Search Results (1,572)

This exploratory research investigates the perceived educational potential of visual, spatial, and auditory immersions as integral components of innovative healthcare simulation technologies. The study examines user experiences in learning health assessment concepts through purposefully designed 360° immersive virtual reality video (360° IVRV). Utilizing a case-study approach, insights were gathered from four subject-matter experts and four doctoral students regarding the perceived effectiveness of 360° IVRV for instructional activities focused on patient health assessment, commonly known as the Onset, Location, Duration, Characteristics, Aggravating/Alleviating factors, Related symptoms, Treatment, and Severity method (OLD CARTS). The research aimed to enhance the accessibility of learning materials by optimizing 360° IVRV content for personal phones and mobile devices, accommodating both online and traditional instructional formats. Interviews were transcribed and analyzed using qualitative data analysis software, with results categorized into subthemes, themes, and perspectives. The findings highlight the distinct perceived advantages of immersive technologies in advancing teaching methods for nursing practitioners. The discussion addresses concerns related to integrating 360° IVRV simulation technology in nursing education and the limitations of current instructional interventions. Practical implications for future research, design, and development of immersive learning materials and their integration with instructional design elements are emphasized. Full article

Artificial intelligence is rapidly changing education. However, many learning chatbots are still reactive tools, which respond to arbitrary questions without leading learners through a meaningful pedagogical journey. This article presents a deterministic state-machine orchestrator coupled with a local large language model and a knowledge-graph-framed tutoring strategy for personalized education. The proposed virtual tutoring agent is designed to combine the flexibility of conversational AI with the reliability of explicit instructional states, key performance indicator (KPI) tracking, learner profiling, and controlled transitions between explanation, practice, feedback, assessment, and remediation. The system is not meant to replace the teacher, but rather to act as a teaching co-pilot that provides ongoing feedback, personalized learning paths, accessibility, and safer deployment by processing data locally. The study also presents a compact interview-based evaluation framework and statistical analysis of user perceptions across interactivity, individuality, proactivity, security, accessibility, gamification, and global preference for educational agents over classical chatbots. The findings show that learners appreciate personalized and interactive support and that proactivity is the key feature that distinguishes an educational agent from a regular chatbot. With this article we argue that deterministic orchestration can help make AI tutoring more transparent, controllable, and ethically fit for real learning contexts. Finally, it discusses privacy, educational value, limitations and future improvements to be made before the large-scale adoption of such systems. Full article

The integration of algorithm-mediated grading in virtual higher education demands an urgent reevaluation of Digital Teaching Competencies to address critical algorithmic opacity. This study aims to systematize available scientific evidence on the intersection between DTC and Algorithmic Ethics in learning assessment within virtual environments. Following PRISMA 2020 guidelines and strict eligibility criteria, the search was executed in Web of Science and Scopus using Boolean operators, screening 262 initial records down to a final corpus of six empirical studies; the protocol was registered in OSF. Regarding results, the reviewed literature suggests that teachers may primarily employ technical and operational DTC for automated assessment design, leaving critical evaluation potentially underdeveloped. Furthermore, empirical research frequently highlights ethical dilemmas concerning data privacy risks, potential automation bias, and sociotechnical discrimination against marginalized students. Additionally, higher levels of advanced DTC and data literacy appear to serve as relevant factors for mitigating these ethical risks and auditing automated outcomes. To address this, the literature proposes implementing institutional algorithmic governance frameworks, systematic socio-ethical teacher training, and shifting automated designs toward formative, transparent feedback models. In conclusion, ethical automated assessment seems to rely substantially on individual teaching competence due to institutional policy gaps, highlighting a potential need for a transition toward techno-pedagogical frameworks focused on algorithmic justice. Full article

The COVID-19 pandemic compelled higher education to resort to e-learning, posing new challenges to the teaching/learning of pharmacy students worldwide. While digital learning provided flexibility, diverse technological infrastructure and institutional availability of resources greatly influenced the student experience. This scoping review aims to assess the perceptions relating to the pivot to e-learning among pharmacy students in the League of Arab States due to the COVID-19 pandemic and how the shift affected student engagement, learning outcomes, and institutional preparedness. Following PRISMA-ScR guidelines, a comprehensive search across ten databases was conducted to identify relevant studies published between January 2020 and December 2025. Forty studies satisfied the inclusion criteria. Pharmacy students in this region responded to the transition to e-learning in diverse ways. While most appreciated the convenience of online modalities, several challenges were consistently enumerated. These were limited technological infrastructure, reduced interpersonal interaction, and disruption of hands-on practical training. Blended learning approaches were largely favored, particularly for their ability to marry online theoretical instruction with face-to-face experiential learning. Reliability and validity issues of internet-based tests were felt by both faculty and students. Stress and mental health problems among students surfaced. Student complaints in general depicted pharmacy education’s need for pedagogic reform, better infrastructure, and student mental health services during e-learning. Areas identified from this review are instructional technology infrastructure improvement, adopting a blended learning strategy, and the need to consider the mental health of students learning at a distance. Full article

Amid the rapid advancement of artificial intelligence (AI) and the digital transformation of schooling, computational thinking has become a foundational competency for K-12 learners and an organizing principle for AI education. Existing research suggests that students need not only programming knowledge, but also the ability to analyze problems, reason with data and models, and evaluate intelligent systems responsibly. However, current K-12 AI education often remains fragmented, with insufficient curriculum progression, tool-oriented instruction, uneven teacher preparation, and limited attention to learning processes, transfer, and ethical AI use. As a conceptual and integrative framework article, this paper synthesizes policy guidance and recent instructional research on integrating computational thinking into K-12 AI education. Building on prior Chinese scholarly discussion of Computational Thinking 2.0, we adopt and extend this perspective to connect rule-based algorithmic reasoning with data- and model-centered AI problem solving. We propose a teacher–student–AI collaborative framework and a multi-level pathway covering tiered curriculum design, blended human–AI teaching, scaffolded project-based learning, AI agent-supported feedback, teacher development, and process-oriented, transfer-sensitive, and ethics-aware assessment. We argue that AI education should move beyond tool demonstration and code production toward authentic problem solving, model understanding, responsible human–AI collaboration, and reflective innovation. The framework offers guidance for cultivating students’ computational thinking, AI literacy, and creative problem-solving capacity, while identifying directions for empirical validation. Full article

Purpose: Competency-Based Education (CBE) represents a fundamental shift from traditional credit-hour systems, emphasizing mastery of defined skills and knowledge outcomes over time-based seat requirements. Despite growing institutional adoption, a comprehensive synthesis of CBE’s implementation frameworks, outcome evidence, and equity implications in the post-2015 context remains limited. Prior systematic reviews of CBE either predate the digital transformation era, focus on single disciplines, or examine only specific implementation dimensions. This review addresses those gaps by synthesizing the full breadth of CBE evidence published between 2015 and December 2025. Methods: This systematic review adheres to PRISMA 2020 guidelines. Four databases (Google Scholar, ERIC, Scopus, and institutional case-study repositories) were searched using four keyword clusters: “Competency-Based Education,” “Traditional Teaching and Students’ Competencies,” “Credit System and Students’ Achievement Measures,” and “Competency-Based Education and Workforce. After removing 125 duplicates and applying eligibility criteria (2015–December 2025; post-secondary focus), 73 sources were retained: 68 peer-reviewed articles and 5 accredited institutional case-study reports. A six-theme thematic synthesis was conducted following the work by Braun and Clarke; inter-rater reliability was κ = 0.79 on a 20% subsample (n = 15). Results: Six themes emerged: (1) Student-Centered Learning Philosophy, (2) Outcome-Based Assessment, (3) Flexible Pacing and Mastery Standards, (4) Implementation Frameworks, (5) Institutional Case Studies (University of Wisconsin Flexible Option, SNHU College for America, Purdue Global ExcelTrack, Northeastern Align, and Western Governors University), and (6) Challenges and Benefits of CBE. Evidence suggests that CBE is associated with improved adult-learner retention, workforce development alignment, and recognition of prior learning; however, these benefits are methodologically constrained, and equity implications remain structurally plausible but empirically unconfirmed. Resistance within institutions, misalignment with accreditation standards, and resource demands are the primary barriers to implementation. Conclusions: CBE provides a credible alternative to credit-hour systems for post-secondary institutions serving diverse learner populations, supported by a growing but methodologically constrained evidence base in which selection bias cannot be excluded as a contributing explanation for observed outcome advantages. Successful implementation requires phased institutional change, comprehensive faculty development, and proactive engagement with accrediting bodies. Future research should prioritize longitudinal outcome data, equity analyses by learner subgroup, and AI-driven adaptive assessments within CBE frameworks. Equity benefits are structurally plausible by design but remain empirically unconfirmed; no included study provides demographic subgroup data sufficient to verify equitable distribution of outcomes. Full article

Despite the recent surge in research on the use of AI in English language learning, little attention has been paid to its role in improving phonological awareness among preschoolers. Most existing studies have focused on general literacy skills or older learners, with insufficient emphasis on early phonemic awareness and its subskills. Furthermore, there is a lack of research examining these relationships within Arab or multilingual contexts. This study investigates the impact of artificial intelligence (AI) tools on the development of English phonological awareness in kindergarten children in an Arab educational context in Jordan using a quasi-experimental design. The participants comprised 45 students divided into two groups: a control group (n = 23), consisting of 14 females and 9 males, and an experimental group (n = 22), consisting of 12 females and 10 males. All participants were physically and mentally healthy 5–6 year-old children from similar socioeconomic and cultural backgrounds. The experimental group was taught via the AI-based Starfall platform and the control group was taught via conventional teacher-oriented instruction. Both groups were given pre- and posttests, which included assessments of five phonemic awareness skills: initial sound recognition, blending, segmentation, deletion, and substitution. Descriptive statistics, including means and standard deviations, and independent-samples t-tests were calculated to determine the effect of the AI program on developing kindergarteners’ phonemic awareness compared with conventional teaching methods. The findings of the study show significant improvements in the experimental group compared with the control group. Bringing AI into the kindergarten classroom may improve literacy instruction and, in turn, early reading readiness through engaging, interactive and adaptive learning experiences. Full article

This study explores the application of artificial intelligence (AI) in higher education to enable the early identification of at-risk students using only engagement data from learning management systems (LMS). Unlike many existing early-warning models that are limited to single disciplines or rely on sensitive demographic and prior academic records, the proposed approach offers a privacy-conscious and highly generalizable predictive framework suitable for diverse higher education contexts. The dataset includes over 1.7 million LMS interaction records from 236 undergraduate subjects spanning four academic divisions. These subjects encompass a wide variety of instructional designs and assessment structures. To address cross-subject heterogeneity, this study employs rank-based engagement features that represent students’ relative behavioral patterns within each course, facilitating meaningful comparison across disciplines without reliance on absolute activity levels. Using standard machine learning classifiers, the model achieves over 90% prediction accuracy for final subject performance by Week 3 of the semester, demonstrating that reliable early detection of at-risk students is feasible at an early stage of teaching and learning. Rather than claiming intervention effectiveness, the study positions AI-enabled early prediction as a scalable foundation for proactive student support and enhanced teaching responsiveness, with the potential to inform timely pedagogical actions such as targeted outreach and academic advising. By emphasizing generalizability, ethical data use, and privacy protection in AI-enabled learning analytics, this research contributes practical insights into how predictive AI can responsibly support teaching and learning in higher education. Full article

The integration of digital formats into undergraduate medical education offers a promising approach to enhance procedural skill acquisition. This study investigates the efficacy of a short, structured instructional video as part of a blended learning curriculum for teaching radial artery puncture to final-year medical students. A single-center, randomized, cross-over trial was conducted at the University Medical Center Mainz. Seventy-eight final-year medical students were randomized into an exposure group (instructional video based on Peyton’s four-step approach) and a control group. Performance was assessed using a 14-item checklist (maximum 16 points; pass threshold: 60%). At T1, the exposure group achieved significantly higher scores (p < 0.001, r = 0.735). Only 2.5% of the exposure group failed compared to 26.3% in the control group. After one week, the exposure group showed no significant performance decline (p = 0.101). The control group improved significantly after viewing the video (p < 0.001). At T2, no statistically significant difference remained between groups (p = 0.013, adjusted α = 0.0125). A short, structured instructional video significantly enhances initial performance and short-term retention of radial artery puncture skills in a blended learning setting. Full article

The application of artificial intelligence (AI) resources and robotics tools in education is considered vital for interdisciplinary fields to enhance the quality of the teaching and learning process. It also helps transform assessment techniques and revolutionize the whole pedagogical setting of science teacher education, in particular, AI and robotics integration in the teaching of Science, Technology, Engineering and Mathematics (STEM) subjects’ courses at the primary level. In this study, a scoping review was conducted involving seventeen peer-reviewed research papers published from 2021 to 2025. Efforts are being made to find the current perceptions and practices of preservice teachers (PSTs) at the primary level (Years 1–6; ages 6–12 in the Australian context) regarding the use of AI and robotics resources, for example, generative artificial intelligence (GenAI), foundational robotics and AI-driven robotics in teaching STEM subjects. Findings indicate that there was a significant gap in primary PSTs’ perspectives regarding their pedagogical practices to integrate STEM. As such, this influences future teachers’ knowledge, understanding, AI acceptance, and attitude toward the integration of smart AI and robotics resources in STEM classrooms. Policymakers and teachers’ education providers should align advanced technological AI resources and robotics applications with STEM curriculum guidelines and preservice teachers’ professional training programs within primary school education. Full article

Digital technologies are increasingly transforming teaching and learning, particularly through technology-enhanced assessment and feedback systems. This study examines the feasibility and pedagogical utility of the Kit-Build Concept Map (KBCM) system as a technology-supported approach for systematizing serial concept mapping in a human–computer interaction course. A three-week study was conducted with 258 undergraduate students, integrating a re-composition framework with real-time feedback to support continuous refinement of students’ externalized conceptual representations. Pre-tests, post-tests, and concept map analytics were used to evaluate learning gains and concept map structures across instructional sessions. The results show that the KBCM system enabled lecturers to identify individual and class-level map gaps and provide timely, data-informed feedback to support instructional monitoring and pedagogical decision-making. Students showed statistically significant improvements in learning outcomes, consistent progress across instructional weeks, along with a measurable reduction in discrepancies between student-generated maps and the expert map. These findings suggest that serial concept mapping with re-composition and feedback support may help students refine their externalized conceptual representations to become more closely aligned with target knowledge over time. Overall, this study highlights the potential of technology-enhanced concept mapping systems to support continuous instructional feedback, assessment, and data-informed pedagogical practices in higher education. The findings should be interpreted within the context of a short-term, three-week implementation focusing on changes in externalized conceptual representations rather than direct measurement of internal cognitive processes. Full article

While existing curricula often focus on theoretical aspects of sustainability, they frequently fail to equip students with practical design skills required by the green industry. To address this disconnect, this study seeks to answer: How can a structured pedagogical framework effectively enhance students’ ability to translate abstract sustainability principles into concrete technical solutions? This study introduces a comprehensive CDIO-based framework reform for Embedded Intelligent Systems education, weaving sustainability throughout every phase. We put forward a “Sustainable CDIO Capability Model” that charts a progressive pathway—starting from basic resource awareness and advancing through to sophisticated sustainable system innovation. Our four-dimensional teaching strategy brings this model to life: first, project-based learning driven by real sustainability challenges; second, a hybrid ecosystem blending online resources, hands-on practice, and immersion in green industry contexts; third, hierarchical team-based pedagogy backed by personalized support mechanisms; and fourth, a multi-dimensional assessment system that weights energy efficiency, resource stewardship, and social value creation alongside conventional metrics. We implemented this approach with Intelligent Science and Technology majors at Wuhan Institute of Technology. The results show the model effectively bridges the persistent gap between dry technical content and the practical demands of green industry. Students made substantial gains not merely in core engineering capabilities—system architecture, hardware-software co-development—but crucially in sustainable design awareness and their capacity to untangle complex sustainability challenges. This work offers a readily transferable framework for embedding Education for Sustainable Development (ESD) into engineering curricula worldwide. It provides practitioners with a concrete, tested model for cultivating the next generation of engineers who naturally think and act with sustainability in mind. Full article

The present article employs a mixed methodology to evaluate IkasLab’s innovative spaces, investigating how the reorganisation of educational spaces contributes to the development of 21st-century skills in the Basque educational context. The sample comprised 13 primary schools (ages 6–12), where quantitative data were collected using a validated instrument (rubric) (n = 13 spaces) and qualitative data through semi-structured interviews (n = 22 teachers). Reliability analyses (Alpha = 0.909; Omega = 0.907) confirmed the robustness of the assessment instrument. The quantitative findings indicated a high level of project implementation ($\overline{\mathrm{x}}$ = 3.26/4), with particular emphasis on the development of communication and relational skills. However, the indicators associated with metacognitive work showed less consolidation. Qualitative analysis yielded significant findings pertaining to student autonomy, methodological innovation, and educational inclusivity. Notable gaps were also identified in the integration of metacognitive practices and urgent needs for systematic teacher training and continuous pedagogical support. The results suggest that IkasLab constitutes a solid and promising framework for reimagining learning spaces and promoting educational practices in line with contemporary challenges. However, the full impact of the model depends on ensuring sufficient resources and strengthening professional training that enables teachers to effectively integrate the principles of the model into their teaching activities. Full article

Persistent disparities in literacy outcomes affect deaf learners, who may experience writing instruction that does not align with their linguistic contexts. This study examined how teachers’ instructional reasoning about writing developed during participation in an online Strategic and Interactive Writing Instruction (SIWI) professional development (PD) program. Nineteen teachers of deaf students completed a 30-hour virtual PD that combined asynchronous modules and synchronous collaborative sessions focused on evidence-based writing instruction. Teachers completed video-based situational simulations at three time points across the PD; responses were scored using a 5-point holistic scale to assess growth in pedagogical content knowledge. A post-workshop survey also asked teachers to rate prior use, anticipated implementation, and readiness to implement SIWI-aligned practices on a 3-point scale. Survey results indicated relatively low pre-workshop use of practices and higher anticipated implementation and readiness after PD. Repeated-measures analyses of simulation scores indicated significant improvement over time, reflecting strengthened ability to identify instructional priorities, integrate language and writing instruction, and justify responsive teaching decisions. To illustrate what this growth looked like in practice, the manuscript includes an embedded illustration of one teacher’s scenario responses across the three time points, showing a shift from more general/imprecise instructional commentary to more SIWI-aligned, objective-driven reasoning that explicitly links language supports to targeted writing instruction and next instructional steps. These findings suggest that video-based simulations offer a feasible, practice-oriented way to assess teacher learning in online PD, and that programs preparing teachers of deaf writers should pair self-report measures with simulation-based tasks that document how teachers apply pedagogical content knowledge to writing instruction. Full article

Within the conceptual framework of the Unified Flexibility & Mindfulness (UFM) model, the current study applied a contextual behavioral science lens to understanding the challenges and dynamics of classroom teaching in the United States. In particular, the study sought to highlight the specific flexibility processes linked to lower teacher burnout and to greater use of adaptive instructional and behavior management strategies—deepening the conceptualization and operationalization of teachers’ Social and Emotional Competence (SEC). Toward that end, a sample of 308 K-12 teachers (79% female, 85% white, M_age = 42 years old) with an average of 13 years of teaching experience completed a relational task (RT) indirectly assessing relational thinking about students along with teacher-report measures of: (1) their own use of 14 forms of mindful flexibility (and distracted, reactive inflexibility) in the classroom, (2) their conscious perceptions of student engagement and disaffection with learning, (3) their use of adaptive instructional and behavior management strategies, and (4) a measure of work-related and student-related burnout. Exploratory network analyses largely supported the Unified Flexibility and Mindfulness model shaping teachers’ functioning in the classroom. The results further highlighted unique links from categorical thinking on the RT (i.e., viewing all positive or negative adjectives as essentially the same in students) to greater burnout and unique links from more nuanced thinking on the RT (i.e., the ability to see negative and positive traits coexisting in students) to greater perceptions of both student engagement and disaffection. Teachers’ engagement of committed action and self-as-context (maintaining a broader perspective in the face of disruptive behavior) along with perceptions of greater student engagement emerged as some of the most robust predictors of using adaptive classroom strategies. In contrast, teachers’ engagement in fusion and inaction (along with perceptions of greater student disaffection and lower student engagement) emerged as the most robust predictors of teacher burnout. Implications are discussed. Full article