Search Results (3,153)

Artificial intelligence (AI) is increasingly being integrated into higher education. Among these innovations, AI teaching assistants have emerged as tools that can provide immediate academic support, personalized feedback, and improved access to learning resources. Despite the growing adoption, limited research has explored students’ knowledge, attitudes, and acceptance of AI teaching assistants in African higher education contexts. The Technology Acceptance Model was adopted as a theoretical lens to explore South African university students’ Knowledge, Attitudes and Acceptance of AI teaching assistants in a clinical learning environment. A qualitative study design within an interpretivist paradigm was employed. Data were collected through semi-structured interviews with six undergraduate students who had experienced both traditional teaching approaches and AI-assisted learning. The data were analysed using thematic analysis. The findings revealed three key themes: students’ understanding of AI teaching assistants, attitudes toward AI-assisted learning, and acceptance and concerns regarding AI in clinical education. The results indicate that students generally demonstrate positive attitudes toward AI teaching assistants and recognize their usefulness for supporting independent learning. However, participants also expressed concerns regarding the accuracy of AI-generated information and emphasized the continued importance of human educators in clinical training. The study contributes context-specific insights into technology acceptance in African higher education, highlighting how perceived usefulness may remain strong even in resource-constrained environments. Full article

Engineering computer graphics education frequently exhibits a gap between procedural CAD software (e.g. CATIA 2022) training and the strategic engineering reasoning required by industrial practice. This paper documents a holistic redesign of two advanced CAD courses. The study is framed within the Scholarship of Teaching and Learning (SoTL) tradition as a practitioner-led reflective case study. The redesign integrates four pedagogical mechanisms within an enterprise-CAD context: authentic problem-based learning, dual-layered asynchronous video scaffolding, software-agnostic heuristics (including pre-modelling cognitive mapping), and cognitive apprenticeship. The analysis triangulates three institutional data sources: quantitative Course Experience Survey indicators, qualitative student response themes, and twelve consecutive years of cohort-level academic performance records (2013–2024). The 2022 intervention iteration coincided with a marked elevation in academic performance. Grades reached approximately two standard deviations above the historical baseline. Concurrently, qualitative themes highlighted perceived industrial relevance and platform-portable confidence. However, performance in the post-intervention iterations (2023 and 2024) partially regressed. While scores remained above the historical mean, they did not sustain the 2022 peak. This pattern indicates partial sustainment, rather than evidence of a stable or definitive sustained pedagogical effect. This case is reported as descriptive rather than inferential. While the observed patterns align strongly with theoretical predictions, they do not establish definitive causal effects. Ultimately, the primary contribution of this study lies in documenting the integrated operationalization of these four mechanisms. Furthermore, it highlights longitudinal pedagogical sustainability as a critical, under-examined dimension that single-iteration evidence systematically obscures. Full article

This paper explores the potential for aligning theoretical approaches and good practices for intersectional approaches to recruitment and career development in academia, focusing on a European university alliance comprising eight institutions. The study applies a participatory approach that includes comparative analysis and stakeholder engagement to assess how institutional practices can become more inclusive. The findings highlight structural barriers, including entrenched notions of meritocracy and inadequate legal and procedural frameworks. Current strategies often juxtapose inequalities rather than addressing their intersections, resulting in approaches remaining siloed. Based on a reflexive case study, the paper identifies critical factors such as the need for formalised procedures, training and financial investment to effectively operationalise intersectional frameworks. It emphasises the need for tailored approaches that take into account the diversity of institutional and legal contexts and enable more inclusive academic policies and services. Together, these efforts aim to address structural inequalities and create sustainable practises that support the professional development and mobility of marginalised groups in academia, responding to the persistent gaps between policy commitments to intersectionality and their practical implementation within higher education institutions. Full article

This study emerges in Chile within the framework of an academic and political debate on inclusion, focused mainly on quantitative studies. Little is known about how the population and social groups give meaning to the experience of inclusion and exclusion from their subjectivity and sociocultural contexts. Adopting a phenomenological and social representation approach, the research explores the perspectives of thirty individuals with disabilities across eleven cities in the Valparaíso Region. Unveiling their narratives, the study identifies employment, participation, and recognition of identity as pivotal to inclusion. Yet, predominant themes center around exclusion, stemming from perceived limited healthcare access, discrimination, job instability, state neglect, and universal accessibility deficits. Findings underscore a persistent charitable view of disability, perpetuating inequality across various dimensions. This study illuminates the nuanced meanings and experiences shaping social inclusion and exclusion in the region, contributing valuable insights to its broader discourse. Full article

This article analyses trading-performance patterns in a stock market simulation conducted with 134 second-year students at the University of Mons (Belgium) on 11 December 2025. Participants had a virtual capital of 100,000 euros and were free to trade CAC 40 securities without any restrictions on the number or volume of transactions. An academic incentive scheme, combining a participation bonus and bonuses for the three best portfolios, created a tournament-style environment with continuous ranking feedback. This feature is considered as part of the experimental context rather than as a separately identified causal mechanism. We estimate a quadratic model linking performance to activity, measured by the number of mean-centered transactions to reduce the collinearity between the first-degree term and its square, and control exposure via the average percentage of cash in the portfolio, portfolio variability (measured as the standard deviation of portfolio value) and the average trade size. Breusch–Pagan and White tests indicate heteroscedasticity, justifying a robust inference. The results highlight a convex relationship between activity and performance: the marginal association is initially negative but becomes positive above a model-implied upper-tail level corresponding to approximately 46 transactions. This value should not be interpreted as a behavioral level or as a trading rule. The percentage of cash in the portfolio and the average trade size are negatively associated with performance, while the portfolio variability does not show a statistically significant association with performance. Overall, the results indicate heterogeneous trading patterns rather than a single activity–performance profile. Full article

Higher education policy on generative artificial intelligence has developed rapidly, yet much of this development remains stronger on governance, permission, disclosure, and assurance than on pedagogy. Universities increasingly move beyond blanket prohibition by distinguishing between restricted and permitted contexts, requiring acknowledgement of tool use, and introducing verification mechanisms to protect authorship and understanding. However, publicly visible institutional approaches appear less developed in providing structured, student-facing workflows that guide responsible AI engagement during assessment completion. This article, informed by a bounded qualitative document analysis, uses the term pedagogical middle layer to describe the process guidance needed between institutional permission settings and academic-integrity or misconduct procedures. Drawing on recent literature and a purposive scan of selected publicly available university policy and guidance documents, the paper argues that current public-facing models are often effective at defining boundaries but less explicit in guiding disciplined, transparent, and defensible forms of human–AI collaboration. In response, the paper presents the Structured AI-Guided Education Assessment Policy (SAGE-AP) as a theoretically grounded policy proposal for AI-assisted assessment, rather than as an empirically validated policy intervention. SAGE-AP frames assessment as a staged process in which students begin from their own understanding, engage with AI critically, document evaluative decisions, refine outputs responsibly, and defend the reasoning represented in the final submission. The paper contributes to institutional policy development by clarifying how permission settings may be complemented by pedagogical process guidance in the generative AI era. Full article

Background/Objectives: Higher education requires students to communicate in complex academic and social contexts, including oral presentations, group work, help-seeking, assessment, and peer interaction. For students with developmental communication disorders, and communication-related developmental profiles, these demands may create hidden participation vulnerabilities that affect mental health, academic engagement, and belonging. This scoping review mapped empirical evidence among tertiary students, focusing on mental health, academic participation, social belonging, institutional support, and contextual influences. Methods: A scoping review was conducted in accordance with PRISMA-ScR guidance. Five databases, PubMed, PsycINFO, CINAHL, Scopus, and Web of Science, were searched for English-language, peer-reviewed empirical studies published from 2000 onwards. Eligible studies involved university, college, or tertiary students with developmental speech, language, fluency, pragmatic communication, or communication-related developmental profiles, who reported at least one mental health, academic, or social participation outcome. Data were charted and synthesised thematically, with methodological quality appraised using CASP-informed criteria. Results: Twenty-one studies were included. Evidence was strongest for stuttering and fluency-related participation, while research on developmental language disorder, speech sound disorder, pragmatic language impairment, cluttering, and mixed communication profiles was limited. Across studies, communication needs intersected with anxiety, depression, stress, self-efficacy, oral assessment, help-seeking, disclosure, stigma, accommodation access, and belonging. Key limitations included reliance on self-report, cross-sectional or retrospective designs, inconsistent diagnostic confirmation, and limited evidence for intervention. Conclusions: The available evidence suggests that developmental communication disorders and communication-related developmental profiles can function as hidden participation vulnerabilities in higher education. These vulnerabilities are shaped by students’ communication profiles and by communication-intensive university environments. Universities may therefore need communication-accessible teaching, flexible assessment, visible support pathways, and coordinated support across disability services, counselling, academic support, and speech–language pathology. Full article

This study examines complex thinking as a higher-order cognitive competence in international graduate education. Drawing on Edgar Morin’s theoretical perspective, it analyzes how master’s students perceive this competence through four interrelated dimensions: systemic, scientific, critical, and innovative thinking. A total of 491 international students from a graduate university in Japan participated in the study. Using a quantitative, cross-sectional design, data were collected with the validated eComplexity instrument and analyzed through PERMANOVA with 999 permutations. The analysis examined differences in self-perceived complex thinking by sex, academic field, nationality, and academic semester. Results showed moderately high levels of self-perceived complex thinking across the sample, with systemic and critical thinking emerging as the strongest dimensions. Significant differences were found by nationality and academic semester, while no significant differences were observed by sex or academic field. These findings suggest that students’ perceptions of complex thinking are associated with cultural and academic trajectories, although the cross-sectional and self-report design requires cautious interpretation. The study contributes to competence-based graduate education by showing that complex thinking can be examined as a multidimensional and context-sensitive form of perceived cognitive development. Educational implications are discussed in relation to curriculum design, intercultural learning, global citizenship, and inclusion in international master’s programs. Full article

Concussions exert a massive cost on our economic and healthcare systems. Many of the most commonly employed neurocognitive measures in concussion assessment have been shown to be psychometrically problematic. Additionally, norms are established from largely male populations. The present study investigates the use of a validated and reliable measure of concussion sequelae, the mobile half-version of the Connors Continuous Performance Test 3rd Edition (CCPT-3), on a representative population to study the influence of sex and age on normative values collected at baseline. Baseline data were analyzed from 71,976 participants across a wide range of academic and athletic contexts, as well as healthcare settings. Multiple regressions examined the influence of sex as a function of age in different developmental groups: children, adolescents, young adults, adults, and older adults. Sex effects emerged during childhood, peaked during adolescence, and decreased in adulthood. Females showed better accuracy (fewer commission and omission errors), whereas males had faster response speeds (hit-rate RT). Effect sizes were generally in the small to very small range (sex effect sizes ranged from Cohen’s d = 0.02 to 0.39). The findings highlight the importance of accounting for sex and age in cognitive test performance and underscore the impact of correcting for even small effects when working with large samples. Full article

Higher-education institutions increasingly use AI-enabled chatbots, personalised communication, recommendation systems, and predictive information services in academic marketing. Adoption of these systems depends not only on technical availability, but also on institutional trust, emotional engagement, and skepticism regarding the reliability, transparency, and autonomy implications of AI. This study examines the Trust-Tech Nexus framework using stakeholder survey data collected at MIT Art, Design and Technology University, Pune, India (N = 300). The analysis combines psychometric validation, WLSMV confirmatory factor analysis for ordered indicators, and cross-validated predictive modelling. Four three-item constructs were measured with five-point Likert indicators, as follows: AI Adoption, Institutional Trust, Emotional Engagement, and AI Skepticism. Reliability and convergent validity were acceptable, and the WLSMV CFA showed strong practical fit (CFI = 0.991, TLI = 0.988, RMSEA = 0.040, SRMR = 0.039). Discriminant validity was supported by HTMT and Fornell–Larcker evidence, while Harman’s single-factor result was treated only as an initial diagnostic. Construct-only ridge regression produced positive out-of-sample predictive evidence (CV R-squared = 0.352; RMSE = 0.642; MAE = 0.501). Exploratory classification results were moderate and are interpreted only as supplementary segmentation evidence because the binary targets were derived from the AI Adoption composite. The study supports a validated four-construct measurement structure and moderate predictive association in one institutional context, while avoiding causal claims. Full article

Background: Despite being considered a country with a larger health workforce in Africa, the South African health workforce continues to experience shortages and a maldistribution of health workers across regions and sectors. Current projections suggest that the workforce is expected to decline further, especially among doctors, nurses and midwives, in large part, due to attrition—which could compromise the delivery of primary health and maternity services. These health workforce shortages and uneven distribution threaten the sustainability and effectiveness of health services in South Africa and drives the need to investigate the factors that may be influencing career choice and change decisions among health professionals in South Africa. Methods: A qualitative exploratory study, making use of purposive sampling and semi-structured interviews, was conducted to investigate the factors influencing career choice and change decisions among health professionals in South Africa. The participants were qualified health professionals in the fields of medicine, nutrition, pharmacy, nursing, and psychology working in the private, public, and academic sectors. Data was collected until saturation was achieved and then thematically analyzed using MAXQDA 24. Results: A total of 10 participants made up of three males and seven females were interviewed. These participants worked in different employment sectors with some having dual roles in private practice, public sector, and academia. The analysis revealed three major themes that capture the nature of and factors influencing career choice and career changes occurring in South Africa. The first theme related to factors influencing career choice (including altruism, family influence, personal experiences, financial/job security, academic achievement, career guidance, and opportunity for change). The second theme focused on career change dynamics (nature of career changes and career transitions occurring in the form of specialization, switching health professions, exiting health professions, adding non-health interests, and shifting focus areas). The third theme revealed factors influencing career change. These were categorized into personal and individual factors, workplace or job-specific factors, and administrative factors. This study has contributed to understanding the career choices and career changes taking place within the health professions in South Africa. It has also revealed a need for reforms in policy and practice for the current health professionals who have no intention of changing their careers while highlighting implications for future training of health professionals. Also, addressing the challenges of poor working conditions, lack of support, unemployment and placement delays, and other administrative barriers will help mitigate some of the issues leading to health workforce shortages and inequities in the South African context. Conclusions: The strongest motivator for choosing a career in health professions is the desire to care for others, while retention of the health workforce is challenged by personal, workplace, and administrative factors. Enhancing workplace conditions and support systems, implementing policy reforms, and minimizing administrative barriers is essential for achieving universal health coverage and sustaining a resilient health workforce in South Africa. Full article

In the current context, where sustainability has become a global imperative, emerging markets have increasingly incorporated green finance as a strategic pillar to foster long-term growth and stability. This study examines the evolution, trends, and key challenges of sustainable investment in emerging economies, with a particular focus on the integration of environmental, social, and governance (ESG) criteria. A systematic literature review was conducted using Scopus and Web of Science, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol, based on a sample of 399 articles published over the past decade. The findings reveal a significant expansion in academic output on ESG investments in emerging markets, with an average annual growth rate of 14.06% and an international co-authorship rate of 37.34%. China, the United Kingdom, South Africa, and the United States emerge as leading contributors, particularly since 2020. However, critical gaps persist, including inconsistencies in ESG ratings and the limited adaptation of ESG frameworks to local socioeconomic and institutional conditions. Future research should focus on strengthening public policy frameworks, designing effective fiscal incentives, assessing the distributive implications of green finance, and leveraging technologies such as fintech, blockchain, and artificial intelligence to enhance ESG rating consistency, transparency, risk measurement, and the overall efficiency of sustainable investments. Full article

This study evaluated a synergistic intervention integrating school social work and ethnic education for reducing dropout-related risk among male students in Zhuang vocational secondary schools in central Guangxi, China. Using a quasi-experimental mixed-methods design with baseline, post-intervention, and follow-up assessments, 457 students were enrolled and 435 were included in the final analysis. Compared with usual support, the intervention group showed a larger reduction in the dropout risk index at follow-up, β = −0.37, SE = 0.08, 95% CI [−0.52, −0.22], p < 0.001, and a lower likelihood of chronic absenteeism, OR = 0.56, 95% CI [0.34, 0.91], p = 0.020. The retention difference was positive but less precise, OR = 1.70, 95% CI [0.79, 3.67], p = 0.174. The intervention group also reported higher school belonging, β = 0.33, SE = 0.06, p < 0.001, and academic self-efficacy, β = 0.30, SE = 0.06, p < 0.001. Parallel mediation analysis suggested that these two protective factors accounted for part of the intervention-associated difference in dropout risk, with a total indirect effect of −0.20, 95% CI [−0.28, −0.12], p < 0.001. The findings suggest that culturally responsive practices, when combined with tiered case management and family engagement, may help strengthen protective processes and slow the accumulation of dropout-related risks. This study provides context-sensitive evidence for designing school retention interventions in vocational schools serving ethnic minority communities, while the quasi-experimental design warrants cautious interpretation. Full article

Because large language models operate through language, they rely on self-reports to access the feeling of understanding, limiting their ability to support students’ learning. A previous study explored whether facial expressions could provide an alternative way of measuring this feeling across phases of understanding in an academic context, but it had limited success. To examine how effective other physiological channels may be in measuring the feeling of understanding, this study investigated whether acoustic feature patterns are associated with nascent understanding, misunderstanding, confusion, emergent understanding, deep understanding, and underconfidence as 198 participants completed a literary analysis task while their speech was recorded over Zoom. CatBoost and logistic regression models showed modest performance, indicating that phases of understanding were not reliably distinguishable at the population level. In contrast, within-person analyses revealed consistent differences between nascent and emergent understanding across several acoustic features, including pitch, jitter, shimmer, and spectral flux. The findings show that acoustic features of speech do not reliably distinguish phases of understanding at the population level in naturalistic academic contexts but do reflect consistent within-person differences between nascent and emergent understanding, highlighting both the potential and the limits of using speech as a physiological measure of the feeling of understanding and pointing to the need for alternative ways to operationalize this feeling as it unfolds across phases. Full article

Prior research suggests that programming is a fundamental competency for all students. Due to its importance, programming education is integrated across many disciplines beyond computer science (e.g., humanities, social sciences, and engineering). Also, many existing courses report increasing enrollment trends. However, these changes have also introduced instructional challenges, particularly in supporting students with diverse backgrounds at scale. In this context, many studies have explored the use of AI code assistants as tools that may support instruction and learning. In these studies, while examining the use of AI code assistants, researchers have reported variation in educational contexts, implementation approaches, and outcomes. With this paper, we argue that synthesized information of such variations could help in understanding the effective use of such tools in programming education. To create a synthesized resource on AI code assistants, in this paper, we present a narrative review that synthesizes existing research. We reviewed 29 peer-reviewed studies identified through searches across three databases. The studies were analyzed to identify reported patterns of use, student and instructor perceptions, limitations in existing research, and suggested directions for future research. Across the reviewed studies, AI code assistants were commonly discussed for tasks such as code generation, debugging support, and real-time feedback, with ChatGPT reported most often (16 mentions), followed by GitHub Copilot (6 mentions). Disciplinary information was available in 24 studies, which helped identify the academic settings where AI code assistants were reported. Students generally describe these tools as useful, while also expressing concerns related to over-reliance and accuracy. Student perceptions were reported in 10 studies, while instructor perceptions were reported in 4 studies. Common reported limitations include small sample sizes, short intervention durations, reliance on self-reported data, and limited examination of long-term learning outcomes. Overall, this review consolidates current evidence on how AI code assistants are used and perceived in programming education and identifies areas where more research is needed. Full article