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Jantakun, Thiti; Jantakun, Kitsadaporn; Jantakoon, Thada – Online Submission, 2023
Advances in augmented and virtual reality (AVR) technology have allowed for the development of AVR interactive learning environments (AVR-ILEs) with increasing fidelity. When paired with a suitably capable computer tutor agent, such environments can permit adaptive and self-directed learning of procedural skills in some cases. We undertook a…
Descriptors: Virtual Classrooms, Computer Simulation, Intelligent Tutoring Systems, Skill Development
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Peng, Tzu-Hsiang; Wang, Tzu-Hua – Journal of Educational Computing Research, 2022
Pedagogical agents (PAs) are a crucial aspect of the e-learning environment. A PA is defined as a virtual character presented on an interface, and they are designed to promote student learning. PAs have been widely discussed in academic papers. However, an appropriate analysis framework has not been proposed because of the diversity and complexity…
Descriptors: Electronic Learning, Instructional Effectiveness, Intelligent Tutoring Systems, Evaluation Methods
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Zhou, Guojing; Azizsoltani, Hamoon; Ausin, Markel Sanz; Barnes, Tiffany; Chi, Min – International Journal of Artificial Intelligence in Education, 2022
In interactive e-learning environments such as Intelligent Tutoring Systems, pedagogical decisions can be made at different levels of granularity. In this work, we focus on making decisions at "two levels": whole problems vs. single steps and explore three types of granularity: "problem-level only" ("Prob-Only"),…
Descriptors: Electronic Learning, Intelligent Tutoring Systems, Decision Making, Problem Solving
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Laine, Joakim; Lindqvist, Timo; Korhonen, Tiina; Hakkarainen, Kai – International Journal of Technology in Education and Science, 2022
Advances in immersive virtual reality (I-VR) technology have allowed for the development of I-VR learning environments (I-VRLEs) with increasing fidelity. When coupled with a sufficiently advanced computer tutor agent, such environments can facilitate asynchronous and self-regulated approaches to learning procedural skills in industrial settings.…
Descriptors: Intelligent Tutoring Systems, Computer Simulation, Industry, Job Skills
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Wu, Ting-Ting; Lee, Hsin-Yu; Li, Pin-Hui; Huang, Chia-Nan; Huang, Yueh-Min – Journal of Educational Computing Research, 2024
This study combines ChatGPT, Apple's Shortcuts, and LINE to create the ChatGPT-based Intelligent Learning Aid (CILA), aiming to enhance self-regulation progress and knowledge construction in blended learning. CILA offers real-time, convergent information to learners' inquiries, as opposed to traditional Google search engine that provide divergent…
Descriptors: Independent Study, Learning Processes, Blended Learning, Artificial Intelligence
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Mohammad M. Khajah – Journal of Educational Data Mining, 2024
Bayesian Knowledge Tracing (BKT) is a popular interpretable computational model in the educational mining community that can infer a student's knowledge state and predict future performance based on practice history, enabling tutoring systems to adaptively select exercises to match the student's competency level. Existing BKT implementations do…
Descriptors: Students, Bayesian Statistics, Intelligent Tutoring Systems, Cognitive Development
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Clarivando Francisco Belizário Júnior; Fabiano Azevedo Dorça; Luciana Pereira de Assis; Alessandro Vivas Andrade – International Journal of Learning Technology, 2024
Loop-based intelligent tutoring systems (ITSs) support the learning process using a step-by-step problem-solving approach. A limitation of ITSs is that few contents are compatible with this approach. On the other hand, recommendation systems can recommend different types of content but ignore the fine-grained concepts typical of the step-by-step…
Descriptors: Artificial Intelligence, Educational Technology, Individualized Instruction, Cognitive Style
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Aniekan Essien; Oyegoke Teslim Bukoye; Xianghan O'Dea; Marios Kremantzis – Studies in Higher Education, 2024
This study investigates the influence of generative artificial intelligence (GAI), specifically AI text generators (ChatGPT), on critical thinking skills in UK postgraduate business school students. Using Bloom's taxonomy as theoretical underpinning, we adopt a mixed-method research employing a sample of 107 participants to investigate both the…
Descriptors: Foreign Countries, Graduate Students, Business Education, Artificial Intelligence
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Fadoua Balabdaoui; Nora Dittmann-Domenichini; Henry Grosse; Claudia Schlienger; Gerd Kortemeyer – Discover Education, 2024
We report the results of a 4800-respondent survey among students at a technical university regarding their usage of artificial intelligence tools, as well as their expectations and attitudes about these tools. We find that many students have come to differentiated and thoughtful views and decisions regarding the use of artificial intelligence. The…
Descriptors: Foreign Countries, College Students, Artificial Intelligence, Student Attitudes
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Jaeho Jeon; Seongyong Lee; Seongyune Choi – Interactive Learning Environments, 2024
Chatbot research has received growing attention due to the rapid diversification of chatbot technology, as demonstrated by the emergence of large language models (LLMs) and their integration with automatic speech recognition. However, among various chatbot types, speech-recognition chatbots have received limited attention in relevant research…
Descriptors: Literature Reviews, Content Analysis, Second Language Learning, Artificial Intelligence
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Arthur William Fodouop Kouam – Discover Education, 2024
This study investigates the effectiveness of Intelligent Tutoring Systems (ITS) in supporting students with varying levels of programming experience. Through a mixed-methods research design, the study explores the impact of ITS on student performance, adaptability to different skill levels, and best practices for utilizing ITS in heterogeneous…
Descriptors: Intelligent Tutoring Systems, Instructional Effectiveness, Programming, Skill Development
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Alj Zakaria; Bouayad Anas; Mohammed Ouçamah Cherkaoui Malki – Journal of Educators Online, 2024
A massive open online course (MOOC) is a powerful tool for expanding educational opportunities, but one of the major challenges facing MOOCs is the high dropout rate. Low completion rates indicate issues with student engagement and motivation. Gamification, the incorporation of game elements in non-game contexts, has shown promise in increasing…
Descriptors: Gamification, MOOCs, Student Motivation, Learner Engagement
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Jing Shi; Na Wan; Roslina Ibrahim – International Journal of Web-Based Learning and Teaching Technologies, 2024
The application of computer technology has revolutionized and promoted the traditional mode of piano teaching. Nowadays, many companies and institutions have begun to apply computer technology to online piano teaching. This paper analyzes the difficulties faced by students in piano teaching and the development of piano assistant practice and…
Descriptors: Music Education, Musical Instruments, Teaching Methods, Algorithms
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Nazia Alam; Behrooz Mostafavi; Sutapa Dey Tithi; Min Chi; Tiffany Barnes – International Educational Data Mining Society, 2024
Reducing learning time or training time in intelligent tutors is a challenging research problem. In this study, we aim to reduce training time while maintaining student performance in intelligent tutors. We propose a Deep Reinforcement Learning (DRL) based method to determine when students need more training and when they don't. We design an…
Descriptors: Intelligent Tutoring Systems, Training, Time, Artificial Intelligence
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Shakya, Anup; Rus, Vasile; Venugopal, Deepak – International Educational Data Mining Society, 2021
Predicting student problem-solving strategies is a complex problem but one that can significantly impact automated instruction systems since they can adapt or personalize the system to suit the learner. While for small datasets, learning experts may be able to manually analyze data to infer student strategies, for large datasets, this approach is…
Descriptors: Prediction, Problem Solving, Intelligent Tutoring Systems, Learning Strategies
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