Search Results (217)

The advancing fluency of large language models (LLMs) raises important questions about their ability to emulate complex human traits, including emotional expression and personality, across diverse linguistic and cultural contexts. This study investigates whether state-of-the-art LLMs can convincingly mimic emotional nuance in English and personality markers in Arabic, a critical under-resourced language with unique linguistic and cultural characteristics. We conduct two tasks across six models: Jais, Mistral, LLaMA, GPT-4o, Gemini, and DeepSeek. First, we evaluate whether machine classifiers can reliably distinguish between human-authored and AI-generated texts. Second, we assess the extent to which LLM-generated texts exhibit emotional or personality traits comparable to those of humans. Our results demonstrate that AI-generated texts are distinguishable from human-authored ones (F1 > 0.95), though classification performance deteriorates on paraphrased samples, indicating reliance on superficial stylistic cues. Emotion and personality classification experiments reveal significant generalization gaps: classifiers trained on human data perform poorly on AI-generated texts and vice versa, suggesting LLMs encode affective signals differently from humans. Importantly, augmenting training with AI-generated data enhances performance in the Arabic personality classification task, highlighting the potential of synthetic data to address challenges in under-resourced languages. Model-specific analyses show that GPT-4o and Gemini exhibit superior affective coherence, while LLaMA performs worse. Linguistic and psycholinguistic analyses reveal measurable divergences in tone, authenticity, and textual complexity between human and AI texts. These findings have significant implications for affective computing, authorship attribution, and responsible AI deployment, particularly within under-resourced language contexts where generative AI detection and alignment pose unique challenges. Full article

This study investigates the relationship between digital leadership dimensions and school effectiveness in a rural school district in South Africa. This study advances the research frontier, which is predominantly focused on urban and well-resourced environments, by providing actual information from rural schools where infrastructural and contextual obstacles remain significant. A quantitative study design was employed, utilizing a census survey of 107 educators from primary and secondary public schools in a rural area of the Western Cape Province. The Digital Leadership and School Effectiveness Questionnaire (DLSEQ) was used to collect data. It measures five dimensions of digital leadership: visionary leadership, digital learning culture, professional development, systemic improvement, and digital citizenship. Partial Least Squares Structural Equation Modelling (PLS-SEM) using SmartPLS 4 was employed to assess both measurement and structural models. The findings reveal that digital citizenship and professional development were significant positive predictors of school effectiveness, whereas digital learning culture showed a significant negative relationship. Visionary leadership and systemic improvement were not significant predictors. Collectively, the five digital leadership dimensions explained 81.9% of the variance in school effectiveness. The study contributes theoretically by integrating Transformational Leadership Theory and the Technology Acceptance Model within the context of rural education. Practical implications advocate for context-sensitive digital leadership strategies to enhance school effectiveness in under-resourced rural environments. Full article

Many South African schools face educational resource shortages that hinder effective teaching and learning. This study investigates a community-driven Open Educational Resources (OER) initiative implemented within a teacher education programme at a South African university, where Bachelor of Education (BEd) students (n = 400) from first to fourth year across the Vanderbijlpark and Potchefstroom campuses were engaged in designing sustainable Learning and Teaching Support Materials (LTSM) from recyclable materials. A purposively selected qualitative subsample (n = 53) participated in the study. Framed within an interpretivist and qualitative phenomenological design, data were collected through structured written reflections capturing participants’ experiences of the project. The findings show that creating OER from recyclable materials strengthened resourcefulness, collaboration, and awareness of educational inequality, while also encouraging more accessible and context-responsive teaching practices. The study contributes to understanding how Environmental, Social, and Governance (ESG)-aligned operational innovation can foster self-directed learning and sustainable teacher education in under-resourced contexts. Full article

Although South Africa has an extensive water infrastructure, it continues to face significant water scarcity due to its semi-arid climate, increasing urbanisation, ageing infrastructure, and pollution. These challenges, coupled with climate change and increasing water demand, have led to inefficiencies across the water value chain, particularly in rural areas. This review paper evaluates the current adoption of predictive analytics in South Africa’s water management system through a systematic literature review. It identifies the current applications, implementation gaps, and key system components that are suitable candidates to enhance efficiency, resource planning, and long-term sustainability in the sector. The findings show that while predictive models are being applied in urban systems for demand forecasting and proactive maintenance, only 15% of the reviewed studies address their actual adoption in rural or under-resourced contexts. This underscores the need for more inclusive development strategies to ensure equitable water service delivery. Although strides have been made in research and innovation, a major barrier is the slow transition from research to operational deployment, which hinders the full realisation of these technologies’ benefits that are essential for water supply sustainability and availability. Full article

Background/Objectives: Transitions to new vaccines or antigen schedules represent complex system changes requiring coordinated governance, reliable data systems, domestic financing, and multisectoral collaboration. In 2025, African countries were moving toward a switch from separate pentavalent and inactivated poliovirus vaccines to the combined hexavalent vaccine. This project report describes the Hexavalent Vaccine Switch Early Adopters Workshop in Dakar, Senegal, which included ten African countries, and its implications for future vaccine introductions. Methods: We conducted a practice analysis drawing on structured documentation of plenary presentations, country case studies, interactive problem-solving sessions, and national roadmap exercises. A thematic framework aligned to ten process points for the hexa switch guided synthesis. Results: Countries reported shared system vulnerabilities, including coexistence risks of legacy and new vaccine stocks, inconsistent data completeness, under-resourced vaccine safety surveillance, and financing uncertainties. Early adopter countries demonstrated operational feasibility, logistical efficiencies, and opportunities for reducing injection burden. Outputs included a Health System Adaptation Checklist, a Switch Risk Mitigation Catalog, and 12-month national roadmaps. Conclusions: Regional peer-learning mechanisms can accelerate decision-making, improve operational quality, and strengthen accountability for vaccine introductions. Structured cross-country collaborations can transform a product switch into a scalable system-strengthening opportunity. Full article

This qualitative review of the literature explores current conversations about the impact of Artificial Intelligence (AI) on teacher education in general and pre-service teachers in particular. Recent advances in AI are beginning to influence teacher education, where curricula, practicum, and school field experience now incorporate AI in curriculum-based instruction and as a context for teaching digital literacy, not as an isolated tool. Researchers regularly situate these shifts alongside broader educational practices and policy. There is also substantial literature dealing with pressing ethical and practical questions such as data privacy and algorithmic bias, equitable access to technology, and the challenges experienced by under-resourced schools. Together, these studies indicate that teachers are redefining and reconfiguring both their own teaching and teacher education, enabled by AI in new, more flexible and responsive ways. Within this shifting paradigm, pre-service and in-service teachers are not conceived as mere end-users but as reflective practitioners who take up such tools, critically question their ramifications, and, sometimes, lead the way in utilizing AI in educational practice, including mainly pedagogical practices. To explain the shared components identified in the present review, this paper offers a post hoc conceptual synthesis of eight recurring dimensions of sustainable AI integration in teacher education. Full article

Organized after-school activities can play a vital role in supporting historically marginalized youth in science, technology, engineering, and mathematics (STEM), yet less is known about how culturally responsive practices—which are practices that integrate youths’ cultural backgrounds and lived experiences into learning—are enacted in math-focused learning spaces. Drawing on empowerment theory and critical youth empowerment frameworks, this qualitative study examines how culturally responsive practices foster empowerment among middle school students participating in a university-based after-school math enrichment program. Ninety-two students (M_age = 12.26 years; 47% girls; 86% Latine) from three under-resourced schools in Southern California participated in semi-structured interviews about moments when they felt empowered and what contributed to those experiences. Thematic analysis revealed that all four domains of culturally responsive practices helped promote empowerment: structured opportunities for contribution and leadership, caring relationships, cultural affirmation, and efforts to make real-world connections. In particular, students most frequently described structured opportunities for contribution and leadership, practices that centered their knowledge and voices, and relational climates characterized by care and high expectations. The findings suggest that in after-school STEM contexts, empowerment does not arise as an isolated individual trait but is part of a relational and context-dependent process that is supported by culturally responsive practices. These findings highlight how intentional, culturally responsive program design can advance both youth empowerment and equity-oriented STEM education. Full article

Preserving ancient languages is essential for understanding the cultural and linguistic heritage of humanity. Old English, however, remains critically under-resourced, which limits its accessibility to modern natural language processing (NLP) techniques. We present a scalable framework that uses advanced large language models (LLMs) to generate high-quality Old English texts to address this gap. In this study, we specifically employ state-of-the-art models, including Llama-3.1-8B and Mistral-7B, as our foundation models, which are then adapted to the unique characteristics of Old English. Our approach combines parameter-efficient fine-tuning (Low-Rank Adaptation (LoRA)), data augmentation via back-translation, and a dual-agent pipeline that separates content generation (in English) and translation (into Old English). Evaluation with automated metrics (BLEU, METEOR, and CHRF) shows improvements over baseline models, with BLEU scores increasing from 26 to over 65 for English-to-Old English translation. Expert human assessment confirms high grammatical accuracy and stylistic fidelity in the generated texts, with average scores of 9.0/10 for inflection and word order, 9.1/10 for lexical authenticity, and 7.8 for semantic coherence. These results demonstrate that the framework can reliably expand limited historical corpora while maintaining linguistic integrity, with immediate practical applications in digital humanities research, computational philology, and the development of educational resources for Old English study. Beyond expanding the Old English corpus, our method offers a practical blueprint for revitalizing other endangered languages, thus linking AI innovation with the goals of cultural preservation. Full article

Background: Malaria remains hyperendemic in the Democratic Republic of the Congo, while arboviral infections are increasingly reported but remain under-surveilled, particularly in remote regions. Overlapping ecological niches and non-specific clinical presentations complicate case management and surveillance. Methods: A cross-sectional door-to-door survey was conducted in December 2023 in Inkanamongo village (Lokolia Health Area, Boende Health Zone, Tshuapa Province). Blood samples were collected from 379 adults; malaria infection was assessed by using HRP2-based rapid diagnostic tests, and arboviral IgG antibodies were measured on dried blood spots using Luminex^® multiplex immunoassay. Sociodemographic data were collected via standardized questionnaires. Results: Malaria prevalence was 51.7% (95%CI: 46.7–56.7). Overall arboviral seroprevalence reached 78.4% (95%CI: 73.1–81.5), dominated by O’nyong-nyong virus, 42.8% (95%CI: 37.6–47.5), Rift Valley fever virus, 32.0% (95%CI: 26.9–36.2), and chikungunya virus, 23.4% (95%CI: 19.0–27.4). Concurrent malaria infection and arboviral exposure were observed in 40.4% (95%CI: 35.6–45.4) of participants. No sociodemographic factors were significantly associated with co-exposure in the multivariable analysis. Conclusions: Substantial co-exposure of malaria and multiple arboviruses occurs in this remote Congo Basin setting. Integrated surveillance and improved diagnostics are urgently needed to guide febrile illness management and preparedness in under-resourced regions. Full article

This study addresses national shortages in school-based mental health (SBMH) providers, particularly in rural and high-needs areas, by examining two innovative training models designed to expand the school psychology workforce. The Grow Your Own (GYO) program respecializes practicing educators in rural communities to become school psychologists, while the Dual-Credentialing Clinical Training (DCT) model integrates school psychology training with supervised clinical experiences, leading toward educational certification and state mental health licensure. Program evaluation data were used to assess early implementation, feasibility, and success of both programs. In the GYO program, nine educators completed training, with eight employed in rural schools one to two years post-graduation, and average supervisor ratings meeting or exceeding the program’s competency expectations across all ten domains. In the DCT program, five trainees completed internship, four earned provisional mental health licenses, two progressed to independent licensure, and four became certified school psychologists. Together, findings indicate that place-based respecialization can strengthen rural retention, while dual-credentialing can expand clinical capacity and funding flexibility, creating complementary training models to help grow the SBMH workforce. Continued scaling and evaluation may enhance access to comprehensive SBMH services for students in under-resourced settings. Full article

Digitalisation continues to shape debates in mathematics education, yet its classroom enactment remains uneven, particularly in rural and under-resourced school contexts. This study examined how mathematics teachers perceive and experience digitalisation, focusing on digital competence, barriers to integration, and readiness to adopt technology. Guided by the Technological Pedagogical Content Knowledge (TPACK) framework, the study employed a qualitative design based on interviews with practising teachers. The findings show that digitalisation was constrained by limited awareness of mathematics-specific digital tools, uneven confidence and competence in their use, and structural barriers such as inadequate infrastructure, poor connectivity, and restricted access to devices. Although some teachers recognised the value of digital tools and expressed willingness to use them, this did not consistently translate into meaningful classroom practice. The study argues that digitalisation in mathematics education extends beyond the availability of technology and requires teacher competence, pedagogical readiness, institutional support, and equitable resourcing. Overall, the findings show that digitalisation in the participating rural schools remains emergent, uneven, and shaped by both teacher-related and structural conditions. Full article

Background/Objectives: Nutritional vulnerability (NV) describes the interaction of diet quality, access to food, health status and socioeconomic factors and may differ between neighborhoods. Nevertheless, there is still a limited amount of evidence regarding local NV variations in contrasting resource landscapes. The purpose of this study was to operationalize NV in Windham, Connecticut and conduct an analysis of its spatial distribution and the differences between neighborhoods for NV and specifically diet quality. Methods: NV was measured with four indicators, including two diet quality measures (liking-based DQI and short food frequency-based sHEI), food security, obesity, and SNAP participation. Areas of vulnerable concentration were determined through spatial mapping. Indicators related to each other were measured by Spearman correlation. To compare the contrasting neighborhoods (resource-dense vs. resource-limited), contextual differences were studied and differences in NV indicators, sociodemographic and movement factors were compared with the help of chi-square tests. Diet quality measures were jointly examined for concordance (both measures low or high) and discordance. Results: Area-level comparisons showed significant differences in mobility-related and sociodemographic characteristics, including vehicle access and education level (p < 0.05). High diet quality (measure concordance) was reported by individuals living in high-resourced regions; low diet quality (measure concordance) by individuals in low-resourced regions. Conclusions: The NV Map illustrated focal patterns of vulnerability determined by the interplay of sociodemographic disadvantage and mobility-related limitations and not by distance to food resources. These results give practical spatial data to promote specific nutrition and resource intervention. Full article

Background: Adults with disabilities living in low-resource communities experience persistent inequities in access to healthcare, mental health services, and community participation. However, qualitative data capturing lived experiences in the Deep South remain limited. This study aimed to identify priority needs among adults with mobility disabilities residing in economically distressed communities near Birmingham, Alabama, to inform future telehealth programming. Methods: Fifteen adults (mean age = 60 ± 10 years), predominantly African American and female, completed semi-structured phone interviews exploring basic needs, neighborhood accessibility, health priorities, and perceived supports. Interviews were audio-recorded, transcribed verbatim, and analyzed using Braun and Clarke’s six-phase thematic analysis. Results: Five themes emerged: (1) seeking stability amid severe mental health strain and inadequate supports; (2) constrained food environments shaped by cost, location, and safety; (3) feeling forgotten: systemic neglect and restricted participation in community life; (4) physical health deprioritized by competing needs and structural barriers; and (5) remote support as a viable but unrealized option. Participants described how safety concerns, transportation barriers, and rising food costs constrained daily functioning, while unmet mental health needs compounded isolation. Despite widespread cardiometabolic disease, immediate needs related to mental health, food, and housing consistently superseded physical health. Mental health support was identified as the most feasible area for remote delivery, though poor awareness of available resources limited engagement with any service model. Conclusions: Findings demonstrate that disability-related disparities in low-resource communities are driven largely by structural and environmental factors rather than individual choice. Telehealth and mobile-based services may provide a feasible access strategy for mental health and supportive care in under-resourced settings, particularly when integrated with broader community supports. Addressing foundational needs is essential for advancing health equity among people with disabilities in the Southeast. Full article

Background/Context: Creating instructional materials that authentically meet the needs of marginalized learner groups such as student veterans, multilingual adult learners, and first-generation doctoral students demands consistent application of Universal Design for Learning (UDL) principles coupled with meaningful content expertise about those learners’ traits, access needs, and lived experiences. Faculty at teaching-intensive institutions face persistent constraints of time, knowledge, and course load that make systematic UDL implementation difficult. Objective: This practitioner-scholar case study examines whether HAIST-structured agentic LLM-assisted instructional design can produce UDL-aligned materials for student veterans and multilingual learners at a quality level and time frame realistic for under-resourced faculty. Methodology: Drawing from the Human-AI Symbiotic Theory (HAIST) and UDL guidelines, we document four AI-assisted cycles of instructional design at a Hispanic-Serving Institution. Outcomes related to UDL alignment were measured using a rubric adapted from CAST Guidelines 2.2. Results: Across four materials, initial AI generation averaged 61.4% UDL alignment (SD = 8.7%); following iterative calibration, this rose to 84.2% (SD = 5.3%). The largest gains occurred in the Engagement category. Conclusions: These descriptive findings, interpreted as exploratory rather than inferential given the single-site case study design and n = 4 materials, suggest that HAIST-structured AI-assisted design has the potential to produce accessible materials for underserved learner populations in time frames feasible for working faculty. Learner outcome data were not collected in this study; future quasi-experimental work is needed to assess the effectiveness of these materials with target learner populations. Full article

Equity in precision cancer health, defined here as equitable access to inclusive cancer risk assessment, cancer risk reduction/management, and risk-appropriate cancer healthcare from prevention through survivorship, is critical for addressing broader population cancer disparities. Specifically, we describe the impact of the Illinois Cancer Health Equity Research (I-CHER) Center on precision cancer health equity, including how the Center cumulatively served >10,000 residents from under-resourced communities; disseminated findings to >300 members of the local cancer health disparities workforce; and translated scientific solutions into sustained clinical practice and two state laws. The objective of this case report is to describe the I-CHER Center’s multisectoral structure; participatory administrative processes for research; early implementation challenges and tensions across sectors; and solutions that contributed to early center-wide successes. This case report offers one example of the administrative infrastructure needed for advancing scientific solutions in precision cancer health equity within a Minority Serving Institution (MSI) and its internal federally qualified health center (FQHC). Full article