Search Results (551)

The application of artificial intelligence in education, particularly in learning programming, is gaining increasing significance. However, research on secondary school students specializing in IT at an early stage has received relatively little attention in this field. The aim of this study is to assess how vocational secondary school IT students utilize Generative artificial intelligence in learning programming. The study employed a survey-based methodology, where students with varying levels of knowledge were surveyed to understand their AI usage patterns. The sample consisted of students from vocational IT schools, and data were analyzed using descriptive statistics and independent samples t-tests. The results indicate that students with different levels of knowledge use AI tools differently, with ChatGPT being the most popular tool. The study further highlights that AI usage brings significant benefits, such as providing a personalized learning experience and enabling quick error correction. However, excessive reliance on AI tools may hinder students from acquiring fundamental programming skills. The findings support the idea that while AI can effectively complement teachers’ explanations, overdependence on it can be risky, potentially reducing students’ creativity and problem-solving abilities. The study emphasizes the crucial role of educators in teaching the responsible and ethical use of artificial intelligence. The results of this research offer new perspectives on the effective integration of Generative artificial intelligence into vocational secondary school programming education and suggest further studies to compare its applications at the university level. However, the study acknowledges certain limitations, such as the potential bias of self-reported data, which may affect the generalizability of the results. Unlike other studies, the age groups we surveyed, and the cohorts formed from them are nearly evenly distributed, making our sample representative of the region in question. Full article

(1) Background: Medical education is moving from a cognition-based to a competency-based model in Germany. Traditional learning activities (LAs) are questioned. Some stakeholders criticise traditional LAs for not facilitating deep learning or operational competency transfer required in practical contexts. This qualitative study aims to take a closer look at the role of lectures in competency-based medical education from a student’s point of view. (2) Methods: Three semi-structured group interviews were held with students from the family medicine curriculum in the summer semester of 2021. Questions focused on the three lectures in this family medicine curriculum and on students’ experiences with lectures in general. One additional expert interview was held with one of the lecturers. The video-recorded interviews were transcribed verbatim and analysed using content analysis. (3) Results: Interview participants highlighted entertainment, the provision of a social and physical learning environment, and the completion of knowledge from books and educational websites as important roles of lectures. Lectures on demand were used by interviewees for time- and space-independent repetition. Lecturer-dependent qualitative differences between lectures were identified by interviewees. Important differences were the extent of interaction, as well as the enthusiasm and preparation of the lecturer. (4) Conclusions: Even though literature suggests that lectures may be a less effective learning activity, under certain circumstances, several aspects make them an essential element of modern curriculum development. By raising interest in a subject, providing a space for discussion and social interaction, interactive lectures appear to be a helpful link between knowledge acquisition and practical training of competencies. Full article

With the rapid evolution of technology and the continuous deepening of digital transformation in education, personalized and adaptive learning have emerged as inevitable trends in the educational landscape. This study focuses on a Computerized Adaptive Learning System Based on Cognitive Diagnosis (CAL-CDS)—an integrated platform that incorporates multiple technologies for assessment and learning. The study is organized around two dimensions: (1) constructing a foundational cognitive diagnostic assessment framework, and (2) investigating the operational mechanisms of the cognitive diagnosis-based computerized adaptive system. It comprehensively incorporates core components including cognitive modeling, Q-matrix generation, and diagnostic test development. On this basis, this study dissects the system’s operational logic from four aspects: the adaptive testing system, diagnostic system, recommendation system, and empirical case studies. This study effectively addresses two core questions: how to construct a cognitive diagnostic assessment framework that alignes with China’s mathematics knowledge structure, and how to facilitate personalized student learning via cognitive diagnosis. Overall, this study offers a systematic solution for developing mathematics-specific cognitive diagnosis-driven adaptive learning systems. Full article

The purpose of this research is to investigate the misconceptions related to geological concepts among Secondary Education students in the region of Achaia, Greece. The study focuses on both Lower Secondary Education (Gymnasium, grades 7–9) and Upper Secondary Education, including General and Vocational Education (grades 10–12). Previous research has shown that students entering Lower Secondary Education or High School often possess several misconceptions about geological concepts. These misconceptions result in a fragmented or incorrect understanding, which may arise from intuitive perceptions of how the natural world evolves that are incorrect, or from stereotypes and assumptions acquired from the family environment or inadequacies in the school curriculum. Despite teachers’ efforts to clarify these concepts, a significant percentage of students continue to hold misconceptions, mainly related to minerals and rocks. A total of 1065 secondary students completed an online closed-ended questionnaire that was designed and validated based on previous research findings to highlight their misconceptions. This study results showed a clear differentiation between students from urban and rural areas, while demographic characteristics (such as gender, age, parents’ occupation, and parents’ marital status) did not appear to play a significant role. In addition, the responses to specific sets of questions varied depending on the student’s grade level. Identifying students’ misconceptions can support the development of appropriate educational tools and/or inform targeted interventions that aim to clarify these concepts and correct any incorrect assumptions. Full article

One of the greatest benefits of inclusion in general physical education (PE) is the opportunity for social interactions between students with and without disabilities. Unfortunately, interviews with students with disabilities who have participated in PE often find that social interactions with students without disabilities were limited. A model that promotes interaction between students with and without disabilities in PE is Special Olympics Unified Physical Education (UPE). In UPE, students with and without disabilities participate in activities together rather than the one-way focus on traditional peer tutoring. There have been anecdotal reports on the positive benefits of UPE for both students with and without disabilities. To date, there has been no published research on UPE. Additionally, some question how UPE has been implemented in schools, specifically questioning if UPE provided quality PE and whether students with disabilities were forced into UPE and denied opportunities to participate in general PE. The purpose of this qualitative study was to better understand how UPE was developed and implemented in select U.S. schools and the impact on social interactions between students with and without disabilities. Interviews with twelve teachers who were directly involved in their UPE programs revealed the following four major themes: (1) our students were not being served appropriately, (2) a mix of PE standards and Special Olympics programming, (3) UPE is a choice, and (4) our students improved in many ways. The discussion examined the results in relation to the criticisms of UPE and how UPE proved to be a positive alternative to limited social interactions in general PE. Full article

The rapid integration of generative AI, particularly ChatGPT, into academic settings has prompted urgent questions regarding its impact on students’ psychological and academic outcomes. Although generative AI holds considerable potential to transform educational practices, its effects on individual traits such as self-control and academic wellbeing remain insufficiently explored. This study addresses this gap through a sequential two-phase design. In the first phase, the ChatGPT Usage Scale was adapted and validated for a Turkish university student population (N = 413). Using confirmatory factor analysis and item response theory, the scale was confirmed as a psychometrically valid and reliable one-factor instrument. In the second phase, a separate sample (N = 449) was used to examine the relationships between ChatGPT usage, self-control, and academic wellbeing through a mediation model. The findings revealed that higher ChatGPT usage was significantly associated with lower levels of both self-control and academic wellbeing. Additionally, mediation analysis demonstrated that self-control partially mediates the negative relationship between ChatGPT usage and academic wellbeing. The study concludes that while generative AI tools are valuable, their integration into education presents a double-edged sword, highlighting the critical need to foster students’ self-regulatory skills to ensure they can harness these tools responsibly without compromising their academic and psychological health. Full article

The rapid advancement of AI technologies and the emergence of large language models (LLMs) such as ChatGPT have facilitated the integration of intelligent question-answering systems into education. However, students often hesitate to ask questions, which negatively affects learning outcomes. To address this issue, this study proposes a closed, locally deployed generative AI teaching assistant system that enables instructors to upload course PDFs to generate customized Q&A platforms. The system is based on a Retrieval-Augmented Generation (RAG) architecture and was developed through a comparative evaluation of components, including open-source large language models, embedding models, and vector databases to determine the optimal setup. The implementation integrates RAG with responsive web technologies and is evaluated using a standardized test question bank. Experimental results demonstrate that the system achieves an average answer accuracy of up to 86%, indicating a strong performance in an educational context. These findings suggest the feasibility of the system as an effective, privacy-preserving AI teaching aid, offering a scalable technical solution to improve digital learning in on-premise environments. Full article

General large language models (LLMs) often suffer from semantic misinterpretation, information redundancy, and hallucinated content when applied to educational question-answering tasks. These issues hinder their effectiveness in supporting students’ specialized course learning and self-directed study. To address these challenges, this study proposes an intelligent tutoring model that integrates a knowledge graph with a large language model (KG-CQ). Focusing on the Data Structures (C Language) course, the model constructs a course-specific knowledge graph stored in a Neo4j graph database. It incorporates modules for knowledge retrieval, domain-specific question answering, and knowledge extraction, forming a closed-loop system designed to enhance semantic comprehension and domain adaptability. A total of 30 students majoring in Educational Technology at H University were randomly assigned to either an experimental group or a control group, with 15 students in each. The experimental group utilized the KG-CQ model during the answering process, while the control group relied on traditional learning methods. A total of 1515 data points were collected. Experimental results show that the KG-CQ model performs well in both answer accuracy and domain relevance, accompanied by high levels of student satisfaction. The model effectively promotes self-directed learning and provides a valuable reference for the development of knowledge-enhanced question-answering systems in educational settings. Full article

Mastering client-side Web programming is essential for the development of responsive and interactive Web applications. To support novice students’ self-study, in this paper, we propose a novel exercise format called the phrase fill-in-blank problem (PFP) in the Web Programming Learning Assistant System (WPLAS). A PFP instance presents a source code with blanked phrases (a set of elements) and corresponding Web page screenshots. Then, it requests the user to fill in the blanks, and the answers are automatically evaluated through string matching with predefined correct answers. By increasing blanks, PFP can come close to writing a code from scratch. To facilitate scalable and context-aware question creation, we implemented the PFP instance generation algorithm in Python using regular expressions. This approach targets meaningful code segments in HTML, CSS, and JavaScript that reflect the interactive behavior of front-end development. For evaluations, we generated 10 PFP instances for basic Web programming topics and 5 instances for video games and assigned them to students at Okayama University, Japan, and the State Polytechnic of Malang, Indonesia. Their solution results show that most students could solve them correctly, indicating the effectiveness and accessibility of the generated instances. In addition, we investigated the ability of generative AI, specifically ChatGPT, to solve the PFP instances. The results show 86.7% accuracy for basic-topic PFP instances. Although it still cannot fully find answers, we must monitor progress carefully. In future work, we will enhance PFP in WPLAS to handle non-unique answers by improving answer validation for flexible recognition of equivalent responses. Full article

The integration of Artificial Intelligence (AI) in education has raised important questions about its role in supporting inclusive practices, particularly in special education. This qualitative-dominant study with quantitative support examines how special education teachers in inclusive primary classrooms in Greece use ChatGPT to design Individualized Education Programs (IEPs) for students with learning disabilities. Six teachers participated, with some employing ChatGPT and others relying on traditional methods. The quality of IEP goals was described using the Revised IEP/IFSP Goals and Objectives Rating Instrument (R-GORI), while in-depth teacher perspectives were explored through thematic analysis. Findings suggest that ChatGPT contributed to clearer goal-setting, generation of diverse instructional resources, and more structured lesson planning. However, teachers emphasized the need for critical oversight, adaptation to real-world classroom conditions, and safeguarding the relational and emotional aspects of teaching. Participants expressed cautious optimism, viewing ChatGPT as a valuable support tool when integrated thoughtfully and ethically. These context-specific, exploratory results offer preliminary guidance for educators, policymakers, and researchers seeking to integrate AI tools into special education. They highlight the importance of targeted professional development, ethical safeguards, and further large-scale research to evaluate the broader applicability of AI-assisted IEP planning. Full article

Traditional educational assessment systems suffer from inefficient question selection strategies that fail to optimally probe student knowledge while requiring extensive testing time. We present a novel hierarchical probabilistic neural framework that integrates Bayesian inference with symmetric deep neural architectures to enable adaptive, efficient knowledge assessment. Our method models student knowledge as latent representations within a graph-structured concept dependency network, where probabilistic mastery states, updated through variational inference, are encoded by symmetric graph properties and symmetric concept representations that preserve structural equivalences across similar knowledge configurations. The system employs a symmetric dual-network architecture: a concept embedding network that learns scale-invariant hierarchical knowledge representations from assessment data and a question selection network that optimizes symmetric information gain through deep reinforcement learning with symmetric reward structures. We introduce a novel uncertainty-aware objective function that leverages symmetric uncertainty measures to balance exploration of uncertain knowledge regions with exploitation of informative question patterns. The hierarchical structure captures both fine-grained concept mastery and broader domain understanding through multi-scale graph convolutions that preserve local graph symmetries and global structural invariances. Our symmetric information-theoretic method ensures balanced assessment strategies that maintain diagnostic equivalence across isomorphic concept subgraphs. Experimental validation on large-scale educational datasets demonstrates that our method achieves 76.3% diagnostic accuracy while reducing the question count by 35.1% compared to traditional assessments. The learned concept embeddings reveal interpretable knowledge structures with symmetric dependency patterns that align with pedagogical theory. Our work generalizes across domains and student populations through symmetric transfer learning mechanisms, providing a principled framework for intelligent tutoring systems and adaptive testing platforms. The integration of probabilistic reasoning with symmetric neural pattern recognition offers a robust solution to the fundamental trade-off between assessment efficiency and diagnostic precision in educational technology. Full article

In Chinese character learning, distinguishing similar characters is challenging for learners regardless of their proficiency. This is due to the complex orthography (visual word form) linking symbol, pronunciation, and meaning. Multimedia learning is a promising approach to implement learning strategies for Chinese characters. However, the availability of multimodal resources specifically designed for distinguishing similar Chinese characters is limited. With the advanced development of generative artificial intelligence (GenAI), we propose a practical framework for constructing multimodal resources, enabling flexible and semi-automated resource generation for Chinese character learning. The framework first constructs image illustrations due to their broad applicability across various learning contexts. After that, other four types of multimodal resources implementing learning strategies for similar character learning can be developed in the future, including summary slide, micro-video, self-test question, and basic information. An experiment was conducted with one group receiving the constructed multimodal resources and the other receiving the traditional text-based resources for similar character learning. We explored the participants’ learning performance, motivation, satisfaction, and attitudes. The results showed that the multimodal resources significantly improved performance on distinguishing simple characters, but were not suitable for non-homophones, i.e., visually similar characters with different pronunciations. Micro-videos introducing character formation knowledge significantly increased students’ learning motivation for character evolution and calligraphy. Overall, the resources received high satisfaction, especially for micro-videos and image illustrations. The findings regarding the effective design of multimodal resources for implementing learning strategies (e.g., using visual mnemonics, character formation knowledge, and group reviews) and implications for different Chinese character types are also discussed. Full article

In the realm of educational assessment, the integration of artificial intelligence (AI) offers a promising pathway for the development of robust evaluations. This study explores the application of AI chatbots in crafting and validating examinations tailored to road geometric design, while adhering to the principles of Bloom’s Taxonomy. Utilizing Gemini AI Studio, three distinct exam versions were generated, covering eight crucial topics within road geometric design. A panel of expert chatbots, including Chat GPT 3.5, Claude 3, Sonet, Copilot, Perplexity, and You, assessed the validity of the exam content. These chatbots achieved scores of 9.17 or higher, establishing their proficiency as experts. Subsequent evaluations focused on relevance and wording, revealing high scores for both metrics, indicating the adequacy of the assessment tools. The two remaining versions were administered to student groups enrolled in the Road Construction II course at the Universidad Técnica Particular de Loja. Only 1.2% of students reached Bloom’s Taxonomy level 3, with many questions deemed easy, leading to varying trends in cognitive levels. Comparative analysis of student scores revealed significant discrepancies between a previous “classic” exam. While AI shows potential in crafting valid assessments aligned with Bloom’s Taxonomy, greater human involvement is necessary to ensure high-quality instrument generation. Full article

Background: Midwifery’s Intangible Heritage was officially recognized by the United Nations Educational, Scientific and Cultural Organization on 6 December 2023, highlighting that elements of midwifery knowledge and practice, shaped over generations, constitute cultural heritage worth safeguarding. While previous studies have investigated midwives’ perceptions of professional identity, none have done so within the explicit framework of MIH. Objective: this study explored how midwives and student midwives in Cyprus perceive the intangible heritage of their profession and how it relates to their shared professional identity. Methods: A qualitative descriptive study was conducted between April and July 2023. Three focus groups were held, involving 22 participants: 15 registered midwives and 7 student midwives. A semi-structured interview guide consisting of 10 questions was used, developed by the lead author (M.P.) based on the literature and improvisation and finalized with the research team (E.H., S.V., N.M.) after expert input. Thematic analysis was performed inductively to identify recurrent themes. Results: Four major themes emerged: (1) key elements of Midwifery’s Intangible Heritage and their transmission across generations; (2) a sense of shared professional identity; (3) perceived threats to the midwifery profession; and (4) midwives’ expectations for the future of the profession. Conclusions: The findings reflect the historical background of midwifery in Cyprus and its contrast with contemporary practice, particularly within the context of the overmedicalization of birth and societal perceptions of midwifery in the socio-cultural setting. Safeguarding Midwifery’s Intangible Heritage requires both empowering women to seek midwifery-led care and enabling midwives to practice autonomously within their full professional scope. In addition, policymakers and educational bodies must support the preservation of midwives’ core skills through targeted educational curricula, structured mentorship, and continuous professional development. Full article

Based on our question, “Are reels/short-videos the real deal in video-based learning?” this study explores the effectiveness of short (around 2 min) video-based learning in engaging medical students from the second large medical Portuguese school. With the increasing integration of digital tools in education, video content has emerged as a dynamic method to enhance learning experiences. This cross-sectional survey was conducted by using anonymous self-administered questionnaires, prepared with reference to previous studies, and distributed to 264 informed students who voluntarily agreed to participate. This sample represented 75.5% of the students attending the classes. The questionnaires included topics related to the 65 short videos about practical classes, as well as the students’ learning preferences. The collected data were analyzed using descriptive and comparative statistics. The students considered that the content and format of the videos were adequate (99.6% and 100%, respectively). Specifically, the videos helped the students to better understand the practical classes, consolidate and retain the practical content, and simplify the study for the exams. Additionally, the videos were praised for their high-quality audiovisual content, being innovative, complete, concise, short and/or adequate, or better than other formats such as printed information. The combination of written and audiovisual support materials for teaching and studying is important and has been shown to improve students’ performance. This pedagogical methodology is well-suited for the current generation of students, aiding not only in study and exam preparation but also in remote learning. Full article