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Beatriz Carbajal-Carrera – Australian Review of Applied Linguistics, 2024
The growing implementation of Generative AI (GenAI) in education has implications on the representation of knowledge and identity across languages. In a context where content biases have been reported in AI-generated content, it becomes relevant to interrogate the ways in which AI technologies represent different linguistic identities. This…
Descriptors: Artificial Intelligence, Sociolinguistics, Language Usage, Bias
Bryant Lopez – ProQuest LLC, 2024
Early in 2020, the United States was faced with the global impact of the COVID-19 pandemic resulting in several school districts across the country shutting down. What began as a temporary school closure morphed into one of the most creative and labor-intensive restructurings of schools. Leading the dramatic changes were elementary school…
Descriptors: COVID-19, Pandemics, School Closing, Principals
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Seth King; Anne Estapa; Tyler Bell; Joseph Boyer – Journal of Behavioral Education, 2024
Researchers increasingly identify virtual reality (VR) simulations as a potentially effective professional development tool. However, simulations used in education and behavior analysis typically require active oversight from technicians and instructors. "Smart" VR integrated with artificial intelligence could independently administer…
Descriptors: Computer Simulation, Skill Development, Behavior Modification, Verbal Communication
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Nicholas Leonard; Johnson Kwame Wor – Art Education, 2024
This article intends to empower and equip art educators to artistically address the functioning of facial detection algorithms through critical race theory (CRT). By highlighting how biometric data, a specific form of data that measures the physical qualities of individuals, is used in common social media facial detection algorithms like Snapchat,…
Descriptors: Art Education, Artificial Intelligence, Racism, Social Media
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Domenico Tullo; Bianca Levy; Jocelyn Faubert; Armando Bertone – Journal of Autism and Developmental Disorders, 2024
The extant literature aimed at characterizing attentional capability in autistics has presented inconsistent findings. This inconsistency and uncertainty may be the product of different theoretical and methodological approaches used to define attention in autism. In the current study, we investigate whether the allocation of attentional resources…
Descriptors: Autism Spectrum Disorders, Attention, Thinking Skills, Capacity Building
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José Luis Rodríguez Illera – Digital Education Review, 2024
The article reviews some of the relationships between AI and education, emphasizing the metaphors used, the difficulties in finding points of agreement, as well as aspects of the social criticism that is made of AI (e.g. considering that it can be a form of unwanted deviation). AI appears as one more case of technology that comes to improve…
Descriptors: Artificial Intelligence, Technology Uses in Education, Thinking Skills, Ethics
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Adronisha T. Frazier – Research Issues in Contemporary Education, 2024
This position paper explores the current state of artificial intelligence (AI) tools, educator support of and opposition to AI tools in teaching and learning, and the ethical and social implications of AI tools in higher education. As technology continuously develops in the educational community, educators must have a voice in how AI exists in the…
Descriptors: Artificial Intelligence, Higher Education, Technology Uses in Education, Inclusion
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Shin-Yu Kim; Inseong Jeon; Seong-Joo Kang – Journal of Chemical Education, 2024
Artificial intelligence (AI) and data science (DS) are receiving a lot of attention in various fields. In the educational field, the need for education utilizing AI and DS is also being emerged. In this context, we have created an AI/DS integrating program that generates a compound classification/regression model using characteristics of compounds…
Descriptors: Chemistry, Science Instruction, Laboratory Experiments, Artificial Intelligence
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Andres Felipe Zambrano; Nidhi Nasiar; Jaclyn Ocumpaugh; Alex Goslen; Jiayi Zhang; Jonathan Rowe; Jordan Esiason; Jessica Vandenberg; Stephen Hutt – International Educational Data Mining Society, 2024
Research into student affect detection has historically relied on ground truth measures of emotion that utilize one of three sources of data: (1) self-report data, (2) classroom observations, or (3) sensor data that is retrospectively labeled. Although a few studies have compared sensor- and observation-based approaches to student affective…
Descriptors: Psychological Patterns, Measurement Techniques, Observation, Middle School Students
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Napol Rachatasumrit; Paulo F. Carvalho; Kenneth R. Koedinger – International Educational Data Mining Society, 2024
What does it mean for a model to be a better model? One conceptualization, indeed a common one in Educational Data Mining, is that a better model is the one that fits the data better, that is, higher prediction accuracy. However, oftentimes, models that maximize prediction accuracy do not provide meaningful parameter estimates, making them less…
Descriptors: Data Analysis, Models, Prediction, Accuracy
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Qinjin Jia; Jialin Cui; Ruijie Xi; Chengyuan Liu; Parvez Rashid; Ruochi Li; Edward Gehringer – International Educational Data Mining Society, 2024
Feedback on student assignments plays a crucial role in steering students toward academic success. To provide feedback more promptly and efficiently, researchers are actively exploring the use of large language models (LLMs) to automatically generate feedback on student artifacts. Although the generated feedback is highly fluent, coherent, and…
Descriptors: Feedback (Response), Assignments, Artificial Intelligence, Accuracy
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Benny G. Johnson; Jeffrey S. Dittel; Rachel Van Campenhout – International Educational Data Mining Society, 2024
Combining formative practice with the primary expository content in a learning by doing method is a proven approach to increase student learning. Artificial intelligence has led the way for automatic question generation (AQG) systems that can generate volumes of formative practice otherwise prohibitive with human effort. One such AQG system was…
Descriptors: Artificial Intelligence, Automation, Textbooks, Questioning Techniques
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Jade Mai Cock; Hugues Saltini; Haoyu Sheng; Riya Ranjan; Richard Davis; Tanja Käser – International Educational Data Mining Society, 2024
Predictive models play a pivotal role in education by aiding learning, teaching, and assessment processes. However, they have the potential to perpetuate educational inequalities through algorithmic biases. This paper investigates how behavioral differences across demographic groups of different sizes propagate through the student success modeling…
Descriptors: Demography, Statistical Bias, Algorithms, Behavior
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Li, Danfeng; Shi, Jiannong – High Ability Studies, 2021
This study examined the effects of fluid intelligence and trait emotional intelligence (trait EI) on academic performance in primary school-aged intellectually gifted and average children (8-11 years of age). One hundred and four average children and eighty gifted children were administered a Raven's Standard Progressive Matrices and a Trait…
Descriptors: Gifted, Prediction, Academic Achievement, Mathematics Achievement
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Sternberg, Robert J.; Wong, Chak Haang; Kreisel, Anastasia P. – Journal of Intelligence, 2021
Cultural intelligence is one's ability to adapt when confronted with problems arising in interactions with people or artifacts of diverse cultures. In this study, we conduct an initial construct-validation and assessment of a maximum-performance test of cultural intelligence. We assess the psychometric properties of the test and also correlate the…
Descriptors: Intelligence, Cultural Awareness, Adjustment (to Environment), Intelligence Tests
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