Hasil untuk "Education"

Sir John Daniel

The COVID-19 pandemic is a huge challenge to education systems. This Viewpoint offers guidance to teachers, institutional heads, and officials on addressing the crisis. What preparations should institutions make in the short time available and how do they address students’ needs by level and field of study? Reassuring students and parents is a vital element of institutional response. In ramping up capacity to teach remotely, schools and colleges should take advantage of asynchronous learning, which works best in digital formats. As well as the normal classroom subjects, teaching should include varied assignments and work that puts COVID-19 in a global and historical context. When constructing curricula, designing student assessment first helps teachers to focus. Finally, this Viewpoint suggests flexible ways to repair the damage to students’ learning trajectories once the pandemic is over and gives a list of resources.

L. Mishra, Tushar Gupta, A. Shree

The whole educational system from elementary to tertiary level has been collapsed during the lockdown period of the novel coronavirus disease 2019 (COVID-19) not only in India but across the globe. This study is a portrayal of online teaching-learning modes adopted by the Mizoram University for the teaching-learning process and subsequent semester examinations. It looks forward to an intellectually enriched opportunity for further future academic decision-making during any adversity. The intended purpose of this paper seeks to address the required essentialities of online teaching-learning in education amid the COVID-19 pandemic and how can existing resources of educational institutions effectively transform formal education into online education with the help of virtual classes and other pivotal online tools in this continually shifting educational landscape. The paper employs both quantitative and qualitative approach to study the perceptions of teachers and students on online teaching-learning modes and also highlighted the implementation process of online teaching-learning modes. The value of this paper is to draw a holistic picture of ongoing online teaching-learning activities during the lockdown period including establishing the linkage between change management process and online teaching-learning process in education system amid the COVID-19 outbreak so as to overcome the persisting academic disturbance and consequently ensure the resumption of educational activities and discourses as a normal course of procedure in the education system.

J. Crawford, K. Butler-Henderson, J. Rudolph et al.

The Coronavirus 2019 (COVID-19) pandemic has created significant challenges for the global higher education community. Through a desktop analysis leveraging university and government sources where possible, we provide a timely map of the intra-period higher education responses to COVID-19 across 20 countries. We found that the responses by higher education providers have been diverse from having no response through to social isolation strategies on campus and rapid curriculum redevelopment for fully online offerings. We provide in our discussion a typology of the types of responses currently undertaken and assess the agility of higher education in preparing for the pandemic. We believe there are significant opportunities to learn from the pedagogical developments of other universities, in order to strengthen our collective response to COVID-19 now and into the future.

Suzanne García Mateus

Junaid Qadir

Engineering education is constantly evolving to keep up with the latest technological developments and meet the changing needs of the engineering industry. One promising development in this field is the use of generative artificial intelligence technology, such as the ChatGPT conversational agent. ChatGPT has the potential to offer personalized and effective learning experiences by providing students with customized feedback and explanations, as well as creating realistic virtual simulations for hands-on learning. However, it is important to also consider the limitations of this technology. ChatGPT and other generative AI systems are only as good as their training data and may perpetuate biases or even generate and spread misinformation. Additionally, the use of generative AI in education raises ethical concerns such as the potential for unethical or dishonest use by students and the potential unemployment of humans who are made redundant by technology. While the current state of generative AI technology represented by ChatGPT is impressive but flawed, it is only a preview of what is to come. It is important for engineering educators to understand the implications of this technology and study how to adapt the engineering education ecosystem to ensure that the next generation of engineers can take advantage of the benefits offered by generative AI while minimizing any negative consequences.

E. Deci

M. Bond, Hassan Khosravi, Maarten de Laat et al.

Although the field of Artificial Intelligence in Education (AIEd) has a substantial history as a research domain, never before has the rapid evolution of AI applications in education sparked such prominent public discourse. Given the already rapidly growing AIEd literature base in higher education, now is the time to ensure that the field has a solid research and conceptual grounding. This review of reviews is the first comprehensive meta review to explore the scope and nature of AIEd in higher education (AIHEd) research, by synthesising secondary research (e.g., systematic reviews), indexed in the Web of Science, Scopus, ERIC, EBSCOHost, IEEE Xplore, ScienceDirect and ACM Digital Library, or captured through snowballing in OpenAlex, ResearchGate and Google Scholar. Reviews were included if they synthesised applications of AI solely in formal higher or continuing education, were published in English between 2018 and July 2023, were journal articles or full conference papers, and if they had a method section 66 publications were included for data extraction and synthesis in EPPI Reviewer, which were predominantly systematic reviews (66.7%), published by authors from North America (27.3%), conducted in teams (89.4%) in mostly domestic-only collaborations (71.2%). Findings show that these reviews mostly focused on AIHEd generally (47.0%) or Profiling and Prediction (28.8%) as thematic foci, however key findings indicated a predominance of the use of Adaptive Systems and Personalisation in higher education. Research gaps identified suggest a need for greater ethical, methodological, and contextual considerations within future research, alongside interdisciplinary approaches to AIHEd application. Suggestions are provided to guide future primary and secondary research.

B. Rimer, C. Skinner

Cynthia E. Glidden

G. Psacharopoulos

P. Lucas, P. Ramsden

J. Kitzinger

L. Schultz

N. Noddings

J. H. McMillan, S. Schumacher

J. Reason

J. Gee

Karin Stolpe, Jonas Hallström

The interest in artificial intelligence (AI) in education has erupted during the last few years, primarily due to technological advances in AI. It is therefore argued that students should learn about AI, although it is debated exactly how it should be applied in education. AI literacy has been suggested as a way of defining competencies for students to acquire to meet a future everyday-and working life with AI. This study argues that researchers and educators need a framework for integrating AI literacy into technological literacy, where the latter is viewed as a multiliteracy. This study thus aims to critically analyse and discuss different components of AI literacy found in the literature in relation to technological literacy. The data consists of five AI literacy frameworks related to three traditions of technological knowledge: technical skills, technological scientific knowledge, and socio-ethical technical understanding. The results show that AI literacy for technology education emphasises technological scientific knowledge (e.g., knowledge about what AI is, how to recognise AI, and systems thinking) and socio-ethical technical understanding (e.g., AI ethics and the role of humans in AI). Technical skills such as programming competencies also appear but are less emphasised. Implications for technology education are also discussed, and a framework for AI literacy for technology education is suggested.

I. H. Y. Yim, Jiahong Su

Artificial intelligence (AI) literacy is a global strategic objective in education. However, little is known about how AI should be taught. In this paper, 46 studies in academic conferences and journals are reviewed to investigate pedagogical strategies, learning tools, assessment methods in AI literacy education in K-12 contexts, and students’ learning outcomes. The investigation reveals that the promotion of AI literacy education has seen significant progress in the past two decades. This highlights that intelligent agents, including Google’s Teachable Machine, Learning ML, and Machine Learning for Kids, are age-appropriate tools for AI literacy education in K-12 contexts. Kindergarten students can benefit from learning tools such as PopBots, while software devices, such as Scratch and Python, which help to develop the computational thinking of AI algorithms, can be introduced to both primary and secondary schools. The research shows that project-based, human–computer collaborative learning and play- and game-based approaches, with constructivist methodologies, have been applied frequently in AI literacy education. Cognitive, affective, and behavioral learning outcomes, course satisfaction and soft skills acquisition have been reported. The paper informs educators of appropriate learning tools, pedagogical strategies, assessment methodologies in AI literacy education, and students’ learning outcomes. Research implications and future research directions within the K-12 context are also discussed.