This conceptual paper proposes an AI Competency Taxonomy that defines user integration levels of AI tools within educational contexts. The rapid advancement of Artificial Intelligence (AI) has transformed teaching, learning, and knowledge creation, particularly in higher education. While existing studies discuss AI literacy and competency, they lack a comprehensive taxonomy that provides actionable guidance for identifying competency stages and evaluating integration pathways. To address this gap, this study develops an AI Competency Taxonomy grounded in Bloom’s Revised Taxonomy—Remembering, Understanding, Applying, Analyzing, Evaluating, and Creating—while synthesizing insights from diverse AI literacy and competency models published between 2021 and 2025. A concept-driven, iterative selection process identified 22 relevant sources, including 21 peer-reviewed journal articles and the UNESCO (2024) AI literacy and competency framework, which was purposefully included as an authoritative global guideline shaping educational policies and competency standards. Findings present a taxonomy comprising six progressive categories based on user roles: (i) Learner-as-Recipient, (ii) Learner-as-Supported, (iii) Learner-as-Collaborator, (iv) Learner-as-Co-Designer, (v) Learner-as-Leader, and (vi) Learner-as-Strategist. These stages represent a continuum of AI integration, ranging from basic, receptive usage to advanced, strategic engagement in academic contexts. This study further identifies key determinants influencing integration levels and outlines structured pathways for progression. The proposed taxonomy offers both a theoretical foundation and a practical framework to guide future empirical research, curriculum design, and policy development aimed at fostering responsible, creative, and effective AI use in education.

Introduction: Virtual reality (VR) has increasingly been explored as a pedagogical innovation in higher education. While its benefits for enhancing motivation, visualization, and practical training are widely acknowledged, the perceptions of engineering in faculties remain less documented, particularly when comparing different institutional contexts.

Methods: A survey instrument comprising 22 validated items grouped into six dimensions (competence, usability, technical aspects, drawbacks, didactic employability, and future projection) was administered to 279 engineering professors from 15 Latin American countries. Responses were analyzed using descriptive statistics, t-tests, and MANOVA to identify differences according to university type (public vs. private), gender, age, and teaching experience.

Results: Professors valued VR highly in terms of usability, technical potential, and teaching utility. However, low self-assessment of competence and lack of formal training highlighted a gap between perceived usefulness and readiness for classroom implementation. Significant differences emerged according to institutional ownership: professors in private universities tended to provide more favorable and consistent evaluations of VR’s educational benefits compared to those in public universities. Gender, age, and teaching experience also influenced perceptions, although these effects varied depending on the subscale analyzed.

Conclusions: The principal results indicate that engineering faculties recognize VR as a promising resource for higher education but still lack sufficient training for its effective use. This institutional divide suggests that private universities may be more proactive in adopting immersive technologies, whereas public universities show greater skepticism. Addressing this imbalance through targeted training and resource allocation could accelerate the integration of VR into engineering education across Latin America.

The rapid evolution of agentic artificial intelligence (AI) and cloud‑native architectures is redefining the possibilities of Technology‑Enhanced Education. This paper introduces a framework that integrates agentic AI systems, secure distributed platforms, and generative models to deliver scalable, adaptive, and inclusive learning experiences. By orchestrating autonomous AI agents within cloud environments, this framework enables personalized instruction, automated assessment, and real‑time feedback loops that respond dynamically to learner progress. Drawing on enterprise‑scale implementations in financial services and retail, this work demonstrates how intelligent orchestration can improve accessibility, optimize resource allocation, and enhance trust through compliance‑driven design.

The proposed approach emphasizes scalability and inclusivity, ensuring that digital pedagogy can meet diverse learner needs while maintaining rigorous standards of privacy and security. Key contributions include: (1) a model for deploying agentic AI in education that balances personalization with institutional governance; (2) strategies for embedding adaptive learning systems into cloud‑native infrastructures; and (3) evidence of improved learner outcomes through AI‑driven search and conversational interfaces.

By bridging technical innovation with pedagogical practice, this paper highlights pathways for transforming classrooms into data‑informed ecosystems that support both educators and learners. The findings aim to provide actionable insights for researchers, practitioners, and policymakers seeking to harness AI responsibly in advancing teaching effectiveness, learner engagement, and equitable access to education.

This paper will explore the Min Thu Wun’s Pyinma Ngout To(ပျဉ်းမငုတ်တို) poem of Higher Education first year Syllabus in Myanmar aspects of the prosody. Prosody in linguistics refers to the study of all the elements speech like intonation, stress, rhythm, and loudness that contribute to the acoustic and rhythmic patterns of language. The aim of this paper is to show that "Pyinma Ngout To" is not just a poem about a tree in the woods, it is a political metaphor for a survivor. By studying the "stress" and "intonation," the poet is effectively described the tone of the national struggle at that time (11.1.1949) and it reflects the present perspective views of point. The research questions are (1) How do the specific prosodic features namely stress, loudness, rhythm, and intonation, manifest in the phonetic structure of Min Thu Wun’s "Pyinma Ngout To"? (2) How does the poem’s rhythmic structure serve as a metaphor for Myanmar’s political endurance from a modern (present) perspective? (3) How does analyzing the prosody of this poem help First-Year Higher Education students understand the evolution of modern Burmese literature and present political issues? This research will focus on a qualitative research design, using a combination of rhythmic and descriptive methods to analyze Min Thuwan’s poem “Pyin Ma Ngo To”. This study uses a rhythmic analysis to establish a scientific and linguistic framework to assess how Burmese poetry functions as a vocal art form (focusing on stress, volume, and rhythm). By teaching the poem, students studying in Myanmar’s crisis can experience and appreciate Pyin Ma Ngo To’s powerful ability to express the energy, resilience, and unwavering determination of life.

Student engagement (SE) is a significant priority for HEIs across the globe, one which is perceived as reflecting education quality as well as influencing outcomes such as ‘student success, retention [and] learning outcomes’ (Shomotova and Ibrahim, 2025, p.1206). Existing studies reveal a ‘paucity of research on the position of academics […] on how they could enhance student engagement' in HE (Owusu-Agyeman, 2022, p.661). As such, our paper makes an exploratory contribution by investigating classroom practice designed to stimulate SE at an operational level.

Empirically, our paper reports on a small, institutionally-approved qualitative study of activities/strategies that were identified by academics (n=19) as ‘engaging’ for students across the degree portfolio (both home and international) when in the university classroom. With taught representation from all levels of study in a School of Education, participants from a research-intensive university in South West England took part in a small group activity during a School training session on SE, generating original documents (n=152) in the form of individual paper ‘strategy squares’.

Richly informed by BERA (2024), retrospective consent was sought to rigorously analyse the documents using thematic analysis (Naeem et al., 2023). Thoughtful reflections in our paper follow an illustrative presentation of four main themes.

Collectively, the research reveals fresh insight into the variety of dynamic pedagogical strategies (Dominguez, 2024) utilised in the School. Despite the limited scope and generalisability of the findings, the data analysis recognises SE to be a complex and multi-dimensional construct, prompting critical questions regarding the depth and quality of such engagement. Our paper concludes by acknowledging the influence of cultural background on students’ receptivity to different pedagogical approaches, as well as the role of instructor–student relationships in shaping supportive and effective learning environments. Given the localised nature of the sample and the exploratory design, we appreciate that our conclusions should be interpreted with appropriate caution.

Introduction

Understanding the role of Artificial Intelligence (AI) in language acquisition is fundamental for multilinguals studying in Northern Cyprus. The current study examines Syrian students’ perceptions of learning Turkish, the effects of AI and contextual exposure, the challenges encountered in the acquisition process (grammar, vocabulary, speaking, and pronunciation), AI support for language skills, and their recommendations for using AI to learn Turkish efficiently. Thus, this investigation drew on Krashen’s input hypothesis, Swain’s output hypothesis, and the SAMR model.

Methods

This study used semi-structured interviews as a qualitative approach to collect data. The developed questions were organized into four main themes: perceptions, AI and contextual factors, challenges and AI support, and future suggestions. Due to exam schedules, female participants could not be reached; therefore, thirteen male pharmacy students, ranging from freshmen to seniors, were interviewed online in Arabic. The transcriptions were generated using digital tools (TurboScribe, Google Docs, and Copilot), and the original Arabic transcripts were manually reviewed to ensure translation validity and reliability. The data were analyzed using qualitative content analysis with MAXQDA Pro 2.0 software, applying the deductive coding approach based on the pre-set categories.

Results

The participants generally expressed positive perceptions of learning Turkish. AI supported language acquisition (e.g., serving as a personal teacher), while immersion remained the key factor in language learning. Although the students addressed several challenges pertaining to language acquisition (e.g., Turkish vowel pronunciation and suffixes), AI tools assisted them in overcoming some of these issues. ChatGPT and Gemini were the most suggested AI tools among the participants, along with practicing sentence structure before real-life conversations.

Conclusion

This study highlights the role of digital assistants in Turkish language acquisition, particularly in improving learners’ proficiency and confidence, despite limitations related to sample type and context. It also provides practical implications and future suggestions for integrating AI into Turkish language learning.

Although hospital antibiograms are widely available to guide antimicrobial selection, they may overcall resistance when extrapolated to common community-acquired infections, such as meningitis, pneumonia, cystitis, and skin and soft tissue infections. The Clinical & Laboratory Standards Institute (CLSI) has called for the need for enhanced community antibiograms but acknowledges the challenges due to a lack of centralized laboratories to take on this responsibility. LifeLabs British Columbia (BC), a regional laboratory network with 129 collection centres in rural and urban BC communities, is uniquely positioned to address this challenge. In summer 2025, we recruited 10 university undergraduate students from various disciplines (medicine, pharmacy, and neuroscience) through the University of British Columbia directed studies and undergraduate summer student research programs to create enhanced antibiograms to address community needs. Under the supervision of a clinical assistant professor and using data collected at LifeLabs regional microbiology laboratories, the students statistically analyzed results for the benefit of community healthcare, public health, and antimicrobial stewardship teams. These antibiogram topics range from blood culture, bacterial meningitis pathogens, pediatric urine culture, sexually transmitted infections, carbapenem-resistant organisms, ear pathogens, and tetracycline baseline resistance rates in various communities. We acknowledge that many of the published infectious disease guidelines are based on American data, which are not always applicable to the BC and Canadian population. An evaluation is required to determine whether these clinical guidelines are still within the Canadian context based on local BC community data. This presentation will share our successes and challenges in this journey and aims to influence colleagues in academia and clinical laboratories to start the same initiative. We successfully conducted 10 different antibiogram projects during the summer, some of which have already been accepted for peer-reviewed journal publications. This experience has demonstrated how we can make use of the resources in education to go the extra mile for patients and the community.

Assessment design plays a pivotal role in shaping students’ learning behaviours, influencing both the depth and direction of study in higher education. This mixed-methods study examined perceptions of oral summative, written summative, and formative assessments among 33 students enrolled in three international master’s programmes at Karolinska Institutet—Translational Physiology and Pharmacology, Bioentrepreneurship, and Global Health—comprising learners from diverse geographic and disciplinary backgrounds.

Quantitative Likert-scale data indicated that written summative assessments were consistently perceived as more effective for consolidating factual knowledge (median = 4), whereas oral summative assessments received more varied ratings for supporting deeper understanding (median = 3) but were clearly valued for encouraging reflection (median = 4). Oral summative formats were moderately motivating (median = 3) and perceived as more stressful than written formats, although reported stress levels remained moderate overall (median = 3). Formative assessments demonstrated the strongest consensus across the cohort (median = 4), with students highlighting clarity, feedback, interaction, and a low-stakes structure as key drivers of engagement.

Qualitative findings reinforced these patterns, illustrating how oral summative assessments foster real-time reasoning and communication, written summative tasks promote clarity and precision, and formative activities cultivate confidence and continuous improvement. In the context of increasing use of Generative AI, oral assessment formats may also support academic integrity by enabling more authentic evaluation of student understanding. Subgroup analyses further indicated that engagement—but not stress—modulated perceptions of professional relevance, with highly engaged students more strongly recognising the authenticity of oral formative assessment practices.

Overall, the findings underscore the pedagogical value of integrating diverse assessment formats to support cognitive depth, emotional regulation, inclusive learning practices, and career preparedness in global biomedical higher education.

Institutional end-of-semester surveys are widely used in higher education to gather students’ evaluations of modules and teaching practices and to inform quality enhancement processes. However, existing research has largely focused on quantitative satisfaction measures, with limited qualitative exploration of how students interpret and engage with feedback systems, particularly within discipline-specific contexts such as postgraduate computing education.

This study explores postgraduate computing students’ experiences of end-of-semester survey effectiveness through a thematic analysis of qualitative data collected via Microsoft Forms. A survey link and QR code were distributed to students enrolled in two postgraduate computing modules, resulting in 52 responses. The questionnaire included open-ended items examining perceived usefulness, clarity and relevance of questions; impact on the teaching–learning environment; influence on academic satisfaction; timing of administration; and strategies to enhance engagement. Thematic analysis identified five themes: perceived benefits of feedback, clarity and relevance of survey questions, the role of timing in feedback collection, students’ need for guidance in providing meaningful feedback, and strategies to improve engagement. The findings indicate that while students recognise the potential value of surveys, their effectiveness is shaped by the relevance of questions, transparency in how feedback is used, and support for meaningful student participation. This study provides qualitative insight into how feedback systems are experienced within postgraduate computing education and highlights the importance of feedback literacy, contextualised survey design, and institutional transparency in enhancing the effectiveness of student evaluation processes.

Introduction:
The rapid incorporation of generative AI into teacher education has outpaced the development of inclusive pedagogical frameworks. This study aims to validate the Integrated Inclusive Model with AI for Challenges (MIIA-R), which combines Universal Design for Learning, challenge-based learning focused on authentic professional problems, and Participatory Action Research to guide accessible instructional design in a virtual postgraduate context.

Methods:
A mixed-methods Participatory Action Research design was implemented across three iterative cycles with 83 master’s students, analysing 80 challenge proposals. Data were collected through an analytic rubric, a creativity scale, reflective group journals, and a satisfaction survey to examine proposal quality, pedagogical uses of AI, and patterns of creativity.

Results:
Overall proposal quality exceeded the functional threshold (M = 3.29/5), confirming the model’s applicability. However, a gap emerged between feasibility (M = 4.18) and pedagogical use of AI (M = 2.39), suggesting that technical adoption does not guarantee inclusive integration. Creativity followed an inverted U-shaped trajectory: it increased during guided design but declined at the public challenge fair, where evaluative pressure acted as a pedagogical selection mechanism between instrumental uses of AI and more reflective applications. In the final cycle, diversity of AI tools correlated significantly with creativity (ρ = 0.527; p < 0.001), supporting the view that natural-language tools function as conditional catalysts rather than automatic drivers of innovation.

Conclusions:
The MIIA-R model offers a reproducible framework that extends previous approaches by integrating AI, UDL, and challenge-based learning into a reflective and ethically grounded design process. Its main contribution is to demonstrate that inclusive pedagogical creativity depends less on the number of tools used than on prior pedagogical scaffolding and critical reflection, reinforcing its relevance for teacher education in virtual environments.