Luh Devi Herliandry, N. Nurhasanah, Maria Enjelina Suban
et al.
The Pandemic COVID-19 has changed various aspects of human life today, especially in education. This requires all elements of education to adapt and continue the rest of the semester. The purpose of this study as a general review of learning during the COVID-19 pandemic. This research uses descriptive content analysis study method. The analysis was carried out on international, national articles and similar sources related to learning solutions during the pandemic. Online learning is an effective solution for activating classrooms even though schools have closed because time and place are at risk during this pandemic. However, this learning technique is important to be evaluated according to local conditions given the distribution of facilities and the ability of parents to provide different online learning facilities to students in Indonesia.
This study aims to systematically review research trends regarding the application of artificial intelligence (AI) in general English education at Korean universities and suggest future directions for transitioning from a technology-centered paradigm to learner-centered education. Following the PRISMA guidelines, 50 articles published in domestic academic journals from 2019 to 2025 were selected for the final analysis. The results indicate that research has expanded rapidly since the emergence of ChatGPT in late 2022, primarily focusing on enhancing grammatical accuracy, lexical diversity, and logical organization in English writing. Methodologically, while mixed-methods research has been actively conducted, longitudinal approaches to investigate long-term language acquisition effects remain relatively scarce. Although AI’s immediate and personalized feedback was identified as a key strength, challenges such as information distortion (hallucination), technological over- dependence, and the weakening of critical thinking were highlighted as critical tasks to be addressed. Based on these findings, this study suggests: expanding research across all four language skills, reconceptualizing AI as a “learning partner” that extends cognitive abilities, and redefining the instructor's role as a “hybrid strategy designer” and “learning coach equipped with data literacy.” This study provides foundational data with academic and practical value for designing future AI-integrated general English curricula.
This article provides an overview of the research method meta-analysis as a tool for general music advocacy. “Why music education?” is framed as underlying advocacy efforts, noting a historical duality between musical outcomes and nonmusical outcomes. A vignette is provided to show how meta-analyses were used to impact legislation on general music programs in the state of Florida. To further promote advocacy, a breakdown of how to understand a meta-analysis is given. Then, results from potential meta-analyses for advocacy platforms are shared which empirically validate the positive effects of music participation on cognitive, social, and emotional outcomes with children. Implications suggest that while “Why music education?” is rarely tied to extramusical outcomes from a philosophical or phenomenological perspective, there are instances when policy or budget is beholden to demonstrating the potential for nonmusical outcomes. In these situations, there is empirical evidence to support advocacy for general music programs.
Salih Gülen, İsmail Dönmez, Fatma Betül Şengönül
et al.
STEM education aims to develop 21st-century skills, support economic growth and promote gender equality in STEM fields. It is known that gender stereotypes play a significant role in the formation of STEM identity. The most important factor preventing some high school-level female students from pursuing STEM careers is their lack of participation in STEM activities. Female students in high schools have limited opportunities to explore or learn about STEM careers due to the emphasis on verbal and religious courses in their curriculum. However, it is known that women can work more autonomously in scientific activities compared to men. The current study examines the effect of robotics and coding education on the development of girls’ STEM careers. The study was conducted at an all-girls high school in Turkey, where the curriculum is predominantly centered on verbal and religious subjects. In the study, a pre-test and post-test experimental design with control group was used. A total of 76 volunteer female students (34 in the experimental group and 42 in the control group) participated in robotics and coding education over a period of 12 weeks. The data were collected using the validated STEM career, motivation and attitude scales and analyzed using t -tests, ANOVA and Pearson correlation. The findings revealed that robotics and coding training significantly improved the participants’ STEM career aspirations, attitudes and motivations. A strong positive correlation was found between career interest, attitude and motivation. The study also showed that STEM career scores are significantly higher among students who wish to become teachers compared to those considering a career in the fields of health or engineering. However, no significant correlation was found between the participants’ parents’ education levels, family income and STEM career aspirations.
History of scholarship and learning. The humanities, Social Sciences
Peer grading has emerged as a scalable solution for assessment in large and online classrooms, offering both logistical efficiency and pedagogical value. However, designing effective peer-grading systems remains challenging due to persistent concerns around accuracy, fairness, reliability, and student engagement. This paper presents a systematic review of 122 peer-reviewed studies on peer grading spanning over four decades. Drawing from this literature, we propose a comprehensive taxonomy that organizes peer grading systems along two key dimensions: (1) evaluation approaches and (2) reviewer weighting strategies. We analyze how different design choices impact grading accuracy, fairness, student workload, and learning outcomes. Our findings highlight the strengths and limitations of each method. Notably, we found that formative feedback -- often regarded as the most valuable aspect of peer assessment -- is seldom incorporated as a quality-based weighting factor in summative grade synthesis techniques. Furthermore, no single reviewer weighting strategy proves universally optimal; each has its trade-offs. Hybrid strategies that combine multiple techniques could show the greatest promise. Our taxonomy offers a practical framework for educators and researchers aiming to design peer grading systems that are accurate, equitable, and pedagogically meaningful.
We present the International Particle Physics Outreach Group (IPPOG), a global network dedi- cated to connecting students, educators, and the general public with the world of particle physics. In this paper, we outline the need to bridge the existing gap between the particle physics community and the wider audience, and we present the solutions that IPPOG has implemented to overcome it through three pillar Activities: the International Masterclasses and the Global Cosmics hands-on activities network, which have engaged together over 200 000 high-school students to date, and the curation of an Outreach Resource Database and web portal.
Yiming Luo, Ting Liu, Patrick Cheong-Iao Pang
et al.
The advent of Large Language Models (LLMs) has profoundly transformed the paradigms of information retrieval and problem-solving, enabling students to access information acquisition more efficiently to support learning. However, there is currently a lack of standardized evaluation frameworks that guide learners in effectively leveraging LLMs. This paper proposes an LLM-driven Bloom's Educational Taxonomy that aims to recognize and evaluate students' information literacy (IL) with LLMs, and to formalize and guide students practice-based activities of using LLMs to solve complex problems. The framework delineates the IL corresponding to the cognitive abilities required to use LLM into two distinct stages: Exploration & Action and Creation & Metacognition. It further subdivides these into seven phases: Perceiving, Searching, Reasoning, Interacting, Evaluating, Organizing, and Curating. Through the case presentation, the analysis demonstrates the framework's applicability and feasibility, supporting its role in fostering IL among students with varying levels of prior knowledge. This framework fills the existing gap in the analysis of LLM usage frameworks and provides theoretical support for guiding learners to improve IL.
This report summarizes the outcomes of a two-day international scoping workshop on the role of artificial intelligence (AI) in science education research. As AI rapidly reshapes scientific practice, classroom learning, and research methods, the field faces both new opportunities and significant challenges. The report clarifies key AI concepts to reduce ambiguity and reviews evidence of how AI influences scientific work, teaching practices, and disciplinary learning. It identifies how AI intersects with major areas of science education research, including curriculum development, assessment, epistemic cognition, inclusion, and teacher professional development, highlighting cases where AI can support human reasoning and cases where it may introduce risks to equity or validity. The report also examines how AI is transforming methodological approaches across quantitative, qualitative, ethnographic, and design-based traditions, giving rise to hybrid forms of analysis that combine human and computational strengths. To guide responsible integration, a systems-thinking heuristic is introduced that helps researchers consider stakeholder needs, potential risks, and ethical constraints. The report concludes with actionable recommendations for training, infrastructure, and standards, along with guidance for funders, policymakers, professional organizations, and academic departments. The goal is to support principled and methodologically sound use of AI in science education research.