Introduction
Human anatomy forms the foundation of medical education and has traditionally been taught through cadaveric dissection and prosected specimens. Over time, anatomy teaching has evolved with the integration of imaging modalities such as X-ray, CT, and MRI, as well as digital tools including three-dimensional (3D) software, virtual reality, and augmented reality. More recently, artificial intelligence (AI) and large language models (LLMs) have been explored for applications such as personalised learning, tutoring systems, and assessment support. However, the potential use of LLMs to generate interactive simulation-based learning resources remains underexplored.

Methods
This study investigated whether LLM platforms can support the development of interactive web-based anatomy simulations from structured expert input. Three models—Codex, Gemini, and Claude—were evaluated for their ability to translate anatomy knowledge into code capable of generating interactive simulation modules. Generated outputs were refined and implemented within a simple web-based environment. The simulations were then qualitatively reviewed by anatomy educators to assess clarity, clinical relevance, and educational usability.

Results
All three LLM platforms demonstrated the ability to convert structured anatomical knowledge into functional interactive simulation scripts. The generated outputs successfully supported interactive learning tasks linking clinical signs to underlying anatomical mechanisms. While LLMs showed strong capability in generating schematic representations and interactive logic, they remain limited in producing accurate anatomical imagery for complex visual simulations. Educator feedback highlighted the potential value of these tools for developing innovative learning resources.

Conclusions
LLMs show promising potential as tools for generating interactive simulation-based learning materials in anatomy education. When combined with expert oversight, this approach may offer a scalable method for educators to develop structured simulations while promoting responsible and innovative use of AI in medical teaching.

Introduction:

With the evolving landscape of global higher education, it is imperative to analyse and understand specific friction points for systemic improvement. This paper provides comparative analysis of primary educational barriers faced by higher education students in Pakistan and Malaysia. Human Capital Theory (HCT) frames education as an investment that is aimed at providing employment and better income prospects, while Bronfenbrenner's Ecological Systems Theory categorises barriers across microsystem, mesosystem, exosystem, and macrosystem levels to capture structural influences at each level.

Methods:

This study employed a cross-sectional, mixed-methods survey design, selected for enabling synchronic comparison across two national contexts. Participants were recruited through purposive and snowball sampling, distributed via WhatsApp student groups, targeting undergraduate and graduate students in Pakistan and Malaysia. A total of 102 valid responses were collected. Likert-scale items were analysed using frequency distributions and descriptive statistics, while open-ended responses underwent inductive thematic analysis through open-coding and theme clustering. Bronfenbrenner's levels structured barrier categorisation and HCT-informed interpretation of student motivations.

Results:

Unanimously at the mesosystem level, both cohorts identified cost of education and mental health pressure as dominant shared barriers, consistent with HCT's premise that financial strain undermines educational investment returns. Structural barriers diverged at the microsystem and exosystem levels. Pakistani students reported frustration with rote memorisation, rigid structures, and outdated assessments, reflecting deeply rooted examination-based credentialism at the macrosystem level. Malaysian students identified lack of personalisation and curriculum–industry misalignment as primary exosystem-level barriers. Both cohorts preferred project-based learning, reflecting HCT-aligned demands for market-relevant skills, while Malaysian respondents additionally favoured hybrid work–study models reflecting macrosystem-level academic industry expectations.

Conclusion:

Shared financial and psychological burdens reflect HCT's consistent expectations of economic returns from education. Reform in Pakistan should prioritise competency-based progression over exam-focused structures, while Malaysia should advance personalised learning and hybrid work–study integration to align academic outcomes with industry demands.

This study investigates the developmental and contextual predictors and behavioral patterns of off-task behavior among kindergarten students in public-school English classrooms in the United Arab Emirates, while also examining teachers’ perspectives on these behaviors within early childhood learning contexts. Adopting a mixed-methods design, data were collected over an eight-week period through teacher interviews, surveys, anecdotal records, and systematic time-sampling observations involving 78 KG1 and 82 KG2 students, alongside 7KG English teachers. Findings revealed consistent behavioral patterns, with off-task behavior most frequently manifested as talking, wandering, and daydreaming. These behaviors occurred more often during afternoon sessions, extended instructional periods, and activities that lacked movement, interaction, or hands-on engagement. The analysis identified both developmental and contextual predictors shaping these behaviors. Developmental factors included emerging attention span, self-regulation abilities, and age-related learning needs, while contextual influences involved classroom environment, instructional structure, lesson pacing, and emotional climate. Teachers’ perspectives consistently framed off-task behavior as a natural and developmentally appropriate part of early childhood learning rather than intentional disruption. Many viewed these behaviors as indicators of fatigue, limited engagement, or unmet learning needs. In response, targeted strategies inspired by Montessori-informed practices, structured transitions, movement-based activities, and brief self-regulation routines were implemented to support children’s focus and participation. These approaches showed promising effects in improving engagement and reducing the frequency of off-task behaviors. Overall, the findings highlight the importance of understanding off-task behavior through a developmental and contextual lens and underscore the value of responsive, child-centered pedagogical practices in supporting engagement, attention, and wellbeing in early childhood English classrooms.

University teaching in Northeast Argentina is primarily a position held by professionals with technical training and no formal pedagogical preparation. This study evaluated the pedagogical impact and relevance of the Diploma in Curriculum Design and Management (2025), a postgraduate course offered by the Faculty of Health Sciences at the National University of Formosa.

The cohort included 104 professionals from various disciplines (engineering, law, accounting, higher education teaching, and health). A mixed-methods approach was used: semi-structured digital surveys were administered to the 61 graduates of the program, with a 100% response rate, to assess satisfaction with the content and teaching performance. Simultaneously, a dropout rate of 41.3% (43 participants) was recorded, and semi-structured interviews were conducted with those who dropped out to explore the reasons for leaving. Distinguishing between graduates and dropouts allowed for the identification of specific barriers to learning and potential selection bias factors in the results.

Graduates expressed satisfaction with the content and teaching; however, the high dropout rate suggests limitations in the suitability of the methodology and course structure to the participants' needs. Among the obstacles identified, the so-called "blank page syndrome" stood out, especially among the heads of practical work, who experienced difficulties in developing curricula. Demands were also noted for the integration of Artificial Intelligence in curriculum design, the use of the CVar (Argentine Curriculum Vitae) system, and the development of a "situated pedagogy" that addresses digital fatigue and the use of digital tools in hybrid and distance learning modalities.

The results suggest the need to make administrative structures more flexible and adapt pedagogical methodologies to the characteristics of the participating professionals, incorporating emerging technologies and strengthening pedagogical training. The findings are specific to the context of the evaluated program and should not be generalized to other institutions or regions without complementary studies.

This exploratory case study examines how text-to-image AI tools intersect with concept generation, the phase where students translate architectural theory into concrete decisions about space, form, structure, and function, in a 3rd year design studio. The research was conducted during Fall 2024 at a Central Asian university with 40 Architecture students developing a cultural center project. All participants received identical instruction on concept development; AI tools were neither assigned nor prohibited.

Documentation included semester-long observation, desk critiques, and analysis of midterm and final submissions.

Three modes of engagement emerged from observed working methods. In total, 38% of students independently incorporated text-to-image tools; 16% showed limited engagement; and 46% pursued research, abstraction, and iterative development without AI assistance. These groupings reflect behavioral patterns rather than assigned categories.

Students in the non-AI group demonstrated growing capacity to connect theoretical references to design decisions. Those using AI tools produced visually developed proposals, though the relationship between their stated concepts and subsequent spatial, formal, or functional decisions was less traceable within this single-semester context. The limited-engagement group showed minimal advancement.

At final review, differences were most apparent in how students explained the path from concept to design decisions. While individual trajectories varied, the association between mode of engagement and development of design reasoning was consistent across observed cases. These findings are specific to this studio context; they suggest questions about pedagogical sequencing when AI tools enter design education, but broader claims would require further research.

Digital simulations have become an important resource in physics and chemistry education, enhancing student motivation and facilitating the visualization of abstract scientific phenomena. They also provide a practical alternative when laboratory equipment is unavailable. This study explores the integration of artificial intelligence (AI), specifically Google AI Studio, to design an interactive simulation illustrating Ohm’s Law for students in the Scientific Common Core of the Moroccan educational curriculum. The simulation includes an interactive circuit visualization, adjustable voltage and resistance parameters, automatic graph generation, virtual measurement tools, and real-time observation of the linear relationship between voltage and current.

To evaluate its pedagogical effectiveness, a comparative study was conducted with two groups of students: an experimental group using the AI-assisted simulation and a control group receiving traditional instruction. Learning outcomes were assessed through a quiz administered after the instructional session. Results showed that the simulation group achieved an average score of 80%, compared to 50% for the control group. Students using the simulation also demonstrated a stronger conceptual understanding and improved graph interpretation skills. These findings suggest that AI-generated simulations can significantly enhance physics learning, particularly in contexts with limited laboratory resources, while also encouraging teachers to shift from being mere users of AI technology to innovators and designers who create and adapt AI-supported learning experiences that deepen students’ understanding.

Generative artificial intelligence (GenAI) is rapidly expanding across sectors and has now become embedded within educational contexts, where it is actively reshaping teaching and learning practices, especially science education. Unlike traditional digital tools, GenAI systems, particularly large language models (LLMs), can generate tailored explanations, simulations and engage students in argumentation by generating counterexamples or hypotheses in natural language. These potentials offer promising opportunities for personalized learning and effective pedagogical support. However, GenAI tools raise significant cognitive, epistemological, and ethical concerns. Science education is more sensitive to these issues because learning scientific concepts and theories requires engagement in epistemic practices such as modeling, argumentation, and evidence-based reasoning. The integration of GenAI therefore requires a critical examination of its didactic potential and implications for the teaching and learning of science. This study adopts a critical analytical approach based on a review of recent peer-reviewed articles indexed in Scopus, Web of Science, and ERIC. Articles published between 2023 and 2026 were selected using keywords related to generative artificial intelligence, science education, and epistemic practices. The analysis is grounded in key theoretical frameworks in science education research, including didactic transposition, epistemic practices, pedagogical approaches, and cognitive regulation. The analysis indicates that GenAI can support science education through personalized learning pathways, intelligent tutoring systems and feedback, automated creation of learning resources, and assistance in instructional design. At the same time, the analysis underscores substantial challenges, including risks of cognitive offloading, epistemic dependency, algorithmic bias, and the risk of confusing generated content with genuine knowledge construction. The findings indicate that the educational value of GenAI depends on its pedagogical orchestration by teachers and its alignment with disciplinary learning objectives. Instead of replacing didactic approaches, GenAI should be regarded as a didactic mediator capable of supporting epistemic practices when integrated into carefully designed science learning environments.

The rapid integration of generative artificial intelligence (AI) tools in programming education has transformed how students approach coding tasks. In web development courses, students are becoming increasingly reliant on AI systems to generate, refine, and debug code. This shift raises an important pedagogical question: does effective prompting represent a meaningful cognitive skill, or is it merely the act of entering instructions into an AI system? This study investigates prompting competency as a measurable construct and examines its relationship with student project performance in undergraduate web programming education. The research was conducted in a web development course in which students individually designed and developed an educational hybrid game. A mixed-methods approach was employed, combining analysis of students’ AI prompt logs, project performance assessments, and reflective reports. Prompting competency was operationalized through a Prompting Competency Index (PCI) consisting of five dimensions: 1) prompt specificity, 2) contextual framing, 3) iterative refinement, 4) debugging strategy, and 5) critical evaluation of AI-generated outputs. Quantitative analysis examined the relationship between PCI scores and project performance, while qualitative analysis explored students’ metacognitive strategies during prompt formulation. The findings indicate that higher-performing students demonstrate structured, iterative, and reflective prompting behaviours. These behaviours align with higher-order cognitive processes such as computational thinking, debugging strategies, and self-regulated learning. The results suggest that prompting does not merely constitute mechanical interaction with AI systems, but an emergent form of digital literacy that can be observed and assessed, highlighting the importance of developing students’ awareness of effective prompting strategies in AI-assisted programming tasks. This study contributes to ongoing discussions on AI-assisted learning by positioning prompting competency as a pedagogically relevant capability in contemporary computing education.

Generative Artificial Intelligence (GenAI) is widely recognized as a powerful tool for enhancing language acquisition, particularly by improving accessibility and personalization. While existing research has explored the application of ChatGPT in educational settings, there remains a notable gap in studies investigating how foreign language learners in China engage with indigenous Chinese AI technologies to support their English learning.

This paper examines how China has integrated GenAI technology into contemporary higher education practice. Specifically, it focuses on two research questions:

1. What novel educational modalities and support services have indigenous Chinese GenAI technologies introduced to Chinese EFL (English as a Foreign Language) learners in contemporary higher education?
2. What advantages, obstacles, and challenges have arisen in the implementation of these AI technologies?

This research utilizes a mixed-methods approach. The main participants were college students from a private university in Guangdong, China. Initially, this study utilized a questionnaire survey to perform preliminary research involving 250 students, examining the impact of China's GenAI technology on their English language acquisition. A detailed analysis of how GenAI technology affects students' learning behaviors and experiences, as well as the specific problems that come up when learning English, was performed. This study was carried out using classroom observations and semi-structured interviews with 10 undergraduates.

Recent research indicates that Gen AI can significantly enhance EFL learners' overall English competence and serve as an effective learning "accelerator." Nonetheless, excessive dependence on these tools may diminish learners' capacity for independent learning. This study empirically examines how GenAI enhances English learning for university students and suggests relevant optimization strategies. It aims to improve understanding of GenAI in EFL contexts and offer guidance for international educators to more efficiently integrate AI into teaching and learning.

The rapid expansion of generative artificial intelligence is transforming higher education by reshaping decision-making processes, institutional governance, and technology-enhanced learning ecosystems. Among these technologies, Open Large Language Models (OLLMs) have emerged as accessible infrastructures capable of supporting scalable, inclusive, and sustainable innovation in educational contexts. Despite their growing adoption, there remains limited systematic evidence regarding their organizational impact and governance implications in higher education. This study conducts a systematic literature review (SLR) to analyze the role of OLLMs in educational management and technology-enhanced learning environments. A structured and replicable search was carried out in Scopus and Web of Science, identifying an initial corpus of 616 articles published between 2020 and 2025. After applying inclusion and exclusion criteria, a final sample of 551 peer-reviewed studies was analyzed through a structured qualitative content analysis based on five research questions addressing managerial domains, governance models, levels of adoption, organizational outcomes, and associated risks. The findings reveal that OLLMs are primarily conceptualized as cognitive infrastructures that augment institutional decision-making rather than replace it, with predominant applications in strategic planning, performance evaluation, and administrative optimization. The results also show a concentration of adoption at exploratory and unit levels, indicating an emerging but uneven institutional integration. Governance discussions emphasize transparency, accountability, and risk management, particularly regarding bias, data protection, and over-reliance on automated systems. Additionally, the analysis highlights that organizational value derived from OLLMs is strongly contingent on institutional digital maturity and managerial capabilities. This study contributes to the field by providing an evidence-based framework that positions OLLMs as key components of technology-enhanced sustainable learning ecosystems, linking their adoption to governance structures and institutional capacity building. The findings offer practical implications for higher education institutions seeking to balance innovation, accessibility, and responsible AI deployment in increasingly complex educational environments.