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Tang, Kai-Yu; Chang, Ching-Yi; Hwang, Gwo-Jen – Interactive Learning Environments, 2023
Artificial intelligence (AI) has been widely explored across the world over the past decades. A particularly emerging topic is the application of AI in e-learning (AIeL) to improve the effectiveness of teaching and learning in precision education. This study aims to systematically review publication patterns for AIeL research with a focus on…
Descriptors: Educational Trends, Trend Analysis, Artificial Intelligence, Technology Uses in Education
Seongyune Choi; Yeonju Jang; Hyeoncheol Kim – Interactive Learning Environments, 2024
Intelligent Personal Assistants (IPAs) are becoming more prevalent in daily and educational contexts, increasing the possibility of using them as learning partners that can provide more personalized and learner-centric learning opportunities. However, research has primarily focused on educational advantages that IPAs may provide, overlooking…
Descriptors: Intelligent Tutoring Systems, Foreign Countries, Technology Uses in Education, Independent Study
Wan, Haipeng; Yu, Shengquan – Interactive Learning Environments, 2023
Most online learning researchers use resource recommendation and retrieve based on learning performance and learning style to provide accurate learning resources, but it is a closed and passive adaptive way. Learners always do not know the recommendation rationale and just receive the result-oriented recommended resources without having a chance…
Descriptors: Electronic Learning, Intelligent Tutoring Systems, Artificial Intelligence, Cognitive Mapping
Abdur Rahman; Prajeesh Tomy – Interactive Learning Environments, 2024
Speaking in a second/foreign language, especially in English, is one of the most anxiety-provoking tasks for language learners. Anxiety provoked while speaking in a second language distresses the learners and further affects their oral proficiency in English. This article focuses on investigating the presence of anxiety among (n = 86) first-year…
Descriptors: Intelligent Tutoring Systems, Second Language Learning, Second Language Instruction, Speech Communication
Meng, Lingling; Zhang, Mingxin; Zhang, Wanxue; Chu, Yu – Interactive Learning Environments, 2021
Bayesian knowledge tracing model (BKT) is a typical student knowledge assessment method. It is widely used in intelligent tutoring systems. In the standard BKT model, all knowledge and skills are independent of each other. However, in the process of student learning, they have a very close relation. A student may understand knowledge B better when…
Descriptors: Bayesian Statistics, Intelligent Tutoring Systems, Student Evaluation, Knowledge Level
Yung-Hsiang Hu; Jo Shan Fu; Hui-Chin Yeh – Interactive Learning Environments, 2024
Artificial intelligence aims to restructure and process re-engineering education and teaching processes and accelerate the evolution of the whole education system from information to intelligence. Robotic Process Automation (RPA) robots learn by observing people at work, analyzing user processes repeatedly, and adjusting or correcting automated…
Descriptors: Intelligent Tutoring Systems, Robotics, Automation, Instructional Effectiveness
Liang, Jia-Cing; Hwang, Gwo-Jen; Chen, Mei-Rong Alice; Darmawansah, Darmawansah – Interactive Learning Environments, 2023
This study explores the roles and research foci of AILEd (Artificial Intelligence in Language Education). The AILEd studies published from 1990 to 2020 in the WOS (Web of Science) database were included in the present study. Based on the well-recognized Technology-based Learning Review model, several dimensions, such as research methods, research…
Descriptors: Artificial Intelligence, Technology Uses in Education, Second Language Learning, Educational Trends
Gonulal, Talip – Interactive Learning Environments, 2023
Intelligent personal assistants (IPAs), which are voice-activated agents enabling human--computer interaction, have recently been reported to be pedagogically useful agents in language learning. IPAs use various forms of humor to better communicate with users and to compensate for any performance limitations. In order to understand the IPAs' sense…
Descriptors: Intelligent Tutoring Systems, Second Language Learning, Second Language Instruction, English (Second Language)
VanLehn, Kurt; Burkhardt, Hugh; Cheema, Salman; Kang, Seokmin; Pead, Daniel; Schoenfeld, Alan; Wetzel, Jon – Interactive Learning Environments, 2021
Mathematics is often taught by explaining an idea, then giving students practice in applying it. Tutoring systems can increase the effectiveness of this method by monitoring the students' practice and giving feedback. However, math can also be taught by having students work collaboratively on problems that lead them to discover the idea. Here,…
Descriptors: Intelligent Tutoring Systems, Cooperative Learning, Mathematics Instruction, Instructional Effectiveness
Wang, Shuai; Christensen, Claire; Cui, Wei; Tong, Richard; Yarnall, Louise; Shear, Linda; Feng, Mingyu – Interactive Learning Environments, 2023
Adaptive learning systems personalize instruction to students' individual learning needs and abilities. Such systems have shown positive impacts on learning. Many schools in the United States have adopted adaptive learning systems, and the rate of adoption in China is accelerating, reaching almost 2 million unique users for one product alone in…
Descriptors: Comparative Analysis, Teaching Methods, Intelligent Tutoring Systems, Foreign Countries
Di Zhang; Gwo-Jen Hwang; Shih-Ting Chu – Interactive Learning Environments, 2024
When encountering difficulties in conventional educational games, learners seldom self-regulate to discover and organize the learning content in the game environment. With the development of the human-computer interaction technology, computer agents are gradually being applied to educational games to provide personalized guidance or support to…
Descriptors: Intelligent Tutoring Systems, Educational Games, Technology Uses in Education, Academic Achievement
Christine Ting-Yu Yang; Shu-Li Lai; Howard Hao-Jan Chen – Interactive Learning Environments, 2024
Research has revealed the positive impact of intelligent personal assistants (IPAs) on L2 learners' oral development and learning attitude. These studies, however, focused mostly on the in-class use of IPAs, with existing research on the out-of-class use being exploratory. To fill the gap of lacking empirical investigations on IPA-based autonomous…
Descriptors: Intelligent Tutoring Systems, Second Language Learning, Influence of Technology, Personal Autonomy
Zhang, Jingjing; Gao, Ming; Holmes, Wayne; Mavrikis, Manolis; Ma, Ning – Interactive Learning Environments, 2021
Feedback in exploratory learning systems has been depicted as an important contributor to encourage exploration. However, few studies have explored learners' interaction patterns associated with feedback and the use of external representations in exploratory learning environments. This study used Fractions Lab, an exploratory learning environment…
Descriptors: Interaction, Behavior Patterns, Discovery Learning, Fractions
Davy Tsz Kit Ng; Jiahong Su; Jac Ka Lok Leung; Samuel Kai Wah Chu – Interactive Learning Environments, 2024
Artificial intelligence (AI) literacy has emerged to equip students with digital skills for effective evaluation, communication, collaboration, and ethical use of AI in online, home, and workplace settings. Countries are increasingly developing AI curricula to support students' technological skills for future studies and careers. However, there is…
Descriptors: Artificial Intelligence, Computer Software, Technology Uses in Education, Secondary School Students
Paquette, Luc; Baker, Ryan S. – Interactive Learning Environments, 2019
Learning analytics research has used both knowledge engineering and machine learning methods to model student behaviors within the context of digital learning environments. In this paper, we compare these two approaches, as well as a hybrid approach combining the two types of methods. We illustrate the strengths of each approach in the context of…
Descriptors: Comparative Analysis, Student Behavior, Models, Case Studies

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