Search Results (68)

Out-of-school tutoring, as a form of privatized compensatory education beyond formal schooling, has become increasingly prevalent, yet its role in fostering students’ holistic competencies remains insufficiently examined. Drawing on a student engagement perspective, this study investigates how different types of out-of-school tutoring, including academic, arts, and sports tutoring, are associated with the development of students’ holistic competencies. Data were drawn from a survey of 704 Grade 10 students in central China. Tutoring engagement during junior secondary school was measured using a self-developed Likert-scale instrument, while holistic competencies were obtained from official Comprehensive Quality Assessment records. The findings reveal differentiated effects across tutoring types. Academic tutoring shows no significant association with academic performance or other dimensions of holistic competence. In contrast, sports tutoring is positively associated with physical and mental health, and arts tutoring demonstrates a significant positive relationship with artistic literacy. Regarding engagement characteristics, simply increasing the number of programs or financial investment yields limited benefits. Instead, time investment and cognitive involvement in sports tutoring, as well as affective involvement in arts tutoring, are positively related to specific dimensions of holistic competence. These results suggest that the effectiveness of out-of-school tutoring depends less on participation amount and more on the nature of students’ engagement. The study highlights the uneven developmental returns of compensatory education and calls for a more balanced and development-oriented approach to tutoring participation. Full article

Although males typically outperform women on spatial tasks, we investigated whether structured training with 3D-printed heart models substantially reduces performance differences in echocardiographic education. A total of 144 medical students (95 female, 49 male) were enrolled in an interventional study via propensity score matching (n = 80: 40 female, 40 male). They received standardized echocardiography training with physical 3D models, tutoring, PowerPoint instruction, and self-directed practice. All measured outcomes showed significant improvements (p < 0.001) but no sex-related differences: 2D visualization (females: +2.30; males: +2.55; p = 0.738), 3D visualization (females: +3.52; males: +3.23; p = 0.661), and visual thinking (females: +5.83; males: +5.78; p = 0.961). Notably, females required more time before and after the intervention (post-test: f: $13.12\pm 3.87$ min vs. m: $10.69\pm 3.86$; p = 0.006) and rated spatial tasks as more difficult (f: $4.2\pm 0.85$ vs. m: $3.62\pm 1.14$; p = 0.0016). Yet they achieved identical objective results. Both groups rated the 3D models as highly effective (4.65 ± 0.86). These findings demonstrate that multi-modal anatomy training with physical 3D-printed models narrows sex differences in spatial learning outcomes and enables equitable development of anatomical visualization skills through appropriate educational scaffolding. However, process-related differences remain. Full article

Background/Objectives: Evaluation of students in practicums is essential in their training process. Mobile technologies enable formative assessments in training, enhance feedback, and improve students’ clinical competence and self-efficacy. Nevertheless, in the absence of previous evidence, their effects on clinical learning must be evaluated with rigor and caution. We aimed to evaluate the improvement in nursing students’ clinical competence and self-efficacy during their clinical practicums using the Evaluation Nursing Student (EVENS) application. Methods: A quasi-experimental design with non-equivalent control and intervention groups was adopted. Participants were not randomly assigned. The inclusion criterion was enrolment for the Supervised Practicum II course in the Nursing degree course at University X. Students agreeing to use the EVENS application during their Supervised Practicum II were assigned to the intervention group. The primary outcomes were student competence and self-efficacy, and the secondary outcome was the usability of the application. The analysis included a comparison of the pre- and post-intervention means of the intervention and control groups using Student’s t-tests. Results: One hundred and forty-nine mostly female (n = 137, 91.9%) students participated in the study. Forty-eight were assigned to the intervention group and 101 to the control group. No statistically significant differences regarding clinical competence or self-efficacy were found between the groups. Tutors rated the application’s usability with an average of 3.8 out of 5. Conclusions: The use of the EVENS application did not improve the primary outcomes. Although it was positively received by tutors as supportive of their role in training students engaged in clinical practicums. Full article

With the development of the digital intelligence era, generative AI is being widely used in scientific research, and its impact on graduate students’ research competence has attracted much attention from the academic community. Based on cognitive distribution theory and self-efficacy theory, this study classifies AI applications into three levels from basic to advanced—technical support AI use, text development AI use, and transformation AI use—explores their effects on graduate students’ research competence, and examines the mediating effect of critical thinking and the moderating effect of research self-efficacy. The results of the empirical analysis show that all three types of AI use behaviors are significantly correlated with research competence, with the strongest correlation for text development type and the weakest for technical support type. In the relationship between the three types of AI use behaviors and research competence, critical thinking plays a significant positive mediating role, and research self-efficacy plays a significant moderating role. Universities and tutors should guide students to focus on higher-order AI use behaviors in the text development and transformation categories, promoting the use of critical thinking to avoid technology misuse and improving research self-efficacy to help students accumulate confidence and support their research. Full article

Limited study time constrains university EFL vocabulary learning, so efficiency should be evaluated alongside accuracy. A web-based multimodal environment was developed that uses a large language model for contextualized drills and tutoring, text-to-speech for pronunciation and listening rehearsal, and an interactive 3D mastery view for self-regulated tracking. Vocabulary knowledge is modeled as a discrete mastery state (m = 0–5), updated after each attempt, and an adaptive scheduler allocates practice across mastery strata. Learning ROI is defined as newly mastered words per hour and computed from logged study time and mastery transitions. In a three-month deployment (N = 171), learners achieved a mean ROI of 9.8 words/hour, about 60% higher than conventional estimates (5–6 words/hour); high-adherence users reached 17–21 words/hour. End-of-trial surprise review results indicated retention above 85%. For CET-4, the platform cohort obtained the highest mean score (457.66) and pass rate (74.24%) compared with Baicizhan (442.22; 64.81%) and traditional instruction (428.60; 53.70%). The results provide quantitative support for the hypothesis that multimodal personalization improves time-based vocabulary gains and their durability. Full article

Intelligent Tutoring Systems are increasingly used in higher education to support personalized learning and academic monitoring in large-scale digital environments. However, existing systems are predominantly based on static architecture and rigid rule-based mechanisms, which limit scalability and hinder effective adaptation to heterogeneous learners, evolving learning behaviors, and real-world educational contexts. This paper presents a self-adaptive multi-agent architecture based on Reinforcement Learning for autonomous decision-making in intelligent systems deployed in real environments. The proposal integrates an RL Meta-Agent that dynamically optimizes the selection of specialized agents through an intelligent switching mechanism, considering the user’s state, behavior, and interaction patterns. The architecture was implemented in Moodle using flows orchestrated in n8n, LLMs, databases, APIs developed in Django, and real academic data. For the empirical evaluation, a real and a simulated case study were designed. A questionnaire was administered to university students, considering dimensions of usability, satisfaction and usefulness, and accessibility and interaction, to understand the perception of the system and improvements. The quantitative data were analyzed using descriptive statistics and nonparametric tests (Mann–Whitney U and Kruskal–Wallis), while the qualitative data were examined using thematic categorization. A simulated case study was conducted to analyze the behavior of the system. The results show that the RL Meta-Agent significantly improves system efficiency, response relevance, and adaptive agent selection, demonstrating that self-adaptive RL-based MAS architectures are a viable solution for intelligent systems applied in real-world contexts, providing empirical evidence of their performance and adaptability in complex scenarios such as higher education. Full article

As artificial intelligence (AI) is increasingly used in classrooms, tutoring systems, and learning platforms, it is essential that these tools are not only powerful, but also easy to understand, fair, and supportive of real learning. Many current AI systems can generate fluent responses or accurate predictions, yet they often fail to clearly explain their decisions, reflect students’ cultural contexts, or give learners and educators meaningful control. This gap can reduce trust and limit the educational value of AI-supported learning. This paper introduces the PEARL framework, a human-centered approach for designing and evaluating explainable AI in education. PEARL is built around five core principles: Pedagogical Personalization (adapting support to learners’ levels and curriculum goals), Explainability and Engagement (providing clear, motivating explanations in everyday language), Attribution and Accountability (making AI decisions traceable and justifiable), Representation and Reflection (supporting fairness, diversity, and learner self-reflection), and Localized Learner Agency (giving learners control over how AI explains and supports them). Unlike many existing explainability approaches that focus mainly on technical performance, PEARL emphasizes how students, teachers, and administrators experience and make sense of AI decisions. The framework is demonstrated through simulated examples using an AI-based tutoring system, showing how PEARL can improve feedback clarity, support different stakeholder needs, reduce bias, and promote culturally relevant learning. The paper also introduces the PEARL Composite Score, a practical evaluation tool that helps assess how well educational AI systems align with ethical, pedagogical, and human-centered principles. This study includes a small exploratory mixed-methods user study (N = 17) evaluating example AI tutor interactions; no live classroom deployment was conducted. Together, these contributions offer a practical roadmap for building educational AI systems that are not only effective, but also trustworthy, inclusive, and genuinely supportive of human learning. Full article

Background: Nurses’ teaching skills in the clinical setting are crucial to ensuring that students receive high-quality training. Despite the growing importance of competency frameworks, there is little research on the relationship between nurses’ teaching competence and sociodemographic, professional, training, and research variables. Methods: This was a cross-sectional, descriptive, and correlational study conducted at nine hospitals linked to the clinical placement subjects of the Bachelor of Nursing of the University of Barcelona. The study population comprised all nurses directly involved in clinical teaching. Participants’ level of self-reported teaching competence was evaluated using the Spanish version of the Capabilities of Nurse Educators (S-CONE) questionnaire, and the sociodemographic, professional, and academic variables were collected in an ad hoc questionnaire. Descriptive statistics, non-parametric tests, and linear and logistic regression models were used to analyse the associations between the S-CONE total score and the variables collected. Results: The mean age of the participants (n = 596) was 41.9 years (standard deviation: 8.82), and 85.6% of them were women (n = 510). The overall mean S-CONE score was 3.81 (SD: 0.73). Higher scores were observed in those with advanced academic degrees, formal teacher training, and participation in academic activities. Professionals with mixed roles (clinical mentor and academic tutor) self-reported significantly higher competence levels. Multivariate analyses identified participation in conferences, tutoring of undergraduate theses, and involvement in research or development projects as the main predictors of higher teaching competence as measured by the S-CONE questionnaire. The lowest-scoring factor was research and evidence, which points to a potential area for improvement. No significant associations were found with age, sex, or years of clinical experience. Conclusions: Participants had a high self-reported level of teaching competence and rated themselves as competent overall, especially in professional practice and curriculum design. However, we identified areas for improvement related to pedagogical innovation and the use of evidence. The findings reinforce the importance of professional development and academic involvement to strengthen teacher competence. Full article

Generative AI is reshaping k–12 learning as a multi-agent system in which goals, activities, and roles co-evolve across formal and informal environments. Following PRISMA and appraising quality with MMAT, we synthesize 84 peer-reviewed empirical studies (2020–2025) involving learners aged 3–18. Using Biggs’s 3P model as a systems lens and embedding CIMO logic, we code learning objectives, activity designs, AI role paradigms, and outcomes. Seven recurring objectives emerge (language/literacy; STEM; creativity; socioemotional skills; feedback literacy and self-regulation; motivation; AI literacy). Five dominant activity patterns are identified: dialogic tutoring and formative feedback, generative iterative co-creation, project-based problem-solving, simulation/game-based learning, and assessment support. Across studies, AI roles shift from AI-directed to AI-supported/empowered, re-allocating agency among students, teachers, and caregivers via feedback loops. Reported outcomes span three categories—epistemic, practice, and affective/identity—with opportunities of deeper knowledge, improved practice, and stronger engagement, and risks of hallucinations, reduced originality, over-reliance, motivational loss, and ethical concerns. We propose a goal–activity–role alignment heuristic for instructional design, plus safeguards around teacher professional development, feedback literacy, and ethics. We call for longitudinal and cross-cultural research to evaluate the impacts of GenAI in k–12. Full article

Artificial Intelligence (AI) tools have transformed academic writing and literacy development in higher education. Students can now receive instant feedback on grammar, coherence, style, and argumentation using AI-powered writing assistants, like Grammarly, ChatGPT, and QuillBot. Moreover, these writing assistants can quickly produce completed essays and papers, leaving little else for the student to do aside from reading and perhaps editing the content. Many teachers are concerned that this erodes critical thinking skills and undermines ethical considerations since students are not performing the work themselves. This study addresses this concern by synthesizing and evaluating peer-reviewed literature on the effectiveness of AI in supporting writing pedagogy. Studies were selected based on their relevance and scholarly merit, following the Scale for the Assessment of Narrative Review Articles (SANRA) guidelines to ensure methodological rigor and quality. The findings reveal that although AI tools can be detrimental to the development of writing skills, they can foster self-directed learning and improvement when carefully integrated into coursework. They can facilitate enhanced writing fluency, offer personalized tutoring, and reduce the cognitive load of drafting and revising. This study also compares AI-assisted and traditional writing approaches and discusses best practices for integrating AI tools into curricula while preserving academic integrity and creativity in student writing. 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

As digital transformation continues to redefine education, understanding how emerging technologies can enhance self-directed learning (SDL) becomes essential for learners, educators, instructional designers, and policymakers, as this approach supports personalized learning, strengthens student autonomy, and responds to the demands of more flexible and dynamic educational environments. This systematic review examines how artificial intelligence (AI) tools enhance SDL by offering personalized, adaptive, and real-time support for learners in online environments. Following the PRISMA 2020 methodology, a literature search was conducted to identify relevant studies published between 2020 and 2025. After applying inclusion, exclusion, and quality criteria, 77 studies were selected for in-depth analysis. The findings indicate that AI-powered tools such as intelligent tutoring systems, chatbots, conversational agents, and natural language processing applications promote learner autonomy, enable self-regulation, provide real-time feedback, and support individualized learning paths. However, several challenges persist, including overreliance on technology, cognitive overload, and diminished human interaction. These insights suggest that, while AI plays a transformative role in the evolution of education, its integration must be guided by thoughtful pedagogical design, ethical considerations, and a learner-centered approach to fully support the future of education through the internet. Full article

The integration of digital serious games into speech learning (rehabilitation) has demonstrated significant potential in enhancing accessibility and inclusivity for children with speech disabilities. This review of the state of the art examines the role of serious games, Artificial Intelligence (AI), and Natural Language Processing (NLP) in speech rehabilitation, with a particular focus on interaction modalities, engagement autonomy, and motivation. We have reviewed 45 selected studies. Our key findings show how intelligent tutoring systems, adaptive voice-based interfaces, and gamified speech interventions can empower children to engage in self-directed speech learning, reducing dependence on therapists and caregivers. The diversity of interaction modalities, including speech recognition, phoneme-based exercises, and multimodal feedback, demonstrates how AI and Assistive Technology (AT) can personalise learning experiences to accommodate diverse needs. Furthermore, the incorporation of gamification strategies, such as reward systems and adaptive difficulty levels, has been shown to enhance children’s motivation and long-term participation in speech rehabilitation. The gaps identified show that despite advancements, challenges remain in achieving universal accessibility, particularly regarding speech recognition accuracy, multilingual support, and accessibility for users with multiple disabilities. This review advocates for interdisciplinary collaboration across educational technology, special education, cognitive science, and human–computer interaction (HCI). Our work contributes to the ongoing discourse on lifelong inclusive education, reinforcing the potential of AI-driven serious games as transformative tools for bridging learning gaps and promoting speech rehabilitation beyond clinical environments. Full article

(1) Background: Structured use of feedback and self-reflection in simulated counselling sessions has a number of benefits, including identification of strategies for improvement, improvement in key skills and adaptability and a patient-centred approach which will help them to succeed as effective healthcare practitioners. The aim of this study was therefore to explore students’ perceptions of self-reflection and feedback in patient counselling simulations and the development of patient counselling skills; (2) Methods: Focus groups explored student perceptions of how the combination of self-reflection, self-assessment and teacher and peer feedback impacted their performance in simulated patient counselling assessments; (3) Results: Four focus groups with 21 pharmacy students were conducted. We identified three main themes and associated subthemes: consistency and continuity (sub-themes learning through repetitive assessment and inconsistent expectations), perceptions of feedback (sub-themes tutor feedback, peer feedback and self-reflection) and real-life practice (sub-themes authenticity of simulation cases, perceptions of empathy and professional development); (4) Conclusions: This study highlights the critical role of integrating consistent, high-quality feedback, peer assessment, and self-reflection in pharmacy education to enhance students’ learning experiences and prepare them for professional practice. As workplace-based assessment becomes more common and expected by accreditation bodies, these insights underscore the need for structured and continuous feedback processes to be integrated into all areas of pharmacy curricula. Full article

As recent advancements in large language models enhance reasoning across various domains, educators are increasingly exploring their use in conversation-based tutoring systems. However, since LLMs are black-box models to users and lack human-like problem-solving strategies, users are hardly convinced by the answers provided by LLMs. This lack of trust can potentially undermine the effectiveness of learning in educational scenarios. To address these issues, we introduce a novel approach that integrates multi-agent debates into a lecture video Q&A system, aiming to assist computer science (CS) university students in self-learning by using LLMs to simulate debates between affirmative and negative debaters and a judge to reach a final answer and presenting the entire process to users for review. This approach is expected to lead to better learning outcomes and the improvement of students’ critical thinking. To validate the effectiveness of this approach, we carried out a user study through a prototype system and conducted preliminary experiments based on video lecture learning involving 90 CS students from three universities. The study compared different conditions and demonstrated that students who had access to a combination of video-based Q&A and multi-agent debates performed significantly better on quizzes compared to those who only had access to the video or video-based Q&A. These findings indicate that integrating multi-agent debates with lecture videos can substantially enhance the learning experience, which is also beneficial for the development of students’ high-order thinking abilities in the future. Full article