Hasil untuk "Education"

Xuesong Zhai, Xiaoyan Chu, C. Chai et al.

This study provided a content analysis of studies aiming to disclose how artificial intelligence (AI) has been applied to the education sector and explore the potential research trends and challenges of AI in education. A total of 100 papers including 63 empirical papers (74 studies) and 37 analytic papers were selected from the education and educational research category of Social Sciences Citation Index database from 2010 to 2020. The content analysis showed that the research questions could be classified into development layer (classification, matching, recommendation, and deep learning), application layer (feedback, reasoning, and adaptive learning), and integration layer (affection computing, role-playing, immersive learning, and gamification). Moreover, four research trends, including Internet of Things, swarm intelligence, deep learning, and neuroscience, as well as an assessment of AI in education, were suggested for further investigation. However, we also proposed the challenges in education may be caused by AI with regard to inappropriate use of AI techniques, changing roles of teachers and students, as well as social and ethical issues. The results provide insights into an overview of the AI used for education domain, which helps to strengthen the theoretical foundation of AI in education and provides a promising channel for educators and AI engineers to carry out further collaborative research.

Stefan A. D. Popenici, Sharon Kerr

This paper explores the phenomena of the emergence of the use of artificial intelligence in teaching and learning in higher education. It investigates educational implications of emerging technologies on the way students learn and how institutions teach and evolve. Recent technological advancements and the increasing speed of adopting new technologies in higher education are explored in order to predict the future nature of higher education in a world where artificial intelligence is part of the fabric of our universities. We pinpoint some challenges for institutions of higher education and student learning in the adoption of these technologies for teaching, learning, student support, and administration and explore further directions for research.

T. Kelley, J. Knowles

The global urgency to improve STEM education may be driven by environmental and social impacts of the twenty-first century which in turn jeopardizes global security and economic stability. The complexity of these global factors reach beyond just helping students achieve high scores in math and science assessments. Friedman (The world is flat: A brief history of the twenty-first century, 2005) helped illustrate the complexity of a global society, and educators must help students prepare for this global shift. In response to these challenges, the USA experienced massive STEM educational reforms in the last two decades. In practice, STEM educators lack cohesive understanding of STEM education. Therefore, they could benefit from a STEM education conceptual framework. The process of integrating science, technology, engineering, and mathematics in authentic contexts can be as complex as the global challenges that demand a new generation of STEM experts. Educational researchers indicate that teachers struggle to make connections across the STEM disciplines. Consequently, students are often disinterested in science and math when they learn in an isolated and disjoined manner missing connections to crosscutting concepts and real-world applications. The following paper will operationalize STEM education key concepts and blend learning theories to build an integrated STEM education framework to assist in further researching integrated STEM education.

R. Luckin, Wayne Holmes

Wayne Holmes, K. Porayska-Pomsta, Kenneth Holstein et al.

While Artificial Intelligence in Education (AIED) research has at its core the desire to support student learning, experience from other AI domains suggest that such ethical intentions are not by themselves sufficient. There is also the need to consider explicitly issues such as fairness, accountability, transparency, bias, autonomy, agency, and inclusion. At a more general level, there is also a need to differentiate between doing ethical things and doing things ethically, to understand and to make pedagogical choices that are ethical, and to account for the ever-present possibility of unintended consequences. However, addressing these and related questions is far from trivial. As a first step towards addressing this critical gap, we invited 60 of the AIED community’s leading researchers to respond to a survey of questions about ethics and the application of AI in educational contexts. In this paper, we first introduce issues around the ethics of AI in education. Next, we summarise the contributions of the 17 respondents, and discuss the complex issues that they raised. Specific outcomes include the recognition that most AIED researchers are not trained to tackle the emerging ethical questions. A well-designed framework for engaging with ethics of AIED that combined a multidisciplinary approach and a set of robust guidelines seems vital in this context.

Andy Nguyen, H. Ngo, Yvonne Hong et al.

The advancement of artificial intelligence in education (AIED) has the potential to transform the educational landscape and influence the role of all involved stakeholders. In recent years, the applications of AIED have been gradually adopted to progress our understanding of students’ learning and enhance learning performance and experience. However, the adoption of AIED has led to increasing ethical risks and concerns regarding several aspects such as personal data and learner autonomy. Despite the recent announcement of guidelines for ethical and trustworthy AIED, the debate revolves around the key principles underpinning ethical AIED. This paper aims to explore whether there is a global consensus on ethical AIED by mapping and analyzing international organizations’ current policies and guidelines. In this paper, we first introduce the opportunities offered by AI in education and potential ethical issues. Then, thematic analysis was conducted to conceptualize and establish a set of ethical principles by examining and synthesizing relevant ethical policies and guidelines for AIED. We discuss each principle and associated implications for relevant educational stakeholders, including students, teachers, technology developers, policymakers, and institutional decision-makers. The proposed set of ethical principles is expected to serve as a framework to inform and guide educational stakeholders in the development and deployment of ethical and trustworthy AIED as well as catalyze future development of related impact studies in the field.

Karen Glanz, B. Rimer, K. Viswanath

Alaa A. Abd-alrazaq, Rawan AlSaad, Dari Alhuwail et al.

The integration of large language models (LLMs), such as those in the Generative Pre-trained Transformers (GPT) series, into medical education has the potential to transform learning experiences for students and elevate their knowledge, skills, and competence. Drawing on a wealth of professional and academic experience, we propose that LLMs hold promise for revolutionizing medical curriculum development, teaching methodologies, personalized study plans and learning materials, student assessments, and more. However, we also critically examine the challenges that such integration might pose by addressing issues of algorithmic bias, overreliance, plagiarism, misinformation, inequity, privacy, and copyright concerns in medical education. As we navigate the shift from an information-driven educational paradigm to an artificial intelligence (AI)–driven educational paradigm, we argue that it is paramount to understand both the potential and the pitfalls of LLMs in medical education. This paper thus offers our perspective on the opportunities and challenges of using LLMs in this context. We believe that the insights gleaned from this analysis will serve as a foundation for future recommendations and best practices in the field, fostering the responsible and effective use of AI technologies in medical education.

S. Ball

Introduction to key concepts: education policy, economic necessity and public service reform Class, comprehensives and continuities: a short history of English education policy Current policy models and The UK government's approach to public service reform Current key issues: forms of policy and forms of equity A sociology of education policy: past, present and future.

Jiahong Su (苏嘉红), Weipeng Yang (杨伟鹏)

Purpose Artificial intelligence (AI) chatbots, such as ChatGPT and GPT-4, developed by OpenAI, have the potential to revolutionize education. This study explores the potential benefits and challenges of using ChatGPT in education (or “educative AI”). Design/Approach/Methods This paper proposes a theoretical framework called “IDEE” for educative AI such as using ChatGPT and other generative AI in education, which includes identifying the desired outcomes, determining the appropriate level of automation, ensuring ethical considerations, and evaluating effectiveness. Findings The benefits of using ChatGPT in education or more generally, educative AI, include a more personalized and efficient learning experience for students as well as easier and faster feedback for teachers. However, challenges such as the untested effectiveness of the technology, limitations in the quality of data, and ethical and safety concerns must also be considered. Originality/Value This study explored the opportunities and challenges of using ChatGPT in education within the proposed theoretical framework.

Joanna R. Southworth, Kati Migliaccio, Joseph Glover et al.

Artificial Intelligence (AI) is a ubiquitous concept and tool already found across society and an integral part of everyday life. As such, basic understanding and knowledge of AI should be a critical component of student education to foster successful global citizens. This position paper describes one possible path to address potential gaps in AI education and integrate AI across the curriculum at a traditional research university. The University of Florida (UF) is infusing AI across the curriculum and developing opportunities for student engagement within identified areas of AI literacy regardless of student discipline. The AI Across the Curriculum initiative being developed at UF will make AI education a cornerstone opportunity for all students. The ultimate goal of AI Across the Curriculum is the creation of an AI-ready workforce covering the essential 21st-century competencies identified as workforce and government needs worldwide. Qualified human capital is essential to face the challenges of the 21st-century, and UF is positioning itself to lead in meeting this global societal need. In designing the AI Across the Curriculum model, all students are provided with a suite of AI opportunities and are encouraged to engage. The university is taking advantage of a significant investment in AI campus-wide to innovate curriculum and create activities that nurture interdisciplinary engagement while ensuring student career readiness. As businesses, industry, and governments transform globally within this AI paradigm shift, AI education, innovation, and literacy will become cornerstones of curriculum with UF providing an inclusive example for all undergraduate, graduate, and professional students. While the AI effort at UF is inclusive and broad, the focus of this paper is on undergraduate programs which also represents a Quality Enhancement Plan (or QEP) effort for reaccreditation of UF ’ s undergraduate programs. This program is highly innovative and transformative, creating interdisciplinary AI literacy opportunity for all students.

G. Psacharopoulos, Harry Anthony Patrinos *

Returns to investment in education based on human capital theory have been estimated since the late 1950s. In the 40‐plus year history of estimates of returns to investment in education, there have been several reviews of the empirical results in attempts to establish patterns. Many more estimates from a wide variety of countries, including over‐time evidence, and estimates based on new econometric techniques, reaffirm the importance of human capital theory. This paper reviews and presents the latest estimates and patterns as found in the literature at the turn of the century. However, because the availability of rate of return estimates has grown exponentially, we include a new section on the need for selectivity in comparing returns to investment in education and establishing related patterns.

Peter McLaren

P. Grossman

M. Greene

M. Lichtman

Juan M. Dempere, K. Modugu, A. Hesham et al.

This study explores the effects of Artificial Intelligence (AI) chatbots, with a particular focus on OpenAI’s ChatGPT, on Higher Education Institutions (HEIs). With the rapid advancement of AI, understanding its implications in the educational sector becomes paramount.Utilizing databases like PubMed, IEEE Xplore, and Google Scholar, we systematically searched for literature on AI chatbots’ impact on HEIs. Our criteria prioritized peer-reviewed articles, prominent media outlets, and English publications, excluding tangential AI chatbot mentions. After selection, data extraction focused on authors, study design, and primary findings. The analysis combined descriptive and thematic approaches, emphasizing patterns and applications of AI chatbots in HEIs.The literature review revealed diverse perspectives on ChatGPT’s potential in education. Notable benefits include research support, automated grading, and enhanced human-computer interaction. However, concerns such as online testing security, plagiarism, and broader societal and economic impacts like job displacement, the digital literacy gap, and AI-induced anxiety were identified. The study also underscored the transformative architecture of ChatGPT and its versatile applications in the educational sector. Furthermore, potential advantages like streamlined enrollment, improved student services, teaching enhancements, research aid, and increased student retention were highlighted. Conversely, risks such as privacy breaches, misuse, bias, misinformation, decreased human interaction, and accessibility issues were identified.While AI’s global expansion is undeniable, there is a pressing need for balanced regulation in its application within HEIs. Faculty members are encouraged to utilize AI tools like ChatGPT proactively and ethically to mitigate risks, especially academic fraud. Despite the study’s limitations, including an incomplete representation of AI’s overall effect on education and the absence of concrete integration guidelines, it is evident that AI technologies like ChatGPT present both significant benefits and risks. The study advocates for a thoughtful and responsible integration of such technologies within HEIs.

Mamdouh Alenezi

Higher education institutions are going through major changes in their education and operations. Several influences are driving these major changes. Digital transformation, online courses, digital-navy students, operational costs, and micro and nano degrees are just some examples of these influences. Digital technologies show a range of tools selected to include formalized learning environments in teaching in higher education, and students utilize these tools to promote their learning. The Industrial Revolution 4.0’s technological growth has penetrated higher education institutions (HEIs), forcing them to deal with the digital transformation (DT) in all of its dimensions. As they enable us to characterize the various interrelationships among stakeholders in a digitally enabled context of teaching and learning, applying digital transformation techniques to the education sector is an emerging field that has attracted attention recently. The aim of this study is to provide an overview of the distinguishing features of the digital transformation implementation process that has occurred at higher education institutions. In addition, how digital learning can be seen as part of the ecosystem of modern higher education. Further study is necessary to determine how higher education institutions can comprehend digital transformation and meet the demands imposed by the fourth Industrial Revolution.

C. Preiksaitis, Christian Rose

Background Generative artificial intelligence (AI) technologies are increasingly being utilized across various fields, with considerable interest and concern regarding their potential application in medical education. These technologies, such as Chat GPT and Bard, can generate new content and have a wide range of possible applications. Objective This study aimed to synthesize the potential opportunities and limitations of generative AI in medical education. It sought to identify prevalent themes within recent literature regarding potential applications and challenges of generative AI in medical education and use these to guide future areas for exploration. Methods We conducted a scoping review, following the framework by Arksey and O'Malley, of English language articles published from 2022 onward that discussed generative AI in the context of medical education. A literature search was performed using PubMed, Web of Science, and Google Scholar databases. We screened articles for inclusion, extracted data from relevant studies, and completed a quantitative and qualitative synthesis of the data. Results Thematic analysis revealed diverse potential applications for generative AI in medical education, including self-directed learning, simulation scenarios, and writing assistance. However, the literature also highlighted significant challenges, such as issues with academic integrity, data accuracy, and potential detriments to learning. Based on these themes and the current state of the literature, we propose the following 3 key areas for investigation: developing learners’ skills to evaluate AI critically, rethinking assessment methodology, and studying human-AI interactions. Conclusions The integration of generative AI in medical education presents exciting opportunities, alongside considerable challenges. There is a need to develop new skills and competencies related to AI as well as thoughtful, nuanced approaches to examine the growing use of generative AI in medical education.

G. van den Berg, E. D. du Plessis

Although artificial intelligence (AI) has been part of our lives for some time, the launch of the Generative Pretrained Transformer (ChatGPT) has given it renewed attention. While most of these debates are about higher education in general, this article focuses on schoolteacher education and teacher training. This research aimed to determine the contribution of generative AI tools such as ChatGPT in lesson planning, critical thinking and openness in education. The research used a qualitative approach and document analysis following an interpretative paradigm. The findings reveal that generative language models such as ChatGPT can provide specific materials and support mechanisms, such as lesson plans, to schoolteachers and student teachers. It also showed that ChatGPT has levelled the playing field by opening access to lesson plans to all teachers. However, to unleash their full potential for education, it is crucial to approach these models with caution and critically evaluate their limitations and potential biases, understanding that they are tools to support teaching and learning and do not replace teachers. The study’s contribution lies in ChatGPT-generated lesson plans’ implications and the enhancement of critical thinking for teacher education, and it also underscores the need for further research to explore best practices for integrating ChatGPT in lesson planning.