Hasil untuk "Education"

S. Golden, J. Earp

Mohammad Amin Kuhail, Nazik Alturki, S. Alramlawi et al.

Chatbots hold the promise of revolutionizing education by engaging learners, personalizing learning activities, supporting educators, and developing deep insight into learners’ behavior. However, there is a lack of studies that analyze the recent evidence-based chatbot-learner interaction design techniques applied in education. This study presents a systematic review of 36 papers to understand, compare, and reflect on recent attempts to utilize chatbots in education using seven dimensions: educational field, platform, design principles, the role of chatbots, interaction styles, evidence, and limitations. The results show that the chatbots were mainly designed on a web platform to teach computer science, language, general education, and a few other fields such as engineering and mathematics. Further, more than half of the chatbots were used as teaching agents, while more than a third were peer agents. Most of the chatbots used a predetermined conversational path, and more than a quarter utilized a personalized learning approach that catered to students’ learning needs, while other chatbots used experiential and collaborative learning besides other design principles. Moreover, more than a third of the chatbots were evaluated with experiments, and the results primarily point to improved learning and subjective satisfaction. Challenges and limitations include inadequate or insufficient dataset training and a lack of reliance on usability heuristics. Future studies should explore the effect of chatbot personality and localization on subjective satisfaction and learning effectiveness.

Alexander Hendra Dwi Asmara

This essay attempts to construct a model of religious education for transformation that effectively addresses the growth of “everyday religious conflict” in Indonesia’s post-Suharto era. Using the lens of transformative learning theory, this essay emphasizes that the task of religious education should not merely serve as an intra-ecclesial agency of church or religious maintenance but must retrieve its task to reconstruct and to transform social situations. Such a vision emphasizes the task of religious educators to inform and form people to bring them into the fullness of life for themselves and others – to transform the world. This essay draws insights from two scholars in transformative learning theory – Jack Mezirow and Paulo Freire – who point out two foundations for transformation: (1) critical reflection, and (2) dialogue. These two visions can inspire religious educators to introduce critical reflection in their curriculum and to develop interreligious education that nurtures dialogue and collaboration. By focusing on developing critical reflection and interreligious education, religious education can offer transformation in Indonesian society plagued by ongoing conflict.

J. Bond, D. Humphrey, K. Paton et al.

D. Boote, Penny M. Beile

S. Downing

Ruth M. Wertheimer

Firas Almasri

The use of Artificial Intelligence (AI) in education is transforming various dimensions of the education system, such as instructional practices, assessment strategies, and administrative processes. It also plays an active role in the progression of science education. This systematic review attempts to render an inherent understanding of the evidence-based interaction between AI and science education. Specifically, this study offers a consolidated analysis of AI’s impact on students’ learning outcomes, contexts of its adoption, students’ and teachers’ perceptions about its use, and the challenges of its use within science education. The present study followed the PRISMA guidelines to review empirical papers published from 2014 to 2023. In total, 74 records met the eligibility for this systematic study. Previous research provides evidence of AI integration into a variety of fields in physical and natural sciences in many countries across the globe. The results revealed that AI-powered tools are integrated into science education to achieve various pedagogical benefits, including enhancing the learning environment, creating quizzes, assessing students’ work, and predicting their academic performance. The findings from this paper have implications for teachers, educational administrators, and policymakers.

Andi Nur Fiqhi Utami, Aco Nata Saputra, Citra N. Fariaty et al.

IntroductionThe establishment of Nusantara Capital City (IKN) as Indonesia’s new capital requires effective multi-level governance in which supporting provinces play a decisive role in ensuring administrative, infrastructural, and economic alignment with national priorities. This study examines the governance readiness and regional policy capacity of West Sulawesi Province, Indonesia, as a supporting region for IKN development.MethodsA qualitative descriptive–analytical case study was conducted using semi-structured interviews with regional officials, sectoral agencies, legislators, academics, and civil society, complemented by policy documents and statistics. Interview data were coded and analyzed using NVivo 12 Plus with crosstab analysis, while InfraNodus network analysis mapped keyword co-occurrence to capture cross-level governance interactions beyond internal regional conditions.ResultsThe findings reveal uneven governance readiness across five dimensions. Environmental sustainability and social infrastructure emerge as the strongest areas, reflecting alignment with national sustainability agendas and expanding public services. Human resource indicators show moderate readiness, particularly in workforce participation and training. In contrast, institutional capacity, higher education availability, and intergovernmental fiscal transfers represent the most critical weaknesses, indicating persistent gaps in administrative robustness, knowledge infrastructure, and vertical fiscal coordination.DiscussionThe study demonstrates that regional readiness for IKN is shaped not only by internal capacity but also by power asymmetries, regulatory dependence, and coordination frictions within multi-level governance. These findings contribute to governance and policy capacity scholarship by highlighting the centrality of cross-level relations in nationally driven megaprojects.

Qian Huang, Thijs Willems

As generative AI (Gen-AI) tools become more prevalent in education, there is a growing need to understand how educators, not just students, can actively shape their design and use. This study investigates how two instructors integrated four custom GPT tools into a Masters-level Qualitative Research Methods course for Urban Planning Policy students. Addressing two key gaps: the dominant framing of students as passive AI users, and the limited use of AI in qualitative methods education. The study explores how Gen-AI can support disciplinary learning when aligned with pedagogical intent. Drawing on the Technological Pedagogical Content Knowledge (TPACK) framework and action research methodology, the instructors designed GPTs to scaffold tasks such as research question formulation, interview practice, fieldnote analysis, and design thinking. Thematic analysis of student reflections, AI chat logs, and final assignments revealed that the tools enhanced student reflexivity, improved interview techniques, and supported structured analytic thinking. However, students also expressed concerns about cognitive overload, reduced immersion in data, and the formulaic nature of AI responses. The study offers three key insights: AI can be a powerful scaffold for active learning when paired with human facilitation; custom GPTs can serve as cognitive partners in iterative research practice; and educator-led design is critical to pedagogically meaningful AI integration. This research contributes to emerging scholarship on AI in higher education by demonstrating how empowering educators to design custom tools can promote more reflective, responsible, and collaborative learning with AI.

William Hersh

Generative AI has had a profound impact on biomedicine and health, both in professional work and in education. Based on large language models (LLMs), generative AI has been found to perform as well as humans in simulated situations taking medical board exams, answering clinical questions, solving clinical cases, applying clinical reasoning, and summarizing information. Generative AI is also being used widely in education, performing well in academic courses and their assessments. This review summarizes the successes of LLMs and highlights some of their challenges in the context of education, most notably aspects that may undermines the acquisition of knowledge and skills for professional work. It then provides recommendations for best practices overcoming shortcomings for LLM use in education. Although there are challenges for use of generative AI in education, all students and faculty, in biomedicine and health and beyond, must have understanding and be competent in its use.

Enes Ayalp

Computing Education faces significant challenges in equipping graduates with the resilience necessary to remain relevant amid rapid technological change. While existing curricula cultivate computing competencies, they often fail to integrate strategies for sustaining and adapting these skills, leading to reduced career resilience and recurrent industry layoffs. The lack of educational emphasis on sustainability and adaptability amid industry changes perpetuates a vicious cycle: As industries shift, skill fragmentation and decay lead to displacement, which in turn causes further skill degradation. The ongoing deficiency in adaptability and sustainability among learners is reflected in the frequent and intense shifts across the industry. This issue is particularly evident in domains marked by high technological volatility such as computer graphics and game development, where computing concepts, including computational thinking and performance optimization, are uniquely and continuously challenged. To foster sustainable and adaptive growth, this paper introduces, a new framework which addresses the question: How can computing education and professional development be connected to in these volatile sectors? It integrates two iterative, interconnected cycles, an educational and a professional, by linking education with profession to establish a lifelong, renewable practice. This approach allows computing professionals to excel and maintain relevance amid constant changes across their industry.

Pablo Merino-Muñoz, Felipe Hermosilla-Palma, Nicolás Gómez-Álvarez et al.

<b>Background/Objectives</b>: Groin and hip injuries are common in sport, and muscle weakness has been identified as an intrinsic risk factor. So, analyzing the strength of the hip musculature becomes important. To date, there are no hip adductor isometric strength tests on force platforms. This study aims to analyze the intra-test reliability of a hip adductor strength test using force platforms. <b>Methods:</b> The study sample comprised 13 male professional soccer players with an average age of 22.3 ± 3 years, body mass of 75.8 ± 5.4 kg, and height of 1.8 ± 0.1 m. Assessments were conducted on a uniaxial force platform. The variables analyzed are peak force (PF), rate of force development (RFD), and impulse. Intra-test reliability was evaluated using the coefficient of variation (CV), intraclass correlation coefficient (ICC), and Bland–Altman plots. <b>Results:</b> Acceptable levels of reliability were identified solely for the variable of peak force, with CV values of D = 5.7% for the dominant profile and ND = 5.4% for the non-dominant profile. Furthermore, moderate and good relative reliability were observed in peak force for the dominant (ICC = 0.706) and non-dominant (ICC = 0.819) profiles, respectively. However, the remaining time-related variables, RFD and impulse, did not achieve acceptable levels of absolute reliability (CV > 10%) and displayed poor to moderate relative reliability. <b>Conclusions</b>: In summary, PF during the hip adductor isometric strength test demonstrated acceptable absolute and commendable relative reliability. Conversely, the time-related variables, specifically RFD and impulse, yielded unsatisfactory absolute and relative reliability levels.

Diego-Martin Lombardo, Christian F Beckmann

The mechanism of neurocognitive failure in Alzheimer's disease remains obscure. While the mainstream hypothesis in the field posits that brain tau pathology is the only process that drives cognitive decline in AD, other complementary mechanisms link vascular brain lesions with beta-amyloid pathology as an important factor leading to neurodegeneration. Recently, it was also proposed that the brain's network's functional imbalance could primarily drive cognitive decline in neurodegenerative diseases. Here, we investigated whether the anticorrelation between the default mode (DMN) and dorsal attention networks (DAN) reveals different pathology burdens in the AD spectrum. We grouped individuals based on their PET amyloid and cognitive status. Using cross-validated regression models, we investigated whether cognitive impairment can be predicted based on rs-fMRI DMN-DAN anticorrelation. We found that the DMN-DAN anticorrelation differentiates between pathology burdens in AD, as quantified by PET amyloid imaging and cognitive performance. We found that an attenuated DMN-DAN anticorrelation predicted cognitive decline, which was controlled by sex, age, education, and brain tau pathology. Education level, measuring cognitive reserve, did not modulate the association between DMN-DAN anticorrelation and cognitive decline. We demonstrate that the attenuation of the anticorrelation between DMN and DAN is associated with a mechanism of cognitive dysfunction independent of tau pathology and proxies of resilience to cognitive decline or cognitive reserve. Our results also suggest the existence of an alternative mechanism of neurocognitive breakdown independent of advanced medial temporal cortex pathology and protective factors of cognitive decline, such as cognitive reserve.

R. Barnett

Basileal Imana, Aleksandra Korolova, John Heidemann

Digital ads on social-media platforms play an important role in shaping access to economic opportunities. Our work proposes and implements a new third-party auditing method that can evaluate racial bias in the delivery of ads for education opportunities. Third-party auditing is important because it allows external parties to demonstrate presence or absence of bias in social-media algorithms. Education is a domain with legal protections against discrimination and concerns of racial-targeting, but bias induced by ad delivery algorithms has not been previously explored in this domain. Prior audits demonstrated discrimination in platforms' delivery of ads to users for housing and employment ads. These audit findings supported legal action that prompted Meta to change their ad-delivery algorithms to reduce bias, but only in the domains of housing, employment, and credit. In this work, we propose a new methodology that allows us to measure racial discrimination in a platform's ad delivery algorithms for education ads. We apply our method to Meta using ads for real schools and observe the results of delivery. We find evidence of racial discrimination in Meta's algorithmic delivery of ads for education opportunities, posing legal and ethical concerns. Our results extend evidence of algorithmic discrimination to the education domain, showing that current bias mitigation mechanisms are narrow in scope, and suggesting a broader role for third-party auditing of social media in areas where ensuring non-discrimination is important.

Mine Dogucu, Sinem Demirci, Harry Bendekgey et al.

The presence of data science has been profound in the scientific community in almost every discipline. An important part of the data science education expansion has been at the undergraduate level. We conducted a systematic literature review to (1) portray current evidence and knowledge gaps in self-proclaimed undergraduate data science education research and (2) inform policymakers and the data science education community about what educators may encounter when searching for literature using the general keyword 'data science education.' While open-access publications that target a broader audience of data science educators and include multiple examples of data science programs and courses are a strength, significant knowledge gaps remain. The undergraduate data science literature that we identified often lacks empirical data, research questions and reproducibility. Certain disciplines are less visible. We recommend that we should (1) cherish data science as an interdisciplinary field; (2) adopt a consistent set of keywords/terminology to ensure data science education literature is easily identifiable; (3) prioritize investments in empirical studies.

Kemas Muslim L, Toru Ishida, Aditya Firman Ihsan et al.

In recent years, it has been observed that the center of gravity for the volume of higher education has shifted to the Global South. However, research indicates a widening disparity in the quality of higher education between the Global South and the Global North. Although investments in higher education within the Global South have increased, the rapid surge in student numbers has resulted in a decline in public expenditure per student. For instance, the student-to-teacher ratio in the Global South is significantly higher compared to that in the Global North, which poses a substantial barrier to the implementation of creative education. In response, Telkom University in Indonesia has embarked on an experiment to enhance the quality of learning and teaching by integrating large language models (LLMs) such as ChatGPT into five of its courses-Mathematics, English, Computing, Computer Systems, and Creative Media. This article elucidates the ongoing experimental plan and explores how the integration of LLMs could contribute to addressing the challenges currently faced by higher education in the Global South.

Joshua Ebere Chukwuere

The integration of generative Artificial Intelligence (AI) chatbots in higher education institutions (HEIs) is reshaping the educational landscape, offering opportunities for enhanced student support, and administrative and research efficiency. This study explores the future implications of generative AI chatbots in HEIs, aiming to understand their potential impact on teaching and learning, and research processes. Utilizing a narrative literature review (NLR) methodology, this study synthesizes existing research on generative AI chatbots in higher education from diverse sources, including academic databases and scholarly publications. The findings highlight the transformative potential of generative AI chatbots in streamlining administrative tasks, enhancing student learning experiences, and supporting research activities. However, challenges such as academic integrity concerns, user input understanding, and resource allocation pose significant obstacles to the effective integration of generative AI chatbots in HEIs. This study underscores the importance of proactive measures to address ethical considerations, provide comprehensive training for stakeholders, and establish clear guidelines for the responsible use of generative AI chatbots in higher education. By navigating these challenges, and leveraging the benefits of generative AI technologies, HEIs can harness the full potential of generative AI chatbots to create a more efficient, effective, inclusive, and innovative educational environment.

Tony Haoran Feng, Andrew Luxton-Reilly, Burkhard C. Wünsche et al.

Generative Artificial Intelligence (GenAI) offers numerous opportunities to revolutionise teaching and learning in Computing Education (CE). However, educators have expressed concerns that students may over-rely on GenAI and use these tools to generate solutions without engaging in the learning process. While substantial research has explored GenAI use in CE, and many Computer Science (CS) educators have expressed their opinions and suggestions on the subject, there remains little consensus on implementing curricula and assessment changes. In this paper, we describe our experiences with using GenAI in CS-focused educational settings and the changes we have implemented accordingly in our teaching in recent years since the popularisation of GenAI. From our experiences, we propose two primary actions for the CE community: 1) redesign take-home assignments to incorporate GenAI use and assess students on their process of using GenAI to solve a task rather than simply on the final product; 2) redefine the role of educators to emphasise metacognitive aspects of learning, such as critical thinking and self-evaluation. This paper presents and discusses these stances and outlines several practical methods to implement these strategies in CS classrooms. Then, we advocate for more research addressing the concrete impacts of GenAI on CE, especially those evaluating the validity and effectiveness of new teaching practices.