Hasil untuk "Education"

H. Crompton, D. Burke

This systematic review provides unique findings with an up-to-date examination of artificial intelligence (AI) in higher education (HE) from 2016 to 2022. Using PRISMA principles and protocol, 138 articles were identified for a full examination. Using a priori, and grounded coding, the data from the 138 articles were extracted, analyzed, and coded. The findings of this study show that in 2021 and 2022, publications rose nearly two to three times the number of previous years. With this rapid rise in the number of AIEd HE publications, new trends have emerged. The findings show that research was conducted in six of the seven continents of the world. The trend has shifted from the US to China leading in the number of publications. Another new trend is in the researcher affiliation as prior studies showed a lack of researchers from departments of education. This has now changed to be the most dominant department. Undergraduate students were the most studied students at 72%. Similar to the findings of other studies, language learning was the most common subject domain. This included writing, reading, and vocabulary acquisition. In examination of who the AIEd was intended for 72% of the studies focused on students, 17% instructors, and 11% managers. In answering the overarching question of how AIEd was used in HE, grounded coding was used. Five usage codes emerged from the data: (1) Assessment/Evaluation, (2) Predicting, (3) AI Assistant, (4) Intelligent Tutoring System (ITS), and (5) Managing Student Learning. This systematic review revealed gaps in the literature to be used as a springboard for future researchers, including new tools, such as Chat GPT. A systematic review examining AIEd in higher education (HE) up to the end of 2022. Unique findings in the switch from US to China in the most studies published. A two to threefold increase in studies published in 2021 and 2022 to prior years. AIEd was used for: Assessment/Evaluation, Predicting, AI Assistant, Intelligent Tutoring System, and Managing Student Learning.

C. Chan

This study aims to develop an AI education policy for higher education by examining the perceptions and implications of text generative AI technologies. Data was collected from 457 students and 180 teachers and staff across various disciplines in Hong Kong universities, using both quantitative and qualitative research methods. Based on the findings, the study proposes an AI Ecological Education Policy Framework to address the multifaceted implications of AI integration in university teaching and learning. This framework is organized into three dimensions: Pedagogical, Governance, and Operational. The Pedagogical dimension concentrates on using AI to improve teaching and learning outcomes, while the Governance dimension tackles issues related to privacy, security, and accountability. The Operational dimension addresses matters concerning infrastructure and training. The framework fosters a nuanced understanding of the implications of AI integration in academic settings, ensuring that stakeholders are aware of their responsibilities and can take appropriate actions accordingly. Proposed AI Ecological Education Policy Framework for university teaching and learning. Three dimensions: Pedagogical, Governance, and Operational AI Policy Framework. Qualitative and quantitative data collected from students, teachers, and staff. Ten key areas identified for planning an AI policy in universities. Students should play an active role in drafting and implementing the policy. Proposed AI Ecological Education Policy Framework for university teaching and learning. Three dimensions: Pedagogical, Governance, and Operational AI Policy Framework. Qualitative and quantitative data collected from students, teachers, and staff. Ten key areas identified for planning an AI policy in universities. Students should play an active role in drafting and implementing the policy.

B. Clark

G. Cooper

The advent of generative artificial intelligence (AI) offers transformative potential in the field of education. The study explores three main areas: (1) How did ChatGPT answer questions related to science education? (2) What are some ways educators could utilise ChatGPT in their science pedagogy? and (3) How has ChatGPT been utilised in this study, and what are my reflections about its use as a research tool? This exploratory research applies a self-study methodology to investigate the technology. Impressively, ChatGPT’s output often aligned with key themes in the research. However, as it currently stands, ChatGPT runs the risk of positioning itself as the ultimate epistemic authority, where a single truth is assumed without a proper grounding in evidence or presented with sufficient qualifications. Key ethical concerns associated with AI include its potential environmental impact, issues related to content moderation, and the risk of copyright infringement. It is important for educators to model responsible use of ChatGPT, prioritise critical thinking, and be clear about expectations. ChatGPT is likely to be a useful tool for educators designing science units, rubrics, and quizzes. Educators should critically evaluate any AI-generated resource and adapt it to their specific teaching contexts. ChatGPT was used as a research tool for assistance with editing and to experiment with making the research narrative clearer. The intention of the paper is to act as a catalyst for a broader conversation about the use of generative AI in science education.

W. Bao

Abstract Starting from the spring of 2020, the outbreak of the COVID‐19 caused Chinese universities to close the campuses and forced them to initiate online teaching. This paper focuses on a case of Peking University's online education. Six specific instructional strategies are presented to summarize current online teaching experiences for university instructors who might conduct online education in similar circumstances. The study concludes with five high‐impact principles for online education: (a) high relevance between online instructional design and student learning, (b) effective delivery on online instructional information, (c) adequate support provided by faculty and teaching assistants to students; (d) high‐quality participation to improve the breadth and depth of student's learning, and (e) contingency plan to deal with unexpected incidents of online education platforms.

Gloria Ladson-Billings

Critical race theory (CRT) first emerged as a counterlegal scholarship to the positivistand liberal legal discourse of civil rights. This scholarly tradition argues against the slow pace of racial reform in the United States. Critical race theory begins with the notion that racism is normal in American society. It departs from mainstream legal scholarship by sometimes employing storytelling. It critiques liberalism and argues that Whites have been the primary beneficiaries of civil rights legislation.Since schooling in the USA purports to prepare citizens, CRT looks at how citizenship and race might interact. Critical race theory's usefulness in understanding education inequity is in its infancy. It requires a critique of some of the civil rights era's most cherished legal victories and educationalreform movements, such as multiculturalism. The paper concludes with words of caution about the use of CRT in education without a more thorough analysis of the legal literature upon which it is based.

Halim Ayaz

The Advanced Certificate in Total Quality Management for Education is centered around the principles of total quality management (TQM) and the Malcolm Baldridge National Quality Award Criteria for Educational Programs. This comprehensive certificate is designed to provide an indepth knowledge of TQM principles, the Baldridge Award Criteria, and an understanding of how to conduct an assessment using the Baldridge criteria. The focus of the four certificate courses is an understanding of the value that Baldridge brings to your organization and how to use the Baldridge Award Criteria for Education as a change management tool. With a focus on strategic goals and indicators, this certificate offers school leaders an approach to help guide district, school and classroom improvement planning.

S. Boonlue

From the book title is "Guidance for Generative AI in Education and Research" for teachers, or this book serves as part of the guidelines for using Generative AI (GenAI) in the fields of education and research. This book was written by W. Holmes and F. Miao in 2023. This book addresses the rapid emergence of publicly available Generative AI tools, with the release of new versions outpacing the establishment of national regulatory frameworks. The lack of national regulations on GenAI in most countries raises concerns about user data privacy and leaves educational institutions unprotected and largely unprepared to scrutinize these tools. UNESCO has issued the first global guidelines on GenAI in education, aiming to support countries in taking immediate action, formulating long-term policies, and developing human capacity to ensure that people can effectively use AI and enhance their work with Generative AI.

J. Hatch

S. Schumacher, J. H. McMillan

Paul W. Richardson

David Card

R. Horner, E. Carr, J. Halle et al.

A. M. White

D. Garrison, H. Kanuka

B. Hooks

P. Pintrich, D. Schunk

K. Lorig, H. Holman

M. Knowles

J. Lagowski