Abstract This study aims to examine the role of employing artificial intelligence (AI) technologies in enhancing the quality of the educational process in university-level health education institutions within the Yemeni context. A descriptive analytical study design was adopted. Data were collected via a questionnaire administered to a sample of 280 respondents, including faculty members and academic leaders from university health education institutions. The instrument consisted of AI technologies, the quality of the educational process, and the challenges and requirements for effective AI implementation. The findings revealed that the level of AI utilization was moderate, and the overall quality of the educational process was also moderate from the respondents’ perspective. The results further revealed a statistically significant positive correlation between the use of AI technologies and the quality of the educational process. Moreover, the results revealed that the most influential AI applications for enhancing educational quality included educational data analysis, support for academic decision-making, and personalized learning content. In contrast, the major challenges identified were weak technological infrastructure, insufficient training programs, and the absence of clear institutional policies. On the basis of the study findings, a proposed model for employing AI applications to increase the quality of the educational process was developed. This study represents a scientific and practical contribution to the field of artificial intelligence in education, as it addresses a contemporary issue and offers innovative, applicable solutions suitable for Arab and developing contexts, thereby contributing to the improvement of university health education quality and community service. Future research should adopt longitudinal and mixed-method designs across diverse geographical regions while examining student-centered perceptions and formulating robust institutional governance policies for ethical AI adoption in resource-constrained educational environments.
Artificial intelligence (AI) is reshaping higher education. It challenges assumptions about knowledge, expertise, and the role of academic institutions. While AI expands learning opportunities and access, it raises important questions about academic identity, educational values, and institutional responsibility. The challenge extends beyond technology adoption to the reexamination of higher education’s philosophical foundations as knowledge is co-generated by human and algorithmic agents. Institutions must therefore understand AI-powered higher education as a dynamic educational ecosystem rather than a set of isolated technological tools. This paper introduces the PIVOT Model in AI-Powered Higher Education, comprising Purpose, Identity, Values, Oversight, and Transformation, to guide institutional responses to AI-driven change. This framework positions AI as a catalyst that refocuses educational purpose on intellectual judgment and epistemic agency, reshapes academic identity through evolving roles of students and faculty, and reframes educational values by integrating technological fluency with human capabilities. It also emphasizes human-in-the-loop governance for responsible implementation and highlights AI as a driver of personalized educational transformation across teaching, learning, and assessment. Within the framework, Purpose prepares students for an AI-augmented workforce; Identity redefines human roles through AI partnership; Values keep AI human-centered throughout the academic journey; Oversight ensures human-in-the-loop governance; and Transformation creates a personalized, adaptive educational ecosystem. It offers an integrated lens for strategic academic transformation through coordinated alignment across pedagogy, assessment, governance, leadership, and institutional policy. PIVOT positions AI integration as a strategic institutional transformation that requires the coordinated redesign of educational systems, academic practices, and organizational culture.
Teacher burnout is multidimensional, yet many studies rely on global scores that may obscure domain-specific links with personal beliefs and appraised work conditions. Using cross-sectional survey data from 751 teachers in Türkiye (SEM analytic N = 740 after multivariate outlier screening), we tested a structural equation model guided by a resource-informed occupational stress perspective, in which four teacher self-efficacy facets were related to four dimensions of perceived work-related motivational conditions and four burnout dimensions. To support comparability, we examined measurement and structural equivalence across public versus private school ownership. In the structural model, social self-efficacy was positively associated with in-school factors, and professional self-efficacy was positively associated with out-of-school factors. Professional self-efficacy showed direct negative associations with job-related burnout and student alienation/depersonalization. Among perceived work-related motivational conditions, professional development and prestige was negatively associated with job-related burnout, while in-school factors were negatively associated with physical/emotional burnout and colleague/administrator alienation/depersonalization. The model explained 5–12
Abstract The paper discusses difficulties evinced by students, particularly novices, in studying programming. It focuses on the difficulties that arise from Discipline-Oriented Teaching (DOT) of a programming language, where the aim is not to learn the language for its own sake, but rather to provide limited knowledge of it necessary for carrying out domain-specific tasks. The paper begins with a discussion of the DOT of programming in general and the R programming language in particular, and examines scholarly research on several particular difficulties, to which DOT may lead, as it invites fragmented and shallow teaching, a fast pace, and parrot-fashion. It next presents the first systematic research on students’ difficulties with DOT of R. Carried out in a 14-week R course intended for undergraduates in a Psychology Department and which incorporates various aspects of DOT, the research is based on an analysis of replies obtained from weekly questionnaires. The questionnaires were completed by 63 students who participated in the course. It was found that the aforementioned teaching practices presented various obstacles to students’ successful learning, including forgetting previously taught material, fragmentary knowledge, difficulty in answering non-replication questions, and variation in solution methods. We suggest that the research results should be considered in designing programming courses based on DOT.
This study investigates the role of adult literacy programs in empowering women living in the slums of Bangalore. Addressing a significant research gap, it examines changes in participants’ awareness of rights, education, economic opportunities, and living standards. The study primarily employed a qualitative approach, supported by quantitative methods. Sample consist of 200 women across four slums. Data were collected using in-depth interviews, achievement tests, and focus group discussions. The findings indicate that adult literacy programs empower women by improving literacy, numeracy, confidence, and awareness of rights. Participants valued education more and became active in family and community life. However, challenges like inflexible timings, short duration, lack of materials, and limited family support hinder sustained participation. The findings highlight that effective implementation requires stronger community outreach, targeted information campaigns, simplifying enrolment procedures and enhancing institutional coordination to increase participation and strengthen programme delivery.
The rapid integration of artificial intelligence (AI) into higher education offers opportunities for personalized learning and academic support but may also evoke anxiety, technostress, uncertainty, and concerns about competence, integrity, and future professional roles. This narrative review examines the relationships among AI anxiety, confidence, self-efficacy, and student engagement. A structured search of PubMed, Scopus, Web of Science, and Google Scholar identified relevant literature published between 2015 and 2026. Empirical studies, theoretical papers, and reviews addressing emotional responses to AI, student engagement, motivation, self-efficacy, and learning behavior were synthesized thematically using Self-Determination Theory, Self-Efficacy Theory, and emotion-regulation perspectives. The literature generally associates AI anxiety with avoidance, cognitive burden, reduced perceived control, and reluctance to experiment, whereas confidence and self-efficacy are linked to persistence, exploration, and active engagement. These relationships appear reciprocal and context dependent, shaped by prior technological experience, AI literacy, discipline, identity, cultural narratives, institutional policy, and the type of AI application. Excessive confidence may also encourage overreliance and uncritical acceptance of AI outputs. The review proposes a provisional five-stage framework comprising curiosity, anxiety, experimentation, confidence, and flourishing. This framework is intended as a heuristic rather than a universal or empirically validated sequence. Educational responses should combine scaffolded practice, transparent policies, critical AI literacy, mentorship, reflective dialogue, and human oversight. Longitudinal, experimental, qualitative, and mixed-method research is needed to test the proposed mechanisms and identify effective interventions.
This study examined in-service mathematics teachers’ awareness, implementation, and support needs regarding Universal Design for Learning (UDL) in secondary schools in Enugu State, Nigeria. A convergent mixed-methods design combined survey and interview data to provide complementary quantitative and qualitative evidence. Data were collected from 105 in-service mathematics teachers through questionnaires and complemented by semi-structured interviews with five teachers. Quantitative data were analysed using descriptive statistics, while qualitative data were analysed using reflexive thematic analysis. The findings revealed low levels of formal awareness of UDL among participants (grand mean = 1.99). Nevertheless, teachers reported moderate use of instructional practices aligned with UDL principles (grand mean = 3.14), despite lacking explicit knowledge of the framework. Major implementation barriers included inadequate professional development, large class sizes, limited instructional resources, and insufficient institutional support. Teachers nevertheless expressed strong willingness to adopt UDL when supported through professional development, appropriate resources, and institutional commitment. The study contributes to the UDL literature by demonstrating that teachers may unknowingly employ UDL-aligned instructional practices despite limited formal awareness of the framework, conceptualised as implicit UDL practice. The findings emphasise the importance of equipping teachers with the knowledge, skills, and resources required for effective inclusive mathematics education.
This qualitative case study explores the execution of an Islamic-Iranian model of the effective teacher educator (ETE) and examines participants’ perceptions of its role in the holistic development of pre-service teachers’ personal, professional, and social skills. Conducted at a teacher education center in Tehran, the research used semi-structured interviews, focus groups, observations, and document analysis with five teacher educators and ten pre-service teachers. Participants reported that the model’s perceived effectiveness was embedded in teacher educators’ representation of the role of an integrated moral and instructional exemplar (Murabbi), who ‘live’ Islamic-Iranian principles and engage in ‘epistemic integration’ by weaving Qur’anic wisdom, Persian literary heritage, and modern pedagogy. According to participant accounts, this approach was associated with reported transformation in pre-service teachers, including stimulated personal identity and spiritual resilience (tazkiyat al-nafs), a value-laden professional repertoire oriented toward moral intentionality, and an activated social consciousness framed as a duty to the Ummah and the marginalized members (Mustaz’afin). Key supporting institutional practices included policy as a visionary scaffold, collaborative reflection sessions (Shura-e-Amuzeshi), and a reformed practicum as a moral apprenticeship. The study additionally identifies inherent tensions in negotiating tradition and modernity, which functioned as catalysts for reflective practice instead of weaknesses. Within the bounded context of this single-institution case study, participants perceived the Islamic-Iranian ETE model as a practical and transformative framework for teacher preparation. While findings are not generalizable, they offer a theoretically transferable illustration of how a value-based model may, from participants’ perspectives, bridge the policy-practice gap. This contributes an indigenous paradigm to the worldwide discourse on value-based teacher education, pending replication across diverse Iranian institutions.
Mathematical argumentation is increasingly recognized as a fundamental competency in mathematics education, yet no comprehensive synthesis of the instructional strategies employed to teach it has been developed to date. This systematic review aims to identify and synthesize the instructional strategies used in the teaching of mathematical argumentation at primary, secondary, and tertiary education levels, addressing the research question of what didactic strategies are used for this purpose. The review was conducted following PRISMA 2020 guidelines. A systematic search was performed in Scopus and supplemented by Google Scholar, covering the period from 2019 to 2025. After screening 138 records, 12 studies meeting the inclusion criteria were selected; categories were identified inductively through collaborative analysis carried out by both authors. Four main categories of instructional strategies emerged: technology-mediated approaches, including GeoGebra and Scratch; dialogic and debate-oriented models, such as DAIM, ACODESA, and peer feedback; historical and contextual approaches integrating the history of mathematics; and gamification and STEAM-based strategies. The most recurrent theoretical frameworks were the Toulmin model, socio-mathematical norms, and sociocultural perspectives. The evidence suggests improvements in argument quality and critical reasoning across the reviewed studies, and several interventions support a progression from empirically based arguments toward more deductive forms of reasoning, consistent with Duval’s distinction between argumentation and proof and Boero et al.’s construct of cognitive unity. Limitations include language bias, potential publication bias, and the absence of independent coding. The review emphasizes the need for longitudinal and cross-cultural research and offers insights for teacher education and curriculum design.
Integers are fundamental to mathematics. However, students still struggle with learning integers. Students' abilities and weaknesses can be detected through a series of tests. Cognitive Diagnostic Assessment (CDA) is a form of assessment that can provide detailed information about students' abilities and weaknesses, which teachers can use to improve their teaching. However, limited studies have developed CDA-based diagnostic instruments specifically for integer operations. Therefore, this study aims to develop valid and reliable CDA-based diagnostic test items using a development research approach on integer operations for seventh-grade students. The Generalized Deterministic Inputs, Noisy “And” Gate (G-DINA) model was employed due to its flexibility in accommodating various attribute interaction patterns. This study used the Tessmer model. The diagnostic test questions developed were 40, with three attributes: A1 (comparing integers), A2 (addition and subtraction of integers), and A3 (multiplication and division of integers). The research subjects were 113 seventh-grade students from three junior high schools in the city of Banda Aceh, Indonesia, selected from three schools categorized as low, medium, and high. Content validity was established through expert judgment based on the alignment between test items, cognitive attributes, and learning continuum indicators. Data from the trial were analyzed using the GDINA program in R. The findings showed that all items met validity and reliability criteria, with high classification reliability values for A1 (0.9961), A2 (0.9954), and A3 (0.9976). The diagnostic findings indicated variations in mastery patterns across attributes, with students experiencing difficulties in more complex integer operations that required prerequisite understanding. In addition, the items demonstrated appropriate diagnostic quality and difficulty in identifying students’ mastery of integer attributes. The developed instrument can be used to diagnose students’ cognitive strengths and weaknesses in integer operations and has the potential to support future adaptive and technology-assisted mathematics learning.
Ensuring the quality of teaching–learning and assessment processes is a fundamental responsibility of higher education institutions and a critical determinant of institutional effectiveness and graduate competence. This study examined the perceptions of internal stakeholders regarding teaching–learning and assessment practices in Ethiopian public higher education institutions (EHEIs), focusing on undergraduate students, graduate students, instructors, and university officials. A descriptive cross-sectional survey design was employed. Eight public universities were selected using stratified sampling, and a total of 1518 participants were proportionally drawn through multistage random sampling procedures. Data were collected using structured Likert-scale questionnaires measuring perceptions of teaching–learning and assessment practices. The data were analyzed using SPSS version 24. Descriptive statistics summarized participants’ perceptions, while independent-samples t-tests and one-way ANOVA with post hoc analyses were conducted to examine differences among stakeholder groups. The findings revealed that participants generally perceived the quality of teaching–learning and assessment practices positively across the sampled institutions. However, undergraduate students consistently reported less favorable perceptions than graduate students, instructors, and university officials. Independent-samples t-tests showed statistically significant differences between undergraduate and graduate students, whereas the perceptions of graduate students and instructors/officials were largely comparable. These findings suggest that undergraduate students experience teaching–learning and assessment practices differently from other internal stakeholders. The observed perceptual differences have important implications for quality enhancement in Ethiopian public universities. The relatively less positive perceptions of undergraduate students indicate the need for targeted interventions to improve their learning experiences. Specifically, universities should strengthen student-centered instructional practices, diversify assessment methods to better support learning, and expand continuous professional development for academic staff. More broadly, the study demonstrates the value of incorporating multiple stakeholder perspectives into institutional quality assurance systems, thereby contributing to a more comprehensive understanding of process-oriented quality in higher education and informing evidence-based quality improvement initiatives.
In mathematics teacher education, limited empirical evidence is available on how technology-enhanced Design Thinking (DT) training supports teachers’ instructional design competencies, and even fewer studies have examined the psychometric quality of performance-based criteria used to evaluate such training processes. This study aims to examine two complementary issues: first, the effect of technology-enhanced DT training on mathematics teachers’ DT competencies, and second, the validity and reliability of the evaluation criteria developed for the training process using the Many-Facet Rasch Measurement (MFRM). The study was conducted with 18 mathematics teachers using a single-group pretest–posttest design. Data were collected through a multidimensional DT skills scale and a training evaluation rubric, and were analyzed using paired-samples t-tests, content validity indices, and MFRM. The results showed a significant increase in teachers’ total DT scores from pretest to posttest, t(17) = 2.97, p = .01, with a moderate-to-large effect size, dz = 0.70. MFRM analysis demonstrated acceptable model–data fit, with high rater separation reliability (0.86) and moderate criterion separation reliability (0.62), while also revealing meaningful differences in rater severity and criterion difficulty. The findings suggest that technology-enhanced DT training can support mathematics teachers’ instructional design competencies; however, the results should be interpreted as preliminary due to the small sample size, single-group design, and absence of follow-up data.
This study examines factors influencing digital inequality among key stakeholders within Somalia’s digital ecosystem by developing and evaluating a multidimensional Digital Inequality Index (DII). Somalia, as a fragile state, faces persistent challenges related to digital infrastructure, affordability, and political instability, which may constrain equitable digital participation. A cross-sectional survey of 80 respondents from the telecommunications and academic sectors in Mogadishu was conducted to investigate digital access, usage behaviors, and perceived barriers. To extend the analysis beyond descriptive statistics, a Digital Inequality Index (DII) was developed by integrating digital literacy, affordability, and infrastructure dimensions. Among the surveyed respondents, the DII indicated notable differences across demographic subgroups. Participants aged 35 years and above recorded the lowest average DII score (0.51), while respondents with a Master’s degree or higher achieved the highest score (0.67) compared with those having secondary education (0.48). Regression analysis further indicated that higher educational attainment was positively associated with the use of advanced digital services, particularly e-government platforms (OR = 3.6, 95
Abstract Flipped classrooms provide opportunities for active learning but also require students to regulate preparation, practice, and assignment completion independently. This study examined longitudinal assignment-submission patterns in an undergraduate flipped programming course and investigated their correspondence with self-reported academic procrastination and academic achievement. An explanatory sequential mixed-methods design was employed with 45 s-year students enrolled in a compulsory Programming Languages I course. Assignment submission timestamps were collected through the learning management system over 12 weeks and analyzed using sequence dissimilarity analysis and hierarchical clustering. Academic procrastination was assessed with the Academic Procrastination Scale, and final examination scores represented academic achievement. Subsequently, semi-structured interviews with 13 students were analyzed using inductive thematic analysis to clarify the experiences underlying the observed patterns. The behavioral analysis identified two profiles: Procrastinators and Non-Procrastinators. Students in the Non-Procrastinator profile obtained significantly higher final examination scores than those in the Procrastinator profile. Psychometric analysis identified low, moderate, and high procrastination groups; students in the low-procrastination group outperformed those in the high-procrastination group. Comparisons between the behavioral and psychometric classifications showed some correspondence but did not fully overlap, indicating that self-reports and submission timestamps captured related yet distinct aspects of students’ procrastination tendencies and submission behavior. Qualitative findings further distinguished students who planned their work in advance from those who described last-minute completion as habitual. The study contributes a longitudinal, multi-source account of procrastination-related behavior and provides a basis for future experimental research on profile-sensitive support in flipped programming courses.
Academic resilience is a context-specific pattern of positive academic adaptation among students facing socioeconomic disadvantage. This study examined academic resilience in Türkiye using 2022 data from the Programme for International Student Assessment (PISA). Students in the weighted bottom quartile of the national Index of Economic, Social and Cultural Status distribution formed the analytic sample (n = 1885). For each of the ten PISA mathematics plausible values (PVs), students reaching the corresponding weighted national top quartile were classified as resilient, yielding 204 to 230 resilient students across PV-specific replications. The survey-aware workflow used student weights, school-grouped validation, training-only preprocessing, and minority-class threshold tuning. Weighted logistic regression, Radial Basis Function Support Vector Machine (RBF-SVM), ExtraTrees, Extreme Gradient Boosting (XGBoost), and CatBoost were benchmarked. Although the primary PV-aware comparison favoured CatBoost on Precision-Recall Area Under the Curve (PR-AUC), a three-repeat school-grouped sensitivity analysis found the highest mean PR-AUC for ExtraTrees (0.332, 95
Mathematics underpins all technical and vocational education and training (TVET) trade areas, particularly welding and fabrication. However, contextualising mathematical concepts within welding and fabrication remains challenging. To address this gap, this study investigated the potential of ChatGPT and Meta AI to assist mathematics teachers in generating contextualised mathematical examples and tasks for welding and fabrication courses. An exploratory case study design was employed. The relevant mathematical concepts were selected from Fabrication Mathematics I and II of the American Welding Society and the Mathematics Curriculum for Welders of Northeast Wisconsin Technical College. Two mathematics teachers used ChatGPT and Meta AI to generate examples and questions relating to various mathematical concepts taught to welding and fabrication students in second-cycle TVET institutions. Experts in welding and fabrication (WF) assessed the generated outputs, and the data were examined using content and thematic analyses. The findings revealed that both ChatGPT and Meta AI generated high-quality, well-structured and meaningful questions that demonstrated an understanding of how to contextualise mathematical content within WF courses. The generated questions were generally appropriate for the students’ academic levels and cognitive demands and were considered suitable for classroom instruction. The findings further indicated that the questions were relevant to WF courses, supported the application and transfer of mathematical knowledge, and addressed the mathematical competencies required in the WF trade area. Thus, when used appropriately and with expert validation, these AI tools may help teachers connect theoretical mathematics with practical vocational applications, thereby making mathematics instruction more relevant to students’ future careers.
The potential and challenges of using Generative artificial intelligence (GAI) in medical education are widely discussed, yet its use by medical students and faculty remains under-researched in the UK context. This exploratory sequential mixed-methods pilot study empirically investigates and compares student and faculty experiences and perceptions of using GAI in medical education. A questionnaire was developed based on focus groups and administered to undergraduate medical students and faculty within a UK medical school. Descriptive analysis and comparative analysis were used to quantify and compare student (n = 29) and faculty (n = 32) experiences and perceptions of GAI. Reflexive thematic analysis of open-ended question responses was undertaken to complement quantitative results. We found that GAI is being used for a variety of purposes by students and faculty in learning/teaching/assessment. However, both of them generally have relatively low self-confidence in GAI-related knowledge and skills. Faculty members tend to have stronger concerns of GAI limitations and its ethical challenges. Males are more confident in using GAI and have more positive attitudes towards GAI than females. The findings also provide preliminary evidence for the importance of open communication of GAI between students and faculty and the need for schools’, universities’, and national regulators’ strategic support to ensure faculty and student ethical and effective use of GAI and equitable access to GAI technology between subgroups. Our findings and the pilot tools can inform future research investigating the use of GAI in medical education in other institutional context or at a larger scale.
This study examines reported changes in digital privacy awareness as a component of early digital literacy following a structured parent–child digital privacy intervention in early childhood. Although educational technology research has increasingly emphasised young children’s digital experiences, limited attention has been given to how digital privacy may be fostered through family-based learning during the preschool years. This qualitative intervention study involved 19 preschool children (48–72 months) and their mothers. Semi-structured interviews were conducted with participating children and mothers before and immediately after a four-week intervention consisting of child-centred classroom activities, a family participation activity, and guidance materials for mothers. Data were analysed using qualitative content analysis. The findings indicate reported differences in children’s digital privacy perceptions, digital content evaluation, personal data awareness, and reported digital decision strategies following participation in the educational programme. Mothers also reported more reflective approaches to digital sharing, personal data protection, and the mediation of their children’s digital experiences. In several families, children’s emerging awareness appeared to encourage mothers to reconsider aspects of their own digital privacy practices, suggesting patterns consistent with reciprocal learning within family contexts. Rather than presenting reciprocal learning as an established mechanism, the study proposes the Reciprocal Digital Privacy Learning Model as a tentative interpretive framework grounded in participants’ reported experiences. The findings illustrate how family-based educational interventions may contribute to the co-construction of digital privacy awareness within early childhood while recognising that the reported changes reflect participants’ experiences within a single qualitative intervention context rather than causal intervention effects.
This study examines the relationship between transformational leadership and employee performance, with affective commitment as a mediating mechanism, among academic and administrative staff in public universities in Southwest Afghanistan. Grounded in Social Exchange Theory, the study explores how leadership behaviors influence employee attitudes and work outcomes in a resource-constrained higher education context. A convergent mixed-methods design was employed, combining survey data collected through purposive sampling with semi-structured interviews. Quantitative data were analyzed using correlation, regression, and mediation techniques, while qualitative data were examined through thematic analysis. The findings indicate that transformational leadership positively affects employee performance both directly and indirectly through affective commitment. Employees who perceive their leaders as supportive, inspirational, and fair are more likely to develop stronger emotional attachment to their institutions and achieve higher performance. Qualitative evidence further emphasizes the importance of communication, fairness, motivation, and institutional support. This study extends transformational leadership research to an underexplored higher education context and offers practical implications for strengthening leadership capacity and organizational effectiveness in public universities.
This quasi-experimental study examined the impact of a Brain-Based Learning Approach (BBLA) on reading attitude and self-efficacy among English as a Foreign Language (EFL) students at Jimma University, Ethiopia. A pretest-posttest control group design was implemented with 109 first-year social science students (experimental: n = 54; control: n = 55). The experimental group received a 16-week BBLA intervention incorporating kinesthetic, visualization, collaborative, and mindfulness strategies, while the control group received conventional instruction. Reading attitude and reading self-efficacy were measured using validated questionnaires. Multivariate analysis of variance (MANOVA) revealed significant multivariate effects favoring the experimental group (Wilks’ Λ = 0.160, F(2, 106) = 277.808, p < 0.001). Independent t-tests showed the experimental group significantly outperformed the control group in both reading attitude (t(107) = 10.54, p < 0.001, d = 1.55) and self-efficacy (t(107) = 15.67, p < 0.001, d = 2.30). Paired t-tests confirmed significant within-group improvements for the experimental group. Results indicate BBLA strategies effectively enhance EFL students’ reading motivation and confidence. Implications for curriculum, teacher training, institutional leadership, and national policy are discussed.
