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Direct linkToyokawa, Yuko; Horikoshi, Izumi; Majumdar, Rwitajit; Ogata, Hiroaki – Smart Learning Environments, 2023
In inclusive education, students with different needs learn in the same context. With the advancement of artificial intelligence (AI) technologies, it is expected that they will contribute further to an inclusive learning environment that meets the individual needs of diverse learners. However, in Japan, we did not find any studies exploring…
Descriptors: Barriers, Affordances, Artificial Intelligence, Inclusion
Xiaohong Ji; Xin Liu; Xin Chen; Rong Li – Education and Information Technologies, 2025
Students classroom behaviours are complex and variable, involving multiple aspects such as students personality traits, learning attitudes, thinking styles and learning abilities, but traditional classroom behavioural assessment cannot comprehensively and reasonably assess students classroom learning status. The study adopts the improved Yolov5…
Descriptors: Student Behavior, Classroom Techniques, Artificial Intelligence, Identification
Jinhee Kim – Education and Information Technologies, 2024
Moving beyond the direct support all alone by a human teacher or an Artificial Intelligence (AI) system, optimizing the complementary strengths of the two has aroused great expectations and educational innovation potential. Yet, the conceptual guidance of how best to structure and implement teacher-AI collaboration (TAC) while ensuring teachers'…
Descriptors: Teacher Attitudes, Artificial Intelligence, Man Machine Systems, Curriculum Development
Hongyu Xie; He Xiao; Yu Hao – International Journal of Web-Based Learning and Teaching Technologies, 2024
Modern e-learning system is a representative service form in innovative service industry. This paper designs a personalized service domain system, optimizes various parameters and can be applied to different education quality evaluation, and proposes a decision tree recommendation algorithm. Information gain is carried out through many existing…
Descriptors: Artificial Intelligence, Electronic Learning, Individualized Instruction, Models
Aneng He; Wenwen Yuan; Lai Soon Lee; Tian Tian – Smart Learning Environments, 2025
This paper explores the challenge of achieving consistent effectiveness in integrating Mathematics Education Technology (MET) in K-12 classrooms, focusing on factors such as technology type, timing, and instructional strategies. It highlights the difficulties novice teachers face in optimizing MET compared to experienced educators, emphasizing the…
Descriptors: Artificial Intelligence, Prediction, Models, Mathematics Education
Gabriel, Florence; Marrone, Rebecca; Van Sebille, Ysabella; Kovanovic, Vitomir; de Laat, Maarten – Irish Educational Studies, 2022
Technology is omnipresent in our rapidly changing digital world. Capitalising on these technologies is paramount for education systems to support a modern workforce. This study presents a review of 10 countries, highlighting how different public education systems have integrated digital technologies for teaching and learning. These education…
Descriptors: Electronic Learning, Educational Policy, Learning Strategies, Comparative Education
Download full textLeaton Gray, Sandra – London Review of Education, 2020
The introduction of artificial intelligence in education (AIED) is likely to have a profound impact on the lives of children and young people. This article explores the different types of artificial intelligence (AI) systems in common use in education, their social context and their relationship with the growth of commercial knowledge monopolies.…
Descriptors: Artificial Intelligence, Technology Uses in Education, Privacy, Data Use
Hu, Xiangen, Ed.; Barnes, Tiffany, Ed.; Hershkovitz, Arnon, Ed.; Paquette, Luc, Ed. – International Educational Data Mining Society, 2017
The 10th International Conference on Educational Data Mining (EDM 2017) is held under the auspices of the International Educational Data Mining Society at the Optics Velley Kingdom Plaza Hotel, Wuhan, Hubei Province, in China. This years conference features two invited talks by: Dr. Jie Tang, Associate Professor with the Department of Computer…
Descriptors: Data Analysis, Data Collection, Graphs, Data Use
