This study examines AI-related technological proficiency among undergraduate students at the University of Casablanca and identifies the most informative indicators for prediction. Using a validated 30-item instrument covering AI applications, AI-related skills, and improvement strategies, data were collected from 600 students drawn from science and humanities programs. Overall proficiency was moderate: 63.3% of respondents met the predefined threshold, and significant group differences were observed by gender and academic specialization. For predictive modeling, correlation-based feature selection retained 17 high-value items. Two classifiers were then trained and evaluated using a 75/25 hold-out split, complemented by repeated stratified 10-fold cross-validation to assess stability. The Support Vector Classifier achieved 96.7% test accuracy with AUROC = 0.9666, while Gaussian Naïve Bayes reached 94.7% accuracy with AUROC = 0.9560; cross-validated estimates remained consistent with these results, supporting robustness. These findings indicate that a reduced set of questionnaire items can provide reliable estimates of students’ AI-related technological proficiency and can support scalable assessment and targeted interventions in higher education.

Exploration problem-based learning in mathematics learning in higher education: A bibliometric review

A scoping literature review of prompt engineering for bridging students AI literacy in higher education

Reducing carbon dioxide emissions (CO2e) is essential to achieving sustainable development objectives, safeguarding the environment, reducing the effects of climate change, and maintaining biodiversity for a future that is cleaner and more resilient. Nowadays, environmentalists also focus on how the environment reacts to society's increasing level of education. Increasing public awareness of environmental deterioration through environmental education, moral sermons, and higher tertiary enrollment can be a crucial policy in the fight against global warming, along with other measures to reduce CO2e. The effort to combat climate change necessitates improving energy efficiency (EE) and information and communication technology (ICT). Therefore, this study examines the impact of higher education (HED), EE and ICT on CO2e under the N-shaped EKC hypothesis. Using the panel data for five BRICS nations between 1991 and 2023, an empirical analysis is carried out, and the coefficients of the variables are estimated using the Second generation techniques (cross-sectional augmented distributed lag (CS-ARDL), Common Correlated Effects Mean Group (CCEMG) and Augmented mean group (AMG) approach. The estimates confirm the Inverted N-shaped EKC hypothesis between the GDP and CO2e. Moreover, the long-run estimates reveal that higher education, energy efficiency and ICT have negative effects on CO2e. BRICS countries should promote environmental education across all tiers, with an emphasis on conservation, climate change mitigation, and sustainable development, to help improve environmental awareness and literacy. Moreover, they should decouple energy use from economic growth to simultaneously achieve both economic and environmental goals, which can be facilitated by increasing ICT utilization, promoting higher tertiary enrollment, and improving energy efficiency. • This study examines the impact of Digitalization, Higher Education, and Energy Efficiency on environmental sustainability. • This study investigates the the N-Shaped Environmental Kuznets Curve in the BRICS economies. • This study utilizes the CS-ARDL, CCEMG and AMG approaches. • The finding shows there exist an Inverted N-shaped EKC hypothesis between the GDP and CO2 emissions. • The digitalization, energy efficiency, and higher education have negative effect on CO2 emissions.

The accelerated adoption of artificial intelligence (AI) in higher education has intensified expectations regarding instructional quality and learning effectiveness, yet empirical evidence on its pedagogical value remains contextually contingent. This study investigates students' perceptions of AI-enabled teaching in Kazakhstan, with a specific focus on perceived instructional quality dimensions and discipline-related skill development. Grounded in contemporary AI-supported learning literature, a structured self-administered questionnaire was developed and administered using a stratified random sampling design across higher education institutions. Instrument reliability and construct validity were established through Cronbach's alpha and the Rasch Rating Scale Model using a pilot sample representing 10% of respondents. Power analysis indicated a minimum requirement of 1504 observations; 2700 valid responses were ultimately analyzed. Data were processed using Microsoft Excel and RStudio, and partial least squares structural equation modeling (PLS-SEM) was employed to test seven hypothesized relationships within the proposed framework. The measurement model exhibited satisfactory reliability and validity, while the structural model showed low multicollinearity, strong explanatory and predictive power, and acceptable fit indices (SRMR, NFI, GoF). The findings reveal that usability, engagement, content quality, and accessibility of AI-based instruction significantly enhance student satisfaction, while instructional quality strongly predicts perceived skill acquisition, particularly in problem-solving, conceptual understanding, and technological competence. Conversely, perceived gains in interpersonal and diagnostic skills were comparatively weaker, and feedback-related pathways were not statistically significant, indicating limitations in current AI feedback mechanisms. The study offers robust perception-driven empirical evidence on the pedagogical implications of AI-integrated instruction in Kazakhstan's higher education system and provides actionable insights for evidence-based instructional design and AI-enabled teaching policy. • Power analysis established a minimum sample of 1504; the study analyzed 2700 valid student responses from Kazakhstan. • A large, demographically diverse dataset enhances the robustness and generalizability of findings on AI-enabled teaching. • PLS-SEM results demonstrate that usability, engagement, content quality, and accessibility significantly drive student satisfaction. • AI-supported instruction strongly predicts problem-solving, conceptual, and technological skill development, while feedback-related effects remain limited. • The findings provide actionable insights for higher education leaders and policymakers to optimize AI-driven teaching and digital strategies.

The swift rise of generative AI in higher education is transforming how students complete academic assignments. In Indonesia's higher education sector, where AI adoption is growing faster than formal guidance, understanding this transformation is particularly critical for safeguarding academic integrity and learning quality. This study seeks to (1) describe patterns of generative AI utilization, (2) explore student views and experiences, and (3) identify implications for higher education in Indonesia. The study employed an exploratory qualitative approach, involving 131 students from a university in Jakarta—encompassing undergraduate, master's, and doctorate programs—who composed reflective essays regarding their experiences with AI. Employing Saldaña's two-cycle coding method, three primary themes were identified: AI as an academic assistance tool, AI for academic skill development, and AI for learning effectiveness. Students predominantly used AI for rapid information retrieval and summarization, improving their academic writing (e.g., refining structure, clarity, and grammar), and supporting their study practices (e.g., generating practice quizzes and organizing study plans). Although AI provided enhanced efficiency and accessibility, apprehensions emerged regarding excessive dependence, diminished critical thinking, and ethical dilemmas pertaining to academic integrity. These findings underscore the significance of AI literacy initiatives to assist students in the responsible integration of AI tools while preserving cognitive engagement and originality. The study is confined to a single institution and depends on self-reported reflective essays, indicating the need for careful interpretation of the findings. As one of the first qualitative studies on this topic in Indonesia, this research addresses a critical empirical and policy gap by offering context-specific evidence on AI's educational role and by outlining a balanced integration pathway for Indonesian higher education institutions. Future research ought to investigate cross-institutional comparisons, the long-term effects of AI adoption, and educators' viewpoints regarding its function in academic settings.

Artificial intelligence literacy in higher education and implications for nursing education: A scoping review.

With the rapid integration of generative AI (GenAI) into higher education, concerns over cognitive offloading, overreliance, and diminished critical thinking underscore an urgent need to prioritize learning-to-learn (L2L) competencies. However, the lack of a clear and detailed conceptualization of L2L, a key 21st-century skill for lifelong learning, hinders interdisciplinary research and limits the development of informed, pedagogically sound AI applications. This paper presents a scoping review of L2L definitions within pedagogical and psychological literature, based on sources retrieved from ERIC, Scopus, and Web of Science. The review follows the PRISMA-ScR framework and identifies 21 relevant publications. We propose a novel three-layered framework organized by conceptual broadness: Dimensions (dispositions like cognitive and metacognitive skills), Processes (actionable activities such as self-regulation), and Tools (concrete strategies like retrieval practice), providing entry points for researchers from varying disciplines. We further highlight existing GenAI research efforts addressing these layers, outline current limitations, and propose directions for future exploration, with a focus on higher education. By unifying L2L theory with GenAI practice, this framework provides an actionable foundation for educational technologies in an AI-driven era. • Three-layer learning-to-learn (L2L) framework (Dimensions, Processes, Tools) to guide AI‑enhanced learning system design. • Synthesizes L2L definitions from 21 studies identified by a PRISMA-ScR review and offers actionable paradigms for teaching L2L, for example, using AI-supported systems. • Maps L2L components to GenAI application use cases in higher education. • Positions L2L as key to reducing GenAI overreliance and fostering learner agency.

Scaffolding critical thinking with generative AI: Design principles for integrating large language models in higher education

Beyond compliance: A transferable and scalable framework for introducing Equality, Diversity and Inclusion in higher education.

The rapid integration of Generative AI (GenAI) into higher education presents both opportunities and systemic challenges, particularly in domains where feedback is central to learning. This study investigates the capacity of a large language model to generate formative feedback for student-created UML diagrams in a university software engineering course. Across two cohorts (N = 262), AI-generated, teacher-generated, and no-feedback conditions were compared, analyzing student perceptions, learning outcomes, and grading reliability. Results show that while students rated GenAI feedback as beneficial and often comparable to human comments, teacher feedback remained more effective in supporting performance gains, especially in complex modeling tasks. Linguistic analysis further revealed that GenAI feedback was more repetitive and less pedagogically rich than human feedback. Beyond these course-level findings, the study highlights broader implications for higher education systems. GenAI feedback represents not just a pedagogical tool but a cognitive partner that can reshape assessment models, curriculum design, and faculty roles. Its scalable nature offers potential to democratize access to high-quality formative feedback, while also raising equity, accountability, and policy challenges at institutional and sectoral levels. By situating empirical results into this broader frame, the study argues that GenAI is catalyzing a paradigm shift toward new systems of learning, where feedback becomes systemic, scalable, and embedded in the very structure of higher education.

Creativity in the age of AI: Exploring moderated mediation relationships between AI use, AI dependence, and academic support in higher education.

Recent advances in large language models have revitalized research on automated essay evaluation, yet critical concerns remain regarding their reliability, validity, and interpretability. This study presents a comparative analysis of five LLMs (GPT-4.1, LLaMA 4 Maverick, Gemini 2.5 Flash, Claude Sonnet 4, and DeepSeek R1) in the assessment of long English essays authored by non-native speakers in higher education. The analysis draws on LLM-generated scores for 60 essays to examine (a) intra-model reliability across repeated scoring runs, (b) the degree of alignment between model outputs and expert human ratings, and (c) causal feature dependencies that clarify how linguistic characteristics influence model scoring behavior. Findings reveal substantial variation: some models achieved near-perfect reproducibility and strong alignment with human raters, whereas others displayed inconsistency, score compression, or systematic underestimation. Causal discovery analysis further uncovered distinct evaluative heuristics, with most models prioritizing lexical precision and fluency, while others emphasized syntactic complexity or cross-domain integration. Collectively, these results establish model-specific reliability profiles and application contexts, providing empirical benchmarks and practical guidance for the responsible use of LLMs in educational writing assessment.

This study examines the potential of Artificial Intelligence (AI) tools to promote global citizenship and awareness of the Sustainable Development Goals (SDGs) through cross-cultural collaboration programs in higher education. Employing a qualitative Delphi method, the research gathered insights from 15 expert faculty and instructors across diverse disciplines (education technology, humanities, social sciences, and IT). The analysis of Delphi rounds and expert interviews revealed several key findings. Experts widely recognize AI's capacity to foster intercultural dialogue and facilitate inclusive learning across cultures, as well as its ability to simulate SDG challenges to build empathy. The study also highlighted key areas where AI could be integrated. The top strategies identified were the use of AI in cross-cultural, project-based learning and the incorporation of AI tools into curricula focused on the United Nations' Sustainable Development Goals. However, significant ethical concerns, including algorithmic bias, digital equity, and the need for culturally responsive AI tools, were highlighted. Furthermore, faculty resistance, often stemming from insufficient training, emerged as a significant barrier to practical implementation.

Empowering university teachers in higher education: A generative AI-responsive competency framework

AIDS remains a major global public-health challenge, with persistent care-cascade gaps driving preventable deaths, especially in LMICs. In Brazil, uneven progress and late presentation reflect social and regional inequities. We used the national notifiable diseases system (SINAN) to profile AIDS at diagnosis and identify mortality risk factors. National retrospective cohort study using the SINAN from 2007 to 2022. We included all SINAN records of AIDS diagnoses in Brazil between 2007 and 2022. Extracted variables comprised demographics (age, sex, education, race), clinical data (AIDS-defining conditions, comorbidities), and outcomes (vital status, time to death). Annual trends and state-level patterns were described using time-series and spatial analyses. Independent mortality factors were identified with multivariable logistic regression. Kaplan-Meier curves compared survival for the most versus least prevalent conditions, with sub-analyses of frequent diseases. Among 400,509 patients, 17.2% died. Those who died were older, predominantly black and mixed-race, and had higher education. Seventeen variables were independently associated with mortality; the strongest were non-Hodgkin lymphoma (Burkitt/diffuse large B-cell; aOR: 3.16, 95%CI: 2.84-3.51), extrapulmonary cryptococcosis (2.96, 2.72-3.22), disseminated histoplasmosis (2.50, 2.25-2.79), Pneumocystis jirovecii pneumonia (2.25, 2.15-2.35), and central nervous system dysfunction (1.83, 1.75-1.91). Kaplan-Meier curves showed shorter survival for prevalent conditions, especially Pneumocystis pneumonia (p<0.001). Mortality was driven by AIDS-defining conditions, particularly non-Hodgkin lymphoma, extrapulmonary cryptococcosis, and disseminated histoplasmosis. Classic signs and symptoms (prolonged fever, weight loss, cytopenias) also predicted death. Early recognition and timely treatment of these high-risk presentations are essential in Brazil.

Moderating effects of educational inequality on education polygenic scores, attained education and dementia-risk relationships.

Previous research has analysed advanced practice nursing education and nurse educators' roles. However, limited attention has been paid, and a gap remains in understanding educators' experiences delivering the APN programme and their readiness to focus on their teaching roles. To enhance our understanding of nurse educators' experiences in becoming APN educators before and after participating in the APN educators' intensive course and additionally describe how participants evaluated this course. A pretest-posttest intervention study. A total of 19 APN educators from the Nordic and Baltic countries completed both pre- and post-surveys. Fourteen respondents answered the course evaluation questionnaire. This intensive course intervention appeared to increase APN educators' readiness and competence. Although the overall results were not statistically significant, the subgroup analyses highlighted that prior clinical experience, higher education and teaching experience influenced competency development. The course evaluation results indicate that the international intensive course was perceived as successful. This study provides new insights into the readiness and competencies of APN educators in the Nordic and Baltic countries. The research findings can contribute to understanding APN educators' teaching readiness and competence and offer directions for future development in education for APN educators.

The integrative literature review addresses the conceptualization and implementation of AI Literacy (AIL) in Higher Education (HE) by examining recent research literature. Through an analysis of publications (2021–2024), we explore (1) how AIL is defined and conceptualized in current research, particularly in HE, and how it can be delineated from related concepts such as Data Literacy, Media Literacy, and Computational Literacy; (2) how various definitions can be synthesized into a comprehensive working definition, and (3) how scientific insights can be effectively translated into educational practice. Our analysis identifies seven central dimensions of AIL: technical, applicational, critical thinking, ethical, social, integrational, and legal. These are synthesized in the AI Literacy Heptagon, deepening conceptual understanding and supporting the structured development of AIL in HE. The study aims to bridge the gap between theoretical AIL conceptualizations and the practical implementation in academic curricula. • Development of a structured seven-dimensional framework (Heptagon) for AI Literacy in Higher Education. • Proposal of a working definition to capture and synthesize both recurring and emerging themes in AI Literacy conceptualizations. • Identification and integration of underrepresented AIL dimensions in recent literature (e.g. integration skills and legal and regulatory knowledge), and delineation from Media, Computational and Data Literacy.

Adoption of AI-Supported gastronomy education in higher education: A mixed methods study based on the UTAUT model