Hasil untuk "Education"

Jie Gao, Jiayi Zhang, Dan Chen

While Generative AI (GenAI) rapidly integrated into higher education, existing research has primarily focused on regulating student use. As a result, student perspectives on faculty adoption of GenAI remained unexplored. In this study, we analyzed survey responses from 156 undergraduate and graduate students to examine their attitudes toward both student and faculty use of GenAI. We classified students into four groups based on their attitudes, including GenAI Optimists, Student Support Group, Faculty Support Group, and Non-supporters. Findings show that 37% of participants do not support GenAI use by either students or faculty, while 31% support GenAI use in both contexts. We also conducted thematic analysis to understand participants' concerns on faculty GenAI usage. Results revealed that (1) a majority of students (79%) questioned the validity and reliability of GenAI-generated responses, and (2) 37% of students feared that faculty overreliance on GenAI created a "futile cycle" that might reduce faculty critical thinking. Our findings showed that students expressed concerns about GenAI use by faculty in teaching and grading contexts, with pedagogical concerns being most prominent. These findings informed the future use of GenAI in teaching and learning in higher education.

Shaouna Shoaib Lodhi, Shoaib Lodhi

The rapid integration of Artificial Intelligence (AI) into K-12 STEM education presents transformative opportunities alongside significant ethical challenges. While AI-powered tools such as Intelligent Tutoring Systems (ITS), automated assessments, and predictive analytics enhance personalized learning and operational efficiency, they also risk perpetuating algorithmic bias, eroding student privacy, and exacerbating educational inequities. This paper examines the dual-edged impact of AI in STEM classrooms, analyzing its benefits (e.g., adaptive learning, real-time feedback) and drawbacks (e.g., surveillance risks, pedagogical limitations) through an ethical lens. We identify critical gaps in current AI education research, particularly the lack of subject-specific frameworks for responsible integration and propose a three-phased implementation roadmap paired with a tiered professional development model for educators. Our framework emphasizes equity-centered design, combining technical AI literacy with ethical reasoning to foster critical engagement among students. Key recommendations include mandatory bias audits, low-resource adaptation strategies, and policy alignment to ensure AI serves as a tool for inclusive, human-centered STEM education. By bridging theory and practice, this work advances a research-backed approach to AI integration that prioritizes pedagogical integrity, equity, and student agency in an increasingly algorithmic world. Keywords: Artificial Intelligence, STEM education, algorithmic bias, ethical AI, K-12 pedagogy, equity in education

Rodrigo Tertulino

The application of data mining and artificial intelligence in education offers unprecedented potential for personalizing learning and early identification of at-risk students. However, the practical use of these techniques faces a significant barrier in privacy legislation, such as Brazil's General Data Protection Law (LGPD), which restricts the centralization of sensitive student data. To resolve this challenge, privacy-preserving computational approaches are required. The present study evaluates the feasibility and effectiveness of Federated Learning, specifically the FedProx algorithm, to predict student performance using microdata from the Brazilian Basic Education Assessment System (SAEB). A Deep Neural Network (DNN) model was trained in a federated manner, simulating a scenario with 50 schools, and its performance was rigorously benchmarked against a centralized eXtreme Gradient Boosting (XGBoost) model. The analysis, conducted on a universe of over two million student records, revealed that the centralized model achieved an accuracy of 63.96%. Remarkably, the federated model reached a peak accuracy of 61.23%, demonstrating a marginal performance loss in exchange for a robust privacy guarantee. The results indicate that Federated Learning is a viable and effective solution for building collaborative predictive models in the Brazilian educational context, in alignment with the requirements of the LGPD.

Prakash Shukla, Suchismita Naik, Ike Obi et al.

The growing integration of AI tools in student design projects presents an unresolved challenge in HCI education: how should AI-generated content be cited and documented? Traditional citation frameworks -- grounded in credibility, retrievability, and authorship -- struggle to accommodate the dynamic and ephemeral nature of AI outputs. In this paper, we examine how undergraduate students in a UX design course approached AI usage and citation when given the freedom to integrate generative tools into their design process. Through qualitative analysis of 35 team projects and reflections from 175 students, we identify varied citation practices ranging from formal attribution to indirect or absent acknowledgment. These inconsistencies reveal gaps in existing frameworks and raise questions about authorship, assessment, and pedagogical transparency. We argue for rethinking AI citation as a reflective and pedagogical practice; one that supports metacognitive engagement by prompting students to critically evaluate how and why they used AI throughout the design process. We propose alternative strategies -- such as AI contribution statements and process-aware citation models that better align with the iterative and reflective nature of design education. This work invites educators to reconsider how citation practices can support meaningful student--AI collaboration.

Wenhan Lyu, Shuang Zhang, Tingting et al.

Generative AI (GenAI) has brought opportunities and challenges for higher education as it integrates into teaching and learning environments. As instructors navigate this new landscape, understanding their engagement with and attitudes toward GenAI is crucial. We surveyed 178 instructors from a single U.S. university to examine their current practices, perceptions, trust, and distrust of GenAI in higher education in March 2024. While most surveyed instructors reported moderate to high familiarity with GenAI-related concepts, their actual use of GenAI tools for direct instructional tasks remained limited. Our quantitative results show that trust and distrust in GenAI are related yet distinct; high trust does not necessarily imply low distrust, and vice versa. We also found significant differences in surveyed instructors' familiarity with GenAI across different trust and distrust groups. Our qualitative results show nuanced manifestations of trust and distrust among surveyed instructors and various approaches to support calibrated trust in GenAI. We discuss practical implications focused on (dis)trust calibration among instructors.

Dharmendra Kumar Gupta, Arunima Chaudhuri, Dip Gaine

Self-directed learning (SDL), which emphasizes the need for students to take ownership of their learning, has become a crucial part of medical education. With the increasing complexity of health care, SDL is seen as a crucial skill for fostering lifelong learning and adapting to new challenges. This systematic review examines the current landscape of SDL in undergraduate medical education, exploring its effectiveness, implementation strategies, and areas for future development. A methodical exploration was carried out within the PubMed database to locate pertinent research articles released between 2012 and 2024. Studies that reported results pertaining to academic achievement, clinical competence, or student perspectives and that concentrated on SDL in undergraduate medical education were included. Two reviewers independently extracted the data, evaluated its quality, and synthesized the results thematically. In all, twenty-three papers were covered in this study. The findings indicate SDL positively impacts students’ academic performance and clinical skills, with many students reporting increased engagement and motivation. Effective implementation strategies included integrating SDL into the curriculum, providing faculty support, and utilizing digital tools to enhance learning. However, the review also identified significant heterogeneity in the definition and assessment of SDL across studies, as well as challenges related to student self-regulation and faculty preparedness. SDL is a useful method in medical education for undergraduates since it helps students become self-reflective, independent practitioners. To fully comprehend its long-term effects, however, longitudinal research, faculty development initiatives, and standardized SDL frameworks are required.

Zhang Xiong, Haoxuan Li, Zhuang Liu et al.

Personalized education, tailored to individual student needs, leverages educational technology and artificial intelligence (AI) in the digital age to enhance learning effectiveness. The integration of AI in educational platforms provides insights into academic performance, learning preferences, and behaviors, optimizing the personal learning process. Driven by data mining techniques, it not only benefits students but also provides educators and institutions with tools to craft customized learning experiences. To offer a comprehensive review of recent advancements in personalized educational data mining, this paper focuses on four primary scenarios: educational recommendation, cognitive diagnosis, knowledge tracing, and learning analysis. This paper presents a structured taxonomy for each area, compiles commonly used datasets, and identifies future research directions, emphasizing the role of data mining in enhancing personalized education and paving the way for future exploration and innovation.

Zhiyu Fan, Yannic Noller, Ashish Dandekar et al.

The growing number of students enrolling in Computer Science (CS) programmes is pushing CS educators to their limits. This poses significant challenges to computing education, particularly the teaching of introductory programming and advanced software engineering (SE) courses. First-year programming courses often face overwhelming enrollments, including interdisciplinary students who are not CS majors. The high teacher-to-student ratio makes it challenging to provide timely and high-quality feedback. Meanwhile, software engineering education comes with inherent difficulties like acquiring industry partners and the dilemma that such software projects are often under or over-specified and one-time efforts within one team or one course. To address these challenges, we designed a novel foundational SE course. This SE course envisions building a full-fledged Intelligent Tutoring System (ITS) of Programming Assignments to provide automated, real-time feedback for novice students in programming courses over multiple years. Each year, SE students contribute to specific short-running SE projects that improve the existing ITS implementation, while at the same time, we can deploy the ITS for usage by students for learning programming. This project setup builds awareness among SE students about their contribution to a "to-be-deployed" software project. In this multi-year teaching effort, we have incrementally built an ITS that is now deployed in various programming courses. This paper discusses the Intelligent Tutoring System architecture, our teaching concept in the SE course, our experience with the built ITS, and our view of future computing education.

Luis Morales-Navarro, Yasmin B. Kafai

As several critical issues in computing such as algorithmic bias, discriminatory practices, and techno-solutionism have become more visible, numerous efforts are being proposed to integrate criticality in K-16 computing education. Yet, how exactly these efforts address criticality and translate it into classroom practice is not clear. In this conceptual paper, we first historicize how current efforts in critical computing education draw on previous work which has promoted learner empowerment through critical analysis and production. We then identify three emergent approaches: (1) inquiry, (2) design and (3) reimagination that build on and expand these critical traditions in computing education. Finally, we discuss how these approaches highlight issues to be addressed and provide directions for further computing education research.

George Boateng, Samuel John, Samuel Boateng et al.

Africa has a high student-to-teacher ratio which limits students' access to teachers for learning support such as educational question answering. In this work, we extended Kwame, a bilingual AI teaching assistant for coding education, adapted it for science education, and deployed it as a web app. Kwame for Science provides passages from well-curated knowledge sources and related past national exam questions as answers to questions from students based on the Integrated Science subject of the West African Senior Secondary Certificate Examination (WASSCE). Furthermore, students can view past national exam questions along with their answers and filter by year, question type, and topics that were automatically categorized by a topic detection model which we developed (91% unweighted average recall). We deployed Kwame for Science in the real world over 8 months and had 750 users across 32 countries (15 in Africa) and 1.5K questions asked. Our evaluation showed an 87.2% top 3 accuracy (n=109 questions) implying that Kwame for Science has a high chance of giving at least one useful answer among the 3 displayed. We categorized the reasons the model incorrectly answered questions to provide insights for future improvements. We also share challenges and lessons with the development, deployment, and human-computer interaction component of such a tool to enable other researchers to deploy similar tools. With a first-of-its-kind tool within the African context, Kwame for Science has the potential to enable the delivery of scalable, cost-effective, and quality remote education to millions of people across Africa.

Stefania Nirchi

Over the years, different definitions have accompanied the evolution of the non-cognitive skills concept. In this essay, we will analyse, in particular, their social-emotional dimension, through the Survey on Social and Emotional Skills (OECD, 2017); the features and critical aspects that have characterized their development and, if is it possible to separate cognitive and non-cognitive skills.   Le non-cognitive skills negli studi internazionali: peculiarità e aspetti critici. Nel corso degli anni, diverse definizioni hanno accompagnato l’evoluzione del concetto di competenze non cognitive. In questo saggio analizzeremo in particolare: la loro dimensione socio-emotiva, attraverso l’indagine Social and Emotional Skills (OCSE, 2017); le peculiarità e gli aspetti critici che ne hanno caratterizzato lo sviluppo e se è possibile separare le competenze cognitive da quelle non cognitive.

Antonia Liguori, Philippa Rappoport, Antonella Poce et al.

Sharli Paphitis, Fatima Akilu, Natasha Chilambo et al.

Background Despite theoretical support for including mental health and psychosocial support (MHPSS) with peacebuilding, few programmes in conflict-affected regions fully integrate these approaches. Aims To describe and assess preliminary outcomes of the Counselling on Wheels programme delivered by the NEEM Foundation in the Borno State of North-East Nigeria. Method We first describe the components of the Counselling on Wheels programme, including education and advocacy for peace and social cohesion through community peacebuilding partnerships and activities, and an MHPSS intervention open to all adults, delivered in groups of eight to ten people. We then conducted secondary analysis of data from 1550 adults who took part in the MHPSS intervention, who provided data at baseline and 1–2 weeks after the final group session. Vulnerability to violent extremism was assessed with a locally developed 80-item scale. Symptoms of common mental disorders were assessed with the Depression, Anxiety and Stress Scale (DASS-21) and Post-Traumatic Stress Disorder Scale (PTSD-8). Data were analysed through a mixed-effect linear regression model, accounting for clustering by community and adjusted for age and gender. Results After taking part in group MHPSS, scores fell for depression (−5.8, 95% CI −6.7 to −5.0), stress (−5.5, 95% CI −6.3 to −4.6), post-traumatic stress disorder (−2.9, 95% CI −3.4 to −2.4) and vulnerability to violent extremism (−44.6, 95% CI −50.6 to −38.6). Conclusions The Counselling on Wheels programme shows promise as a model for integrating MHPSS with community peacebuilding activities in this conflict-affected region of Africa.

Pooya Alinian, Raziyeh Mohammadi, Azadeh Parvaneh et al.

Recent economic developments of countries like Japan, Korea, and Singapore, as a result of improvement in the quality of their education, show that having a high-quality education may lead to economic growth. In this article, using some statistical methods, we argue that high quality education can change the economy towards higher growth. Therefore, for the development of the country, one should think about how to improve its education. One of the effective ways to improve the quality of education is to increase the efficiency of teachers and attract talented people to teaching positions. Research shows that raising teachers' salaries, along with a proper quality improvement program, can help facilitate this process.

Cristian Candia, Javier Pulgar, Flavio Pinheiro

Underlying complex systems, there is a rich web of interconnected components that determine the relational properties of the system. Yet, traditional methods used in education sciences often disregard the underlying complexity of the educational system and, consequently, its emergence phenomena. Here, we argue that an interconnected vision of educational systems -- from classrooms to an organizational level -- is key to improving learning, social integration, well-being, and decision making, all fundamental aspects of the educational experience. Understanding the education system as an interconnected network of people, degree programs, and institutions requires methods and concepts from computational social sciences. Thus, we can leverage institutional records and (quasi) experimental designs to elicit the relational maps of key players in education and derive their implications in their functioning at all scales. In different settings, from elementary classrooms to higher education programs, we show how mapping the network relationships between entities can lead to the inference of novel insights about education systems and the development of solutions with societal implications.

Yajing Guo, Guangzhi Yan, Shengnan Chen et al.

Porcine parvoviruses (PPVs) are a group of small non-enveloped viruses with seven species (porcine parvovirus 1–7, PPV1-7) have been identified. In this study, a novel porcine parvovirus, provisionally named porcine parvovirus 8 (PPV8), was initially identified via high-throughput sequencing (HTS) in porcine reproductive and respiratory syndrome virus-positive samples collected from swine herds in Guangdong province, 2021. The nearly full-length genome of PPV8 strain GDJM2021 is 4,380 nucleotides in length with two overlapping open ORFs encoding NS1 and VP1 respectively. Sequence analysis indicated that PPV8 shared 16.23–44.18% sequence identity at the genomic levels to PPV1-7 with the relatively highest homology to PPV1. PPV8-GDJM2021 shared 31.86–32.68% aa sequence identity of NS1 protein with those of PPV1 and porcine bufavirus (PBuV), and formed an independent branch neighboring to those formed by members of the genus Protoparvovirus. Of the 211 clinical samples collected from 1990 to 2021, 37 samples (17.5%) distributed over 12 regions in China were positive for PPV8 with time spanning 24 years (1998–2021). To our knowledge, this is the first report on the genomic characterization of the novel PPV8 and its epidemiological situations in China.

Isabel Foster, Michelle Galloway, Wieda Human et al.

Stigma is a critical barrier for TB care delivery; yet data on stigma reduction interventions is limited. This review maps the available literature on TB stigma reduction interventions, using the Health Stigma and Discrimination framework and an implementation analysis to identify research gaps and inform intervention design. Using search terms for TB and stigma, we systematically searched PubMed, EMBASE and Web of Science. Two independent reviewers screened all abstracts, full-texts, extracted data, conducted a quality assessment, and assessed implementation. Results were categorized by socio-ecological level, then sub-categorized by the stigma driver or manifestation targeted. After screening 1865 articles, we extracted data from nine. Three studies were implemented at the individual and interpersonal level using a combination of TB clubs and interpersonal support to target internal and anticipated stigma among persons with TB. Two studies were implemented at the interpersonal level using counselling or a video based informational tool delivered to households to reduce stigma drivers and manifestations. Three studies were implemented at the organizational level, targeting drivers of stigma among healthcare workers (HW) and enacted stigma among HWs. One study was implemented at the community level using an educational campaign for community members. Stakeholder consultation emphasized the importance of policy level interventions and education on the universality of risk to destigmatize TB. Review findings suggest that internal and anticipated TB stigma may be addressed effectively with interventions targeted towards individuals using counselling or support groups. In contrast, enacted TB stigma may be better addressed with information-based interventions implemented at the organizational or community level. Policy level interventions were absent but identified as critical by stakeholders. Implementation barriers included the lack of high-quality training and integration with mental health services. Three key gaps must be addressed in future research: consistent stigma definitions, standardized stigma measurement, and measurement of implementation outcomes.

Coral Elizondo Carmona

El diseño universal para el aprendizaje es un marco educativo que guía el diseño de métodos, materiales y entornos flexibles que minimizan las barreras al aprendizaje. Está formado por pautas y puntos de verificación que ofrecen propuestas para un diseño universal que logre el aprendizaje experto para todos. Estas pautas y puntos de verificación se organizan en torno a la neurociencia y la psicología cognitiva, lo cual permite aportaciones a la educación desde un estudio transdisciplinar.

Christian Habeck, Yunglin Gazes, Yaakov Stern

Cognitive Reserve (CR), according to a recent consensus definition of the NIH-funded Reserve and Resilience collaboratory,1 is constituted by any mechanism contributing to cognitive performance beyond, or interacting with, brain structure in the widest sense. To identity multivariate activation patterns fulfilling this postulate, we investigated a verbal Sternberg fMRI task and imaged 181 people with age coverage in the ranges 20–30 (44 participants) and 55–70 (137 participants). Beyond task performance, participants were characterized in terms of demographics, and neuropsychological assessments of vocabulary, episodic memory, perceptual speed, and abstract fluid reasoning. Participants studied an array of either one, three, or six upper-case letters for 3 s (=encoding phase), then a blank fixation screen was presented for 7 s (=maintenance phase), to be probed with a lower-case letter to which they responded with a differential button press whether the letter was part of the studied array or not (=retrieval phase). We focused on identifying maintenance-related activation patterns showing memory load increases in pattern score on an individual participant level for both age groups. We found such a pattern that increased with memory load for all but one person in the young participants (p < 0.001), and such a pattern for all participants in the older group (p < 0.001). Both patterns showed broad topographic similarities; however, relationships to task performance and neuropsychological characteristics were markedly different and point to individual differences in Cognitive Reserve. Beyond the derivation of group-level activation patterns, we also investigated the inter-subject spatial similarity of individual working memory rehearsal patterns in the older participants’ group as a function of neuropsychological and task performance, education, and mean cortical thickness. Higher task accuracy and neuropsychological function was reliably associated with higher inter-subject similarity of individual-level activation patterns in older participants.

Roy Meissner, Andreas Thor

EAs.LiT is an e-assessment management and analysis software for which contextual requirements and usage scenarios changed over time. Based on these factors and further development activities, the decision was made to adopt a microservice architecture for EAs.LiT version 2 in order to increase its flexibility to adapt to new and changed circumstances. This architectural style and a few adopted technologies, like RDF as a data format, enabled an eased implementation of various use cases. Thus we consider the microservice architecture productive and recommend it for usage in other educational projects. The specific architecture of EAs.LiT version 2 is presented within this article, targeting to enable other educational projects to adopt it by using our work as a foundation or template.