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Lucas Kohnke; Di Zou; Haoran Xie – Education and Information Technologies, 2025
The rapid emergence of generative artificial intelligence (GenAI) tools has underscored the urgent need for pre-service teachers to develop technological pedagogical content knowledge (TPACK) and self-regulated learning (SRL) strategies -- both critical for integrating AI into classrooms. However, existing teacher education programmes lack…
Descriptors: Artificial Intelligence, Technology Uses in Education, Preservice Teacher Education, Technological Literacy
Guanyao Xu; Aiqing Yu; Anna Gao; Guy Trainin – Education and Information Technologies, 2025
Teachers' technological knowledge is essential for integrating Artificial Intelligence (AI) into language education. Additionally, teachers' self-efficacy and attitudes toward AI can foster the integration of AI tools in education. This study developed an Artificial Intelligence -- Technological Pedagogical Content Knowledge (AI-TPACK) framework…
Descriptors: Artificial Intelligence, Technological Literacy, Pedagogical Content Knowledge, Computer Attitudes
Mari Ueda; Katsuhiro Kanamori; Katsumi Takahashi; Shogo Kiryu; Tetsuo Tanaka – International Association for Development of the Information Society, 2025
Generative Artificial Intelligence (GenAI) is catalyzing a paradigm shift in higher education, demanding new pedagogical approaches that integrate AI literacy as a core competency. This paper addresses the long-standing challenge of teaching acoustics, a field often perceived as abstract and mathematically intensive by undergraduate students. We…
Descriptors: Undergraduate Students, Programming Languages, Artificial Intelligence, Higher Education
Pramod C. Mane – International Journal of Information and Learning Technology, 2025
Purpose: The purpose of this study is to investigate the accuracy and creativeness of ChatGPT in the domain of quantitative aptitude. Design/methodology/approach: ChatGPT 3.5 is used to generate multiple-choice quantitative aptitude questions. A total of 1,100 questions were created across 11 different areas of quantitative aptitude. A dataset is…
Descriptors: Accuracy, Creativity, Artificial Intelligence, Technology Uses in Education
Mehmet Ekizoglu; Ayse Nesil Demir – Discover Education, 2025
This study examines the impact of AI-assisted writing feedback on the writing skills of secondary-level EFL students. A sample of 60 Turkish high school students was divided into an experimental group receiving feedback from an AI writing assistant and a control group receiving traditional teacher feedback. Over an 8 week period, students wrote…
Descriptors: Artificial Intelligence, Computer Uses in Education, Writing Skills, Skill Development
Vincent Cho; Sofia Dueñas – Journal of Educational Research and Practice, 2025
Unbeknownst to many researchers, online surveys can be vulnerable to attacks from bots and generative artificial intelligence (AI), which can generate hundreds of fraudulent responses instantly. Confronting this challenge to data integrity, we employed an adaptive approach to test the effectiveness of various anti-fraud tactics (e.g., CAPTCHA;…
Descriptors: Artificial Intelligence, Online Surveys, Research Methodology, Research Projects
Nerea Martinez-Yarza; Josu Solabarrieta Eizaguirre; Rosa Santibáñez Gruber – Educational Research for Policy and Practice, 2025
Social-emotional skills can help students overcome learning challenges and prevent at-risk or problematic behaviors as well as promote their overall well-being and psychological growth. Despite the recent evolution of intervention programs targeting social-emotional skills, psychometrically sound and effective assessment tools remain limited. The…
Descriptors: Rating Scales, Social Development, Emotional Development, Interpersonal Competence
Yulu Hou – Journal of Learning Development in Higher Education, 2025
This reflection offers a student perspective on integrating generative artificial intelligence (AI) in graduate education, focusing on collaboration rather than replacement. Drawing on my experience in a doctoral seminar on the intellectual history of educational technology, I examine four key instructional practices that shaped meaningful AI use:…
Descriptors: Artificial Intelligence, Natural Language Processing, Graduate Students, Student Attitudes
Stefanelli, Silvia; Alloway, Tracy Packiam – Journal of Intellectual Disabilities, 2020
Borderline intellectual functioning is a neurodevelopmental condition characterized by an intelligence quotient (IQ) in the range of 70-85. The present study aimed to investigate the mathematical abilities and the working memory of students with borderline intellectual functioning (BIF). The sample group included 10 year-old students with BIF (n =…
Descriptors: Mathematics Skills, Short Term Memory, Children, Slow Learners
Wilinski, Antoni; Kupracz, Lukasz – Informatics in Education, 2020
The aim of the article is to determine in the studied groups the multiple intelligence distribution defined in the 1980s by Howard Gardner. The research was conducted in three groups of respondents. The first study group was first-year students of computer science, the second was master (2nd degree) students, educationally 4 years older than the…
Descriptors: Multiple Intelligences, Intelligence Tests, College Freshmen, Graduate Students
Kulikowski, Konrad; Orzechowski, Jaroslaw – Applied Cognitive Psychology, 2019
This study aimed to investigate the relationships between working memory capacity, fluid intelligence (Gf), and work engagement within the framework of the job demands-resources theory. Multioccupational employees (N = 175 in Study 1 and N = 383 in Study 2) completed sets of Gf, complex span, and n-back tests, along with job resources and work…
Descriptors: Intelligence, Short Term Memory, Employees, Work Attitudes
Canivez, Gary L.; Youngstrom, Eric A. – Applied Measurement in Education, 2019
The Cattell-Horn-Carroll (CHC) taxonomy of cognitive abilities married John Horn and Raymond Cattell's Extended Gf-Gc theory with John Carroll's Three-Stratum Theory. While there are some similarities in arrangements or classifications of tasks (observed variables) within similar broad or narrow dimensions, other salient theoretical features and…
Descriptors: Taxonomy, Cognitive Ability, Intelligence, Cognitive Tests
Canivez, Gary L.; Watkins, Marley W.; McGill, Ryan J. – British Journal of Educational Psychology, 2019
Background: There is inadequate information regarding the factor structure of the Wechsler Intelligence Scale for Children -- Fifth UK Edition (WISC-V[superscript UK]; Wechsler, 2016a, Wechsler Intelligence Scale for Children-Fifth UK Edition, Harcourt Assessment, London, UK) to guide interpretation. Aims and methods: The WISC-V[superscript UK]…
Descriptors: Children, Intelligence Tests, Construct Validity, Factor Analysis
OECD Publishing, 2021
Artificial intelligence (AI) and robotics are major breakthrough technologies that are transforming the economy and society. The OECD's Artificial Intelligence and the Future of Skills (AIFS) project is developing a programme to assess the capabilities of AI and robotics, and their impact on education and work. This volume reports on the first…
Descriptors: Artificial Intelligence, Skill Development, Evaluation, Competence
Dorfman, Leonid; Kalugin, Alexey; Mishkevich, Arina – International Society for Technology, Education, and Science, 2021
The commonality is one of underlying conditions that provide the individual-intellectual integrations. Three forms identify the commonality. The first is the causal commonality, the second is the generalizing commonality, third is the intertwining commonality. Confirmatory one- and two- factor analysis (CFA) and path analysis (PA) specified the…
Descriptors: Undergraduate Students, Factor Analysis, Individual Characteristics, Attribution Theory

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