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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
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
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
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
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
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
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
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
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
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
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
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
Chu, Jinjin; Szlagor, Maciej – International Journal of Web-Based Learning and Teaching Technologies, 2023
Distance education between the student and the teacher through online sessions can make it difficult for a student who does not understand a concept to ask for clarification. Lack of a physical campus or social pressure from peers can demotivate students from completing their assignments. The framework of multi-intelligence English teaching based…
Descriptors: Distance Education, Blended Learning, Educational Technology, Multiple Intelligences
Deutsch, Joe – Quest, 2021
The National Association for Kinesiology in Higher Education's (NAHKE) efforts to connect professionals to share our strategies and passions for success within kinesiology are more valuable than ever before and developing emotionally intelligent leaders and administrators is very important. For the 30th Delphine Hanna Commemorative Lecture, the…
Descriptors: Emotional Intelligence, Kinesiology, Higher Education
Ellala, Ziyad K.; Abu Attiyeh, Jamal H.; Ellala, Saeb K.; Kaba, Abdoulaye – Gifted Education International, 2022
The current study aimed to identify the level of emotional intelligence of outstanding students at the College of Education, Al Ain University (AAU), the United Arab Emirates, and their counterparts at Princess Nourah University (PNU), in the Kingdom of Saudi Arabia. A sample of 77 students was selected from both universities, of whom 41 students…
Descriptors: Emotional Intelligence, Academically Gifted, Foreign Countries, Schools of Education

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