Search Results (615)

As educators are increasingly expected to integrate technology into meaningful learning experiences, higher education institutions face growing demands to prepare digitally competent STEM teachers. This study examines an interdisciplinary IoT-enhanced project-based training involving pre-service teachers from the Computer Education and Instructional Technology (CEIT) and Science Education programmes, focusing on pre–post changes in computational thinking, collaborative processes, project development experiences, and participants’ perceptions of how the training contributed to their professional development. The study involved 36 pre-service teachers from CEIT and Science Education programmes and employed an embedded mixed-methods design combining computational thinking assessments with qualitative analyses of project and collaboration processes. Results showed statistically significant pre–post increases in decomposition in both groups and in algorithmic thinking among Science Education pre-service teachers. The overall Computational Thinking Skills Test (CTS Test) score did not change significantly in the CEIT group, whereas the corresponding change in the Science Education group was interpreted cautiously because it was at the conventional significance threshold. No statistically significant changes were found in pattern recognition or abstraction in either group, and the subdimension findings were treated as exploratory given the small sample and the absence of correction for multiple testing. Furthermore, active participation in collaborative meetings was associated with stronger teamwork practices, while limited engagement was associated with reported implementation challenges. Because attendance was self-selected, these associations cannot be interpreted causally. Participants perceived their IoT learning experience as making a positive contribution to their professional development and future teaching practice. Taken together, the findings suggest that interdisciplinary IoT-supported project-based learning (PBL) may provide a promising framework for supporting selected dimensions of computational thinking and collaboration in pre-service teacher education, while also offering an accessible instructional model that supports participation among learners with different levels of prior technical expertise. Full article

Traditional engineering education often relies on delayed summative assessments, struggling to provide the real-time feedback required for sustainable teaching quality improvement. To address this gap, this study proposes a sustainable teaching quality assurance framework leveraging OpenMAIC, an AI-enhanced Multi-Agent Interactive Classroom. A comparative case study was conducted in an undergraduate “Electronic Technology” course. The experimental group utilized the AI-driven curriculum, where an “AI Professor” and “AI Peers” engaged students in heuristic dialogues while generating dynamic HTML5 virtual simulations for immersive circuit experiments. We evaluated the framework’s effectiveness by analyzing fine-grained learning-unit analysis interaction logs, milestone scores, and stakeholder surveys. The results revealed that the multi-agent pedagogical model enabled real-time detection of potential misconceptions based on interaction behaviors, identifying learning bottlenecks weeks earlier than traditional exams. Furthermore, the experimental group significantly outperformed the control group in complex project-based learning tasks, alongside reporting reduced learning anxiety. Instructors also experienced a substantial decrease in repetitive administrative workloads. This study concludes that integrating multi-agent AI and immersive virtual simulations not only enhances continuous process assessment but also establishes a brand-new scalable and continuously improvable teaching paradigm for future digital engineering education. Full article

The rapid digital transformation of education has increased the demand for intelligent assessment systems and architecture capable of evaluating institutional readiness for technology-enhanced teaching, learning, and workforce development. As educational organizations adopt digital platforms, cloud-based learning environments, and globally connected instructional models, there is a growing need for systematic frameworks that can assess human, technological, and organizational capabilities required for successful implementation. This study proposes a digital capability assessment framework for technology-enhanced educational systems that integrates instructional competency evaluation, technology readiness analysis, infrastructure assessment, and institutional support monitoring within a unified analytics-driven model. The proposed framework consists of multiple assessment components, including digital literacy measurement, technology integration capability analysis, instructional innovation indicators, collaborative learning readiness metrics, and institutional resource evaluation mechanisms. These components are designed to support continuous monitoring of digital transformation initiatives and provide evidence-based decision support for educational planning, resource allocation, and technology adoption strategies. The framework further incorporates analytics and reporting functions that enable stakeholders to identify capability gaps, evaluate implementation risks, and prioritize system improvement initiatives. To demonstrate the applicability of the framework, a pilot assessment was conducted using competency and readiness data collected from instructional personnel within a technology-enhanced educational environment. Analytical results revealed strong capability levels across digital instructional practices, technology-supported curriculum development, online learning delivery, and collaborative knowledge-sharing activities. The assessment also identified infrastructure and support-related constraints that may affect the scalability and sustainability of advanced digital learning initiatives. The proposed framework contributes a scalable architecture for institutional readiness assessment and digital capability analytics within technology-enhanced educational systems. By integrating human capability indicators, infrastructure readiness measures, and organizational support metrics into a unified evaluation model, the framework provides a foundation for intelligent decision-support systems, digital transformation monitoring platforms, and technology governance mechanisms in modern educational ecosystems. Full article

The rapid digitalisation of the construction and built-environment sectors has increased demand for innovative teaching methods in higher education. Among emerging digital tools, metaverse applications offer immersive and interactive learning environments with the potential to transform traditional pedagogical practices. This study evaluates the adoption of the metaverse application (EON-XR) in built-environment education by examining educator training outcomes using the Kirkpatrick model. The evaluation focuses on a structured training workshop designed to equip built-environment educators with the skills to integrate metaverse-based applications into first-year teaching modules. A post-training evaluation employed a Google Forms survey instrument explicitly aligned with Kirkpatrick’s four evaluation levels: reaction, learning, behaviour, and results, with Levels 3 (Behaviour) and 4 (Results) operationalised as behavioural-intention and perceived-results proxies collected immediately post-training, ahead of any classroom implementation. The instrument captured educators’ perceptions of usability, relevance, pedagogical value, behavioural intention to apply acquired skills, and the broader institutional and disciplinary implications of metaverse adoption. Both qualitative and quantitative data were analyzed to assess the training programme’s effectiveness across these four dimensions. Findings indicate a generally positive reaction to metaverse training, with educators recognizing the relevance and user-friendliness of the EON-XR application. Evidence of learning was demonstrated by increased confidence and perceived competence in applying metaverse tools for teaching. At the behavioural level, participants expressed strong intentions to integrate metaverse applications into their teaching practice. At the results level, the training was perceived as enhancing teaching engagement, stimulating interest in construction digitalisation, and contributing to the institutional standing of higher education providers. The study concludes that metaverse-based educator training can play a critical role in advancing digital pedagogy within built-environment education. By applying the Kirkpatrick model, this research provides a structured, replicable framework for evaluating the adoption of immersive technology in higher education contexts. Full article

Preparing future educators for technology-enhanced learning environments has become increasingly important as artificial intelligence (AI) continues to influence teaching and learning. Guided by the Intelligent Technological Pedagogical Content Knowledge (i-TPACK) framework, this mixed-methods study examined preservice teachers’ perceptions of AI and the competencies they considered necessary for effective AI integration. Participants included 108 preservice teachers enrolled in a teacher preparation program at a public university in the southeastern United States. Data were collected using a survey containing Likert-scale and open-ended questions. Quantitative data were analyzed using descriptive statistics, exploratory factor analysis, and independent-samples t-tests, while qualitative responses were analyzed using thematic coding. The exploratory factor analysis identified a four-factor empirical structure that partially corresponded with the original theoretical domains, with varying levels of internal consistency. Preservice teachers generally viewed AI favorably and recognized its potential to support teaching and learning. Participants with internship experience reported significantly higher scores for perceived changes brought by AI and reasons for using AI than those without internship experience. Qualitative findings identified five competencies considered important for responsible AI integration: AI literacy, prompt engineering, critical evaluation, ethical AI use, and pedagogical balance. Participants also expressed concerns about academic dishonesty, misinformation, overreliance on AI, reduced critical thinking, and loss of human interaction. The findings highlight the importance of preparing preservice teachers to integrate AI in pedagogically meaningful and ethically responsible ways. Full article

Backgrond/Objectives: Human anatomy education has traditionally relied on cadaveric dissection as the reference standard for acquiring anatomical knowledge. However, decreasing availability of donated bodies, curricular changes, and rapid technological advances have encouraged the progressive integration of complementary educational resources, including historical anatomical collections, physical models, plastinated specimens, and digital technologies. At the same time, anatomical museums have evolved from repositories of scientific heritage into dynamic educational environments that promote public engagement, accessibility, and lifelong learning. Methods: This article presents the educational experience of the Human Anatomy and Histology Institute of the University of Palermo, where historical anatomical collections, cadaveric dissection, plastination, three-dimensional (3D) digitization, virtual anatomy, and interactive educational technologies have been integrated within a single multimodal teaching framework. Results: The strengths and limitations of each educational resource are discussed in relation to contemporary anatomy education and museum communication. Our experience suggests that digital technologies achieve their greatest educational value when they complement rather than replace traditional anatomy teaching. Conclusions: Integrating historical collections with cadaveric study, physical models, and immersive digital tools offers a comprehensive educational approach capable of enhancing anatomical understanding, improving accessibility, and promoting more engaging learning experiences for students and the wider public. Full article

With the rapid advancement of educational digitalization and generative artificial intelligence, knowledge graphs and AI agents have emerged as key technologies supporting smart higher education. To address challenges in current AI-assisted teaching, including insufficient support from structured knowledge systems, inadequate coordination across teaching processes, and delayed evaluation and feedback, this study proposes a collaborative framework integrating knowledge graphs and AI agents within an integrated teaching platform, and illustrates its integrated operational mechanism for knowledge organization, learning support, learning analytics, and teaching evaluation. Using the core course Nautical Navigation in the Navigation Technology Specialty at WHUT as a case study, the framework was implemented on the Chaoxing Smart Course Platform and applied to 459 students across two cohorts. The implementation achieved knowledge structuring and learning process visualization. The constructed course knowledge graph includes 336 knowledge points and more than 2700 associated learning resources and assessment items, while 30 instructional AI agents were developed and deployed to support different teaching and learning scenarios. The results indicate that the framework improves course knowledge organization, enhances student engagement and self-directed learning, enables visualization of learning processes and precision in teaching evaluation, and promotes a shift from experience-based to data-driven instructional decision-making. Compared with the previous cohort, students’ average daily learning time increased from 564 s to 618 s, participation rates in chapter quizzes, group discussions, and assignment completion all exceeded 90%, and more than 80% of respondents expressed willingness to continue using this learning model. The study provides a practical reference for AI-enabled teaching reform and digital transformation in higher education. Full article

The use of Artificial Intelligence (AI) in Formative and Shared Assessment (F&SA) processes refers to the application of AI-based technologies to support formative and continuous assessment in Higher Education (HE). F&SA systems involve the ongoing monitoring of students’ learning, the provision of feedback that enables them to regulate and improve their performance, and the collection of information that informs the continuous improvement of teaching practice. In this context, AI can serve a dual purpose: when orientated towards students, it enhances learning outcomes; when directed at educators, it supports the development of their pedagogical expertise through tools designed to assist in the creation of assessment instruments, the generation of automated feedback, the analysis of learning data, and the design of simulation environments that foster the development of professional competencies. The integration of AI into F&SA practices holds considerable potential to transform traditional assessment approaches by enabling more personalised, adaptive, and timely feedback for both students and educators. In this shared assessment framework, students may likewise draw on AI applications to support specific dimensions of their learning, including academic writing, knowledge organisation, and the generation of educational content, thereby becoming active participants in their own assessment processes. However, the incorporation of AI into F&SA also requires careful consideration of the pedagogical, ethical, and institutional challenges it entails, particularly those related to academic integrity, cognitive offloading, and the responsible use of AI tools. It is therefore essential to promote AI literacy in HE among both faculty members and students, fostering a critical and informed engagement with these technologies that ensures the pedagogical relationship, along with the shared, formative nature of assessment, remains at the core of meaningful learning processes. Full article

Artificial intelligence (AI)-based support systems are transforming the educational landscape by enhancing teaching efficiency, personalized learning, and accessibility. Despite rapid technological progress, educational institutions face persistent challenges such as unequal access to quality learning resources, limited teacher support, and the need for individualized student engagement. These issues hinder effective learning outcomes and inclusivity in modern classrooms. This study presents a comprehensive literature review and a methodology grounded in constructivist learning theory to develop an AI-based educational support framework. The study is situated within the context of a higher education course integrating AI-supported learning. The proposed framework is developed by synthesizing theoretical and empirical evidence and is subsequently evaluated by experts in educational technology and artificial intelligence. Data are collected through structured expert questionnaires and qualitative feedback. Quantitative data are analyzed using descriptive statistics, while qualitative responses are examined through thematic analysis to inform framework refinement. The study adheres to established ethical principles, including informed consent, voluntary participation, confidentiality, anonymity, and secure data management. Moreover, the paper explores the design and implementation of sustainable and AI-based educational support systems that address these challenges through intelligent tutoring, adaptive learning analytics, and automated feedback mechanisms. By integrating natural language processing, machine learning, and predictive modelling, the proposed framework provides real-time assistance to educators and learners, fostering data-driven decision-making and inclusive pedagogy. Qualitative expert evaluation suggests that an AI-based educational support framework has the potential to improve teaching support, learner engagement, and personalized learning while providing a scalable and equitable approach for higher education. Full article

This paper focuses on the development of 3D digital resources and a learning platform for the Engineering Drawing course, exploring construction pathways and implementation methodologies while further investigating the application potential of artificial intelligence techniques in model construction and instructional processes. The 3D digital resources encompass basic geometric elements, complex structures, and engineering entities, supporting interactive operations such as rotation, scaling, and sectioning. These resources effectively overcome the inherent limitations of traditional engineering drawing laboratories, including high costs, fragility, delayed updates, limited coverage, and inadequate adaptability to individual student differences. The integration of AI technologies significantly enhances the efficiency of digital resource development and effectively stimulates student engagement in learning. At the pedagogical practice level, this paper proposes two AI-based conceptual teaching frameworks, namely the Adversarial Learning Model and the Challenge-based Learning Model. The learning platform incorporates knowledge graphs and process-based assessment mechanisms, achieving an organic integration of personalized and contextualized learning. A three-year longitudinal teaching performance analysis reveals a marked improvement in overall student grades, with a notable increase in high-grade proportions and a decrease in failure rates. Questionnaire survey results further confirm that students’ spatial imagination, comprehension, and practical application abilities have been strengthened, with minimal adverse effects. Moreover, in extracurricular activities, students participating in graphic design competitions have achieved outstanding performance. Comprehensive findings indicate that the synergistic application of digital resources, online learning platforms, and advanced AI technologies show a positive correlation with improved teaching effectiveness and provide robust support for the cultivation of innovative engineering talents. Full article

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

The rapid integration of Generative Artificial Intelligence (GenAI) into programming education has raised important questions regarding its impact on learning processes, conceptual understanding, and technological dependency. This study analyzed the effects of four GenAI-supported instructional strategies in an introductory programming course for undergraduate engineering students. A multi-group quasi-experimental pre-test–post-test design was implemented involving 686 students distributed across 53 class groups, from 10 campuses, taught by 32 professors. The instructional conditions included Quizzes for Self-Regulation, Github-Copilot-assisted learning, Prompt Problems with Iterative Refinement, and Flipped Learning enhanced with GenAI, which were compared against a traditional teaching approach. Learning outcomes were measured using normalized learning gain, while statistical analyses were conducted using non-parametric methods due to deviations from normality and heteroscedasticity. Results indicate that GenAI integration did not produce statistically significant overall differences in learning gain when all GenAI-supported strategies were analyzed as a single cluster compared to traditional instruction. However, differences emerged between specific strategies, with Quizzes and Copilot-based approaches having higher median learning gains than Prompt Problems and Flipped Learning strategies. No statistically significant differences associated with gender were identified. These findings suggest that the effectiveness of GenAI in programming education depends less on the mere presence of the technology and more on the pedagogical conditions under which it is integrated into the teaching–learning process. Full article

Recent advancements in Large Language Models (LLMs) hold significant promise for reshaping programming education. However, critical instructional challenges such as high failure and dropout rates, insufficient feedback, and inadequate support for students’ independent analytical thinking remain prevalent. Addressing these gaps, this study introduces the Guided Programming and Analysis System (GPAS), an innovative educational approach leveraging LLM-based technology to enhance programming instruction. GPAS is designed to support autonomous learning processes by integrating multi-turn interactive thought guidance, code polishing, semantic annotation generation, and structured scoring mechanisms across multiple programming languages. Experimental results demonstrate significant effects were observed in correlation analysis with expert evaluations ($r=0.668$, $p=6.84\times {10}^{-12}$) and paired-sample tests on code and report-level improvements (effect sizes Cohen’s $d=0.92$ and $1.56$, respectively, $p=3.56\times {10}^{-6}$ and $p=1.64\times {10}^{-10}$). Additionally, findings revealed that the GPAS platform significantly improved code quality, particularly benefiting lower-achieving students, and effectively captured nuanced improvements in readability, structure, and boundary handling. Moreover, GPAS emphasizes the supportive role of educators, enabling them to focus more effectively on higher-order teaching tasks while the platform handles routine instructional feedback. Collectively, these results suggest that GPAS provides a promising framework for supporting learner-centered programming education and independent problem-solving processes. 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