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Cecilia Ka Yuk Chan – Education and Information Technologies, 2025
This study explores the concept of AI guilt, a psychological phenomenon where individuals feel guilt or moral discomfort when using generative AI tools, fearing negative perceptions from others or feeling disingenuous (Chan, 2024). The phenomenon has become increasingly relevant as AI tools gain prominence in educational contexts. This paper…
Descriptors: Artificial Intelligence, Anxiety, Measures (Individuals), Psychological Patterns
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Anshul Saxena; Bikramjit Rishi – International Journal of Educational Management, 2025
Purpose: This study investigates how generative AI models (ChatGPT, Claude and Gemini) can be systematically integrated into curriculum design using Hilda Taba's inductive model. Addressing Sustainable Development Goal 4 (SDG 4), it introduces the EduCompass framework to enhance inclusivity and instructional quality. Design/methodology/approach: A…
Descriptors: Artificial Intelligence, Sustainable Development, Masters Programs, Business Education
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Gordana Stankovska; Dimitar Dimitrovski; Fatime Ziberi; Tatjana Takasmanova – Bulgarian Comparative Education Society, 2025
Education plays a central role in preparing students to face the changes and complexities of the environment. Society and students' life experiences have significantly transformed in the last century. Emotional intelligence, communication skills, and adaptability are important for university students. Thus, the main objective of this research was…
Descriptors: College Students, Social Emotional Learning, Emotional Intelligence, Learning Motivation
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Smitha Dev; Mary Varghese; Sidra Rafique – Journal of Social Studies Education Research, 2025
Emotional intelligence (EI) is recognized as a critical factor in students' academic success, with research suggesting that students with higher EI demonstrate stronger self-regulation and increased academic engagement. However, limited evidence exists regarding the effectiveness of classroombased interventions designed to improve EI skills in…
Descriptors: Foreign Countries, Undergraduate Students, Emotional Intelligence, Intervention
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Lewis Doyle; Robert A. Nash; Viktoria Jakcsiova; Ellen Turner – Technology, Knowledge and Learning, 2025
Providing feedback is time-consuming for teachers, but new Artificial Intelligence tools aim to reduce this burden and improve feedback quality. We asked teachers (N = 12) to trial an AI tool for providing feedback on students' work. In semi-structured interviews they reflected on the positive and negative implications of such tools. In focus…
Descriptors: Feedback (Response), Teacher Attitudes, Student Attitudes, Artificial Intelligence
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Charity M. Dacey; Jasmin Cowin; Joy de los Reyes – Journal of Invitational Theory and Practice, 2025
The authors integrate the classical elements -- earth, air, water, and fire -- within post-human perspectives to explore the multifaceted integration of Artificial Intelligence (AI) in educational contexts. A transdisciplinary approach invited a fertile dialogue among three academic experts from distinct fields of study, who then examined the…
Descriptors: Interdisciplinary Approach, Artificial Intelligence, Technology Uses in Education, Technology Integration
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Karabulut, Ridvan; Ömeroglu, Esra – International Journal of Curriculum and Instruction, 2021
The study aimed to develop a measure that enables gifted children to be picked out in early childhood through the nomination of teachers. In order to collect the data, a conceptual framework based on Gardner's theory of multiple intelligences was set to identify gifted children. Once the conceptual framework was created, a 64-item framework…
Descriptors: Test Validity, Test Reliability, Test Construction, Gifted
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Kristja´nsson, Kristja´n – British Educational Research Journal, 2021
The concept of "phronesis" enters educational discourse at various levels of engagement, and it continues to fascinate and frustrate educational theorists in equal measure. This article begins by charting the vagaries of three educational discourses on phronesis, and by eliciting insights from the recently burgeoning wisdom research…
Descriptors: Intelligence, Educational Philosophy, Hypothesis Testing, Educational Research
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Frost, Stephanie; McCalla, Gord – International Journal of Artificial Intelligence in Education, 2021
The focus of this paper is a novel pedagogical planner that we have developed called the CFLS planner (Collaborative Filtering based on Learning Sequences). The CFLS planner has been designed for an open-ended and unstructured learning environment based on the ecological approach (EA) architecture (McCalla "Journal of Interactive Media in…
Descriptors: Educational Planning, Intelligent Tutoring Systems, Artificial Intelligence
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Ryan, Joseph J.; Gontkovsky, Samuel T. – Journal of Psychoeducational Assessment, 2021
We analyzed data from the WASI-II manual to determine discrepancy score reliabilities of the Verbal Comprehension (VCI) and Perceptual Reasoning (PRI) indexes and the four subtests in the child and adult standardization samples. Reliabilities of the VCI-PRI discrepancy scores range from 0.78 to 0.86 for children and 0.82 to 0.89 for adults and…
Descriptors: Intelligence Tests, Test Reliability, Scores, Children
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Kim, Minsam; Shim, Yugeun; Lee, Seewoo; Loh, Hyunbin; Park, Juneyoung – International Educational Data Mining Society, 2021
Knowledge Tracing (KT) is a task to model students' knowledge based on their coursework interactions within an Intelligent Tutoring System (ITS). Recently, Deep Neural Networks (DNN) showed superb performance over classical methods on multiple dataset benchmarks. While most Deep Learning based Knowledge Tracing (DLKT) models are optimized for…
Descriptors: Models, Artificial Intelligence, Knowledge Level, Evaluation Methods
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Pursun, Tugba; Efilti, Erkan – European Journal of Educational Research, 2019
This study aims to analyse the emotional intelligence scores of the special education teacher candidates for the predictor of multiple intelligences areas. This study was conducted through relational scanning model. 211 teacher candidates, 106 females and 105 males, participated in the study. Data were collected through Personal Information Form,…
Descriptors: Emotional Intelligence, Preservice Teachers, Special Education Teachers, Multiple Intelligences
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Labelle, Fannie; Béliveau, Marie-Julie; Jauvin, Karine; Akzam-Ouellette, Marc-Antoine – Canadian Journal of School Psychology, 2023
Intellectual impairments in preschoolers have been widely studied. A regularity that emerges is that children's intellectual impairments have an important impact on later adjustments in life. However, few studies have looked at the intellectual profiles of young psychiatric outpatients. This study aimed to describe the intelligence profile of…
Descriptors: Preschool Children, Referral, Intelligence Quotient, Intellectual Disability
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Liangliang Xia; Xin An; Xinyi Li; Yan Dong – Journal of Educational Computing Research, 2026
Student learning agency, encompassing key abilities, essential mental characteristics and active actions, is recognized as a crucial factor for effective teaching and learning in the generative artificial intelligence (AI) era. This study examines the structural relationships among perceptions of generative AI (i.e., effort expectancy, performance…
Descriptors: Artificial Intelligence, Technology Uses in Education, Intention, College Students
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Stephanie Ward; Molly M. Jameson – Journal of Faculty Development, 2026
While the need for faculty development around generative artificial intelligence is great, the capacity of institutions to provide this training can be limited. The director of a campus center for teaching and learning and an academic teaching librarian share their process of collaborating to provide professional development on AI literacy,…
Descriptors: Academic Libraries, Faculty Development, Artificial Intelligence, Technological Literacy
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