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Karen Moran Jackson; Rosemary Papa – Palgrave Macmillan, 2024
As artificial intelligence becomes an all-encompassing issue in education and beyond, this book seeks to answer how it will change the arc of educational leadership in K-12 schooling. Educators and leaders serve as the champions and gatekeepers of technology use in schools. They need to consider how AI can change education for the better while…
Descriptors: Artificial Intelligence, Technology Uses in Education, Elementary Secondary Education, Educational Administration
Shuguang Wang; Le Qin; Qian Yu – AERA Online Paper Repository, 2024
ChatGPT, a generative, pre-trained transformer gained great attention in various fields, Education included. The authors feel the need to investigate the views of practitioners and researchers regarding this issue. Using the systematic review as the structure of research method, the authors created their criteria, and got 65 papers after applying…
Descriptors: Artificial Intelligence, Metacognition, Plagiarism, Teaching Methods
Cara Giacomini; Deborah Trumble; Divya Krishnaswamy; Jacqueline King – Council for Advancement and Support of Education, 2024
How, and to what extent, are advancement professionals integrating artificial intelligence (AI) into their work? What do they view as the primary opportunities presented by this new technology, and what are their concerns regarding its use? To answer these questions, the Council for Advancement and Support of Education (CASE) and GiveCampus…
Descriptors: Institutional Advancement, Artificial Intelligence, Nonprofit Organizations, Schools
Bahar Radmehr; Adish Singla; Tanja Käser – International Educational Data Mining Society, 2024
There has been a growing interest in developing learner models to enhance learning and teaching experiences in educational environments. However, existing works have primarily focused on structured environments relying on meticulously crafted representations of tasks, thereby limiting the agent's ability to generalize skills across tasks. In this…
Descriptors: Reinforcement, Artificial Intelligence, Educational Environment, Natural Language Processing
Chengyuan Liu; Jialin Cui; Ruixuan Shang; Qinjin Jia; Parvez Rashid; Edward Gehringer – International Educational Data Mining Society, 2024
Evaluating the helpfulness of review comments is increasingly important in peer-assessment research, as students are more likely to accept and implement the feedback they perceive as helpful. Automating the evaluation of review helpfulness by AI models faces two challenges: (1) the limited availability of annotated datasets with helpfulness tags…
Descriptors: Artificial Intelligence, Technology Uses in Education, Peer Evaluation, Helping Relationship
Yijun Zhao; Zhengxin Qi; Son Tung Do; John Grossi; Jee Hun Kang; Gary M. Weiss – International Educational Data Mining Society, 2024
GRE Aptitude Test scores have been a key criterion for admissions to U.S. graduate programs. However, many universities lifted their standardized testing requirements during the COVID-19 pandemic, and many decided not to reinstate them once the pandemic ended. This change poses additional challenges in evaluating prospective students. In this…
Descriptors: College Entrance Examinations, Graduate Study, Scores, College Applicants
Manh Hung Nguyen; Sebastian Tschiatschek; Adish Singla – International Educational Data Mining Society, 2024
Student modeling is central to many educational technologies as it enables predicting future learning outcomes and designing targeted instructional strategies. However, open-ended learning domains pose challenges for accurately modeling students due to the diverse behaviors and a large space of possible misconceptions. To approach these…
Descriptors: Artificial Intelligence, Natural Language Processing, Synthesis, Student Behavior
Jionghao Lin; Eason Chen; Zifei Han; Ashish Gurung; Danielle R. Thomas; Wei Tan; Ngoc Dang Nguyen; Kenneth R. Koedinger – International Educational Data Mining Society, 2024
Automated explanatory feedback systems play a crucial role in facilitating learning for a large cohort of learners by offering feedback that incorporates explanations, significantly enhancing the learning process. However, delivering such explanatory feedback in real-time poses challenges, particularly when high classification accuracy for…
Descriptors: Artificial Intelligence, Man Machine Systems, Natural Language Processing, Feedback (Response)
Silverman, Linda K.; Gilman, Barbara J. – Psychology in the Schools, 2020
School psychologists in today's schools have the unique opportunity--and responsibility--to guide identification for gifted programs. "Who is gifted?" remains a perennial question in the gifted education literature, not answered by group intelligence screeners that purportedly level the playing field for all. As the student body grows…
Descriptors: Best Practices, Academically Gifted, Talent Identification, Children
Reiss, Michael J. – School Science Review, 2020
School genetics is changing. Nowadays, students are more likely to be introduced to the idea that many characteristics of organisms, including those of humans, are not determined by the actions of just one or two genes but result from interactions between the products of many genes and the environments of each organism. This article asks whether…
Descriptors: Science Instruction, Genetics, Intelligence, Outcomes of Education
Martin-Requejo, Katya; Santiago-Ramajo, Sandra – Electronic Journal of Research in Educational Psychology, 2021
Introduction: There continues to be a lack of conclusive data on how IQ, executive functions and emotional intelligence, as a set of factors, contribute toward academic skills. Method: This lack prompted the implementation of this study in 34 children (9-year-olds), through the application of the following instruments--Kaufman Brief Intelligence…
Descriptors: Intelligence Quotient, Executive Function, Emotional Intelligence, Academic Ability
Gallego, María Gómez; Perez de los Cobos, Alfonso Palazón; Gallego, Juan Cándido Gómez – Education Sciences, 2021
A main goal of the university institution should be to reduce the desertion of its students, in fact, the dropout rate constitutes a basic indicator in the accreditation processes of university centers. Thus, evaluating the cognitive functions and learning skills of students with an increased risk of academic failure can be useful for the adoption…
Descriptors: Identification, At Risk Students, Potential Dropouts, Cognitive Processes
Martín-Requejo, Katya; Santiago-Ramajo, Sandra – Mind, Brain, and Education, 2021
It is necessary to know the influence of the current pandemic situation on children's emotional intelligence (EI). Therefore, this study aimed to analyze the difference in 34 Spanish children's EI (aged 9-10) caused by the lockdown. EI was measured with the BarOn Emotional Intelligence Inventory (EQ-i:YV). Results have revealed a reduction in EI,…
Descriptors: Emotional Intelligence, Elementary School Students, Pandemics, COVID-19
Troche, Stefan J.; von Gugelberg, Helene M.; Pahud, Olivier; Rammsayer, Thomas H. – Journal of Intelligence, 2021
One of the best-established findings in intelligence research is the pattern of positive correlations among various intelligence tests. Although this so-called positive manifold became the conceptual foundation of many theoretical accounts of intelligence, the very nature of it has remained unclear. Only recently, "Process Overlap…
Descriptors: Executive Function, Attention Control, Psychometrics, Intelligence Tests
Lewis, Andrew – Perspectives in Education, 2019
With the ascent of the National Party to power in South Africa in 1948, education reflected apartheid thinking and practices and implemented the ideology of separate development in educational institutions. Pronouncements of the African child's inferiority were reflected in government policy and legislation. The origins of this thinking and…
Descriptors: Blacks, Foreign Countries, Race, Educational Policy

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