., information quality, service quality, and system quality on the learners’ satisfaction with moderating role of teacher in EdTech platforms. Primary data were collected through questionnaire method from 473 students of 5th to 12th standard who were availing services of EdTech platforms. Results of the study substantiated significant positive association between IS success model constructs and learners’ satisfaction. Likewise, moderating role of teachers has been instituted between DeLone and McLean IS Success Model constructs and learners’ satisfaction. Furthermore, results established that the impact of service quality and information quality on learners’ satisfaction is enhanced in the presence of teacher whereas impact of system quality is decreased in teacher’s presence. Present study makes unique addition to the sparse literature on user’s satisfaction in e-learning environment on EdTech platforms by reintroducing the posits of the DeLone and McLean IS Success Model, 2003 and building it on the premise that teacher plays a crucial role in affecting learner’s satisfaction with system, service, and information quality.
The rapid expansion of research on academic resilience in Indonesia has been driven by digitalization, hybrid learning, and demands for equitable quality. However, a systematic synthesis of intellectual structure, thematic evolution, collaboration networks, and scholarly impact remains absent. In this study, a comprehensive bibliometric and science mapping analysis of academic resilience research in the Indonesian context from 2000 to 2025 was conducted. Performance analysis and science mapping techniques—including co-word analysis, co-citation analysis, bibliographic coupling, thematic evolution mapping, and burst keyword detection—were integrated and visualized using VOSviewer. Records were retrieved through Publish or Perish based on Google Scholar. The findings reveal three major patterns. First, publication trends indicate a shift from predominantly psychological and pandemic-related online learning themes toward institutional and systemic concerns and culturally embedded educational practices. Second, five dominant thematic clusters were identified: individual capacities, social support, academic outcomes, institutional and learning environments, and Indonesian cultural–linguistic contexts. Third, scholarly influence is concentrated in review articles and pandemic-era empirical studies employing validated measurement scales and mechanism-based structural models. Cross-national comparative studies were found to enhance citation reach. Overall, the intellectual trajectory of academic resilience research in Indonesia is structured around a dominant explanatory pathway linking self-efficacy and support to resilience and subsequent outcomes. Substantive research gaps remain at the lecturer and organizational levels, as well as in the systematic integration of Islamic and Indonesian linguistic–cultural frameworks into resilience theory. These findings provide a systematic intellectual mapping of the field and offer a foundation for advancing contextually grounded and policy-relevant research agendas.
Proficiency in academic writing is widely recognized as a foundational competence for students enrolled in English-medium instruction programs; however, systematic evidence regarding the nature and distribution of writing difficulties across socio-academic groups remains limited. In the present study, linguistic error patterns in undergraduate academic writing were examined within an English-medium instruction context, with particular attention given to disciplinary background and gender as socio-educational variables. A total of 49 undergraduate students from the University of Dhaka participated in the study, and academic essays were collected as writing samples. A multi-stage analytical framework was employed, combining manual linguistic error analysis with quantitative statistical procedures conducted using SPSS (Version 25). Errors were categorized into four principal domains: grammatical errors, lexical errors, mechanical errors, and discourse-level errors. The distribution of errors revealed that grammatical errors constituted the most frequent category, whereas discourse-level errors occurred least frequently. Independent-samples t-tests were performed to examine differences across gender and disciplinary affiliation. No statistically significant differences were identified between male and female students in overall linguistic error production. In contrast, statistically significant differences were observed between disciplinary groups, with students from social science disciplines producing fewer linguistic errors than their counterparts from science disciplines. The results underscore the importance of discipline-sensitive writing instruction and targeted pedagogical interventions aimed at strengthening grammatical accuracy and genre-specific discourse competence. This study contributes to a more nuanced understanding of academic writing difficulties in English-medium instruction in higher education and provides an empirical foundation for curriculum design and academic writing support programs.
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