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Direct linkBabette Bühler; Efe Bozkir; Patricia Goldberg; Ömer Sümer; Sidney D'Mello; Peter Gerjets; Ulrich Trautwein; Enkelejda Kasneci – International Journal of Artificial Intelligence in Education, 2025
Student's shift of attention away from a current learning task to task-unrelated thought, also called mind wandering, occurs about 30% of the time spent on education-related activities. Its frequent occurrence has a negative effect on learning outcomes across learning tasks. Automated detection of mind wandering might offer an opportunity to…
Descriptors: Attention, Automation, Identification, Video Technology
Igor Esnaola; Sara Martínez-Gregorio; Lorea Azpiazu; Iratxe Antonio-Agirre; Amparo Oliver – Psychology in the Schools, 2025
The main goal of this study was to analyze a longitudinal model which reviews the relationships between parent trust, trait emotional intelligence (EI) and self-concept. The sample was composed of 484 Spanish adolescents (226 boys, 258 girls) who completed the questionnaires "Parent Trust and Understanding Scale, Emotional Quotient Inventory:…
Descriptors: Parent Attitudes, Trust (Psychology), Emotional Intelligence, Self Concept
William McGalliard; Samuel Otten – Digital Experiences in Mathematics Education, 2025
This article considers the rise of generative Artificial Intelligence (GenAI) in the context of secondary mathematics education, focusing on its responses to cognitively demanding tasks and the pedagogical implications of these interactions. Using tools such as ChatGPT (OpenAI) and Gemini (Google), we investigate how GenAI engages in complex…
Descriptors: Artificial Intelligence, Secondary School Mathematics, Computer Uses in Education, Mathematics Education
Alden J. Edson; Ashley Fabry; Ahmad Wachidul Kohar; Leslie Bondaryk; Elizabeth Difanis Phillips – Digital Experiences in Mathematics Education, 2025
This article reports on a novel approach to integrate artificial intelligence into a digital collaborative platform embedded with a problem-based mathematics curriculum. Using design research methodologies, we developed a new "proof-of-concept" design feature called "student proportional reasoning arrows (SPArrows)." SPArrows…
Descriptors: Artificial Intelligence, Documentation, Problem Based Learning, Computer Uses in Education
Mariusz Chrostowski; Andrzej Jacek Najda – Journal of Religious Education, 2025
Biblical didactics is an important element of confessional religious education. In traditional settings, it is primarily associated with working with the text, alone or in groups, in plenary discussion or pantomime. Nowadays, however, young people are increasingly acquiring their knowledge--including about the Bible--on the Internet, using new…
Descriptors: Artificial Intelligence, Natural Language Processing, Technology Uses in Education, Religious Education
Gabriela C. Zapata, Editor – Routledge, Taylor & Francis Group, 2025
"Generative AI Technologies, Multiliteracies, and Language Education" is a comprehensive edited volume that examines the integration of Generative AI (GenAI) technologies within the framework of multiliteracies pedagogies to enhance language teaching and learning. This collection of chapters offers an in-depth understanding of how GenAI…
Descriptors: Artificial Intelligence, Technology Uses in Education, Multiple Literacies, Second Language Instruction
Elpis V. Pavlidou; Susana Silva; Vasiliki Folia – European Journal of Psychology of Education, 2025
The precise relation between implicit learning and reading abilities has not been determined yet, given the limited research on implicit learning between typically developing (TD) students and those with reading difficulties (RD). Additionally, the influence of modality-related implicit learning performance in children remains largely unknown. To…
Descriptors: Reading Difficulties, Grammar, Artificial Intelligence, Artificial Languages
Download full textFrank Morley; Emma Walland – Research Matters, 2025
The recent development of Large Language Models (LLMs) such as Claude, Gemini, and GPT has led to widespread attention on potential applications of these models. Marking exams is a domain which requires the ability to interpret and evaluate student responses (often consisting of written text), and the potential for artificial intelligence (AI)…
Descriptors: Ethics, Artificial Intelligence, Automation, Scoring
Monsalve-Pulido, Julian; Aguilar, Jose; Montoya, Edwin – Education and Information Technologies, 2023
The adaptation of traditional systems to service-oriented architectures is very frequent, due to the increase in technologies for this type of architecture. This has led to the construction of frameworks or methodologies for adapting computational projects to service-oriented architecture (SOA) technology. In this work, a framework for adaptation…
Descriptors: Artificial Intelligence, Information Technology, Design, Governance
Kebede, Mihiretu M.; Le Cornet, Charlotte; Fortner, Renée Turzanski – Research Synthesis Methods, 2023
We aimed to evaluate the performance of supervised machine learning algorithms in predicting articles relevant for full-text review in a systematic review. Overall, 16,430 manually screened titles/abstracts, including 861 references identified relevant for full-text review were used for the analysis. Of these, 40% (n = 6573) were sub-divided for…
Descriptors: Automation, Literature Reviews, Artificial Intelligence, Algorithms
Kataoka, Yuki; Taito, Shunsuke; Yamamoto, Norio; So, Ryuhei; Tsutsumi, Yusuke; Anan, Keisuke; Banno, Masahiro; Tsujimoto, Yasushi; Wada, Yoshitaka; Sagami, Shintaro; Tsujimoto, Hiraku; Nihashi, Takashi; Takeuchi, Motoki; Terasawa, Teruhiko; Iguchi, Masahiro; Kumasawa, Junji; Ichikawa, Takumi; Furukawa, Ryuki; Yamabe, Jun; Furukawa, Toshi A. – Research Synthesis Methods, 2023
There are currently no abstract classifiers, which can be used for new diagnostic test accuracy (DTA) systematic reviews to select primary DTA study abstracts from database searches. Our goal was to develop machine-learning-based abstract classifiers for new DTA systematic reviews through an open competition. We prepared a dataset of abstracts…
Descriptors: Competition, Classification, Diagnostic Tests, Accuracy
Elkhatat, Ahmed M.; Elsaid, Khaled; Almeer, Saeed – International Journal for Educational Integrity, 2023
The proliferation of artificial intelligence (AI)-generated content, particularly from models like ChatGPT, presents potential challenges to academic integrity and raises concerns about plagiarism. This study investigates the capabilities of various AI content detection tools in discerning human and AI-authored content. Fifteen paragraphs each…
Descriptors: Artificial Intelligence, Integrity, Plagiarism, Educational Technology
Van Biesen, D.; Van Damme, T.; Pineda, R. C.; Burns, J. – Journal of Intellectual Disabilities, 2023
Our aim was to identify the suitability of three assessment tools (i.e., Flanker test, Updating Word Span, and Color Trails Test) for future inclusion in the classification process of elite Paralympic athletes with intellectual disability and to assess the strength of the relation between Executive function (EF) and intelligence. Cognitive and EF…
Descriptors: Intellectual Disability, Inclusion, Executive Function, Intelligence
Baena-Rojas, Jose Jaime; Castillo-Martínez, Isolda Margarita; Méndez-Garduño, Juana Isabel; Suárez-Brito, Paloma; López-Caudana, Edgar Omar – Journal of Social Studies Education Research, 2023
Various technological devices, especially information communications technologies (ICTs), have become increasingly remarkable in higher education to help develop students' skills and qualifications. Considering this trend, supported by several academic theories, this paper proposes a breakthrough guidebook for universities and other scholastic…
Descriptors: Information Technology, Artificial Intelligence, Robotics, Higher Education
Zhao, Li; Zheng, Yi; Zhao, Junbang; Li, Guoqiang; Compton, Brian J.; Zhang, Rui; Fang, Fang; Heyman, Gail D.; Lee, Kang – Child Development, 2023
Academic cheating is common, but little is known about its early emergence. It was examined among Chinese second to sixth graders (N = 2094; 53% boys, collected between 2018 and 2019) using a machine learning approach. Overall, 25.74% reported having cheated, which was predicted by the best machine learning algorithm (Random Forest) at a mean…
Descriptors: Cheating, Elementary School Students, Artificial Intelligence, Foreign Countries
