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Paladines, José; Ramírez, Jaime; Berrocal-Lobo, Marta – Interactive Learning Environments, 2023
This paper addresses the challenge of integrating a dialog system with an ITS created for supporting procedural training in a 3D virtual environment. To this end, we first describe the desired features of the dialog to be provided to students in such system. Then, we explain some technical issues of our proposal such as the architecture; the…
Descriptors: Intelligent Tutoring Systems, Dialogs (Language), Man Machine Systems, Computer Simulation
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Helene Ackermann; Anja Henke; Johann Chevalère; Hae Seon Yun; Verena V. Hafner; Niels Pinkwart; Rebecca Lazarides – npj Science of Learning, 2025
Rising interest in artificial intelligence in education reinforces the demand for evidence-based implementation. This study investigates how tutor agents' physical embodiment and anthropomorphism (student-reported sociability, animacy, agency, and disturbance) relate to affective (on-task enjoyment) and cognitive (task performance) learning within…
Descriptors: Artificial Intelligence, Intelligent Tutoring Systems, Animals, Human Body
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Angélique Létourneau; Marion Deslandes Martineau; Patrick Charland; John Alexander Karran; Jared Boasen; Pierre Majorique Léger – npj Science of Learning, 2025
The use of artificial intelligence in education (AIEd) has grown exponentially in the last decade, particularly intelligent tutoring systems (ITSs). Despite the increased use of ITSs and their promise to improve learning, their real educational value remains unclear. This systematic review aims to identify the effects of ITSs on K-12 students'…
Descriptors: Artificial Intelligence, Technology Uses in Education, Intelligent Tutoring Systems, Elementary Secondary Education
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Ambroise Baillifard; Maxime Gabella; Pamela Banta Lavenex; Corinna S. Martarelli – Education and Information Technologies, 2025
Effective learning strategies based on principles like personalization, retrieval practice, and spaced repetition are often challenging to implement due to practical constraints. Here we explore the integration of AI tutors to complement learning programs in accordance with learning sciences. A semester-long study was conducted at UniDistance…
Descriptors: Intelligent Tutoring Systems, Artificial Intelligence, Instructional Effectiveness, Learning Strategies
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Pauldy C. J. Otermans; Charlotte Roberts; Stephanie Baines – International Journal of Technology in Education, 2025
This study examines the relationship between students' attitudes toward artificial intelligence (AI) and both AI competence and conceptions. 176 UK university students completed a survey where they were asked to rate statements in relation to their attitudes towards AI, their AI competence and their conceptions about AI using 5-point Likert-type…
Descriptors: Artificial Intelligence, Student Attitudes, Technology Uses in Education, Educational Technology
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Antonie Alm; Louise Ohashi – Technology in Language Teaching & Learning, 2024
This exploratory study investigated how 367 university language educators from 48 countries/regions responded to ChatGPT in the first 10 weeks after its release. It explored awareness, use, attitudes, and perceived impact through a survey collecting both quantitative and qualitative data. Most participants demonstrated moderate awareness, but…
Descriptors: Higher Education, Language Teachers, Artificial Intelligence, Intelligent Tutoring Systems
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Milos Ilic; Goran Kekovic; Vladimir Mikic; Katerina Mangaroska; Lazar Kopanja; Boban Vesin – IEEE Transactions on Learning Technologies, 2024
In recent years, there has been an increasing trend of utilizing artificial intelligence (AI) methodologies over traditional statistical methods for predicting student performance in e-learning contexts. Notably, many researchers have adopted AI techniques without conducting a comprehensive investigation into the most appropriate and accurate…
Descriptors: Artificial Intelligence, Academic Achievement, Prediction, Programming
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Galafassi, Cristiano; Galafassi, Fabiane Flores Penteado; Vicari, Rosa Maria; Reategui, Eliseo Berni – International Journal of Artificial Intelligence in Education, 2023
This work presents the intelligent tutoring system, EvoLogic, developed to assist students in problems of natural production in propositional logic. EvoLogic has been modeled as a multiagent system composed of three autonomous agents: interface, pedagogical and specialist agents. It supports pedagogical strategies inspired by the theory of…
Descriptors: Intelligent Tutoring Systems, Logical Thinking, Models, Teaching Methods
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Lu, Yu; Wang, Deliang; Chen, Penghe; Meng, Qinggang; Yu, Shengquan – International Journal of Artificial Intelligence in Education, 2023
As a prominent aspect of modeling learners in the education domain, knowledge tracing attempts to model learner's cognitive process, and it has been studied for nearly 30 years. Driven by the rapid advancements in deep learning techniques, deep neural networks have been recently adopted for knowledge tracing and have exhibited unique advantages…
Descriptors: Learning Processes, Artificial Intelligence, Intelligent Tutoring Systems, Data Analysis
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Wang, Huanhuan; Tlili, Ahmed; Huang, Ronghuai; Cai, Zhenyu; Li, Min; Cheng, Zui; Yang, Dong; Li, Mengti; Zhu, Xixian; Fei, Cheng – Education and Information Technologies, 2023
Intelligent Tutoring Systems (ITSs) have a great potential to effectively transform teaching and learning. As more efforts have been put on designing and developing ITSs and integrating them within learning and instruction, mixed types of results about the effectiveness of ITS have been reported. Therefore, it is necessary to investigate how ITSs…
Descriptors: Intelligent Tutoring Systems, Literature Reviews, Research Methodology, Instructional Effectiveness
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Sanz Ausin, Markel; Maniktala, Mehak; Barnes, Tiffany; Chi, Min – International Journal of Artificial Intelligence in Education, 2023
While Reinforcement learning (RL), especially Deep RL (DRL), has shown outstanding performance in video games, little evidence has shown that DRL can be successfully applied to human-centric tasks where the ultimate RL goal is to make the "human-agent interactions" productive and fruitful. In real-life, complex, human-centric tasks, such…
Descriptors: Artificial Intelligence, Intelligent Tutoring Systems, Teaching Methods, Learning Activities
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Pelánek, Radek; Effenberger, Tomáš; Cechák, Jaroslav – International Journal of Artificial Intelligence in Education, 2022
Complexity and difficulty are two closely related but distinct concepts. These concepts are important in the development of intelligent learning systems, e.g., for sequencing items, student modeling, or content management. We show how to use complexity and difficulty measures in the development of learning systems and provide guidance on how to…
Descriptors: Difficulty Level, Intelligent Tutoring Systems, Measurement Techniques, Computer System Design
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Marin, Rafael; Notargiacomo, Pollyana – International Journal of Web-Based Learning and Teaching Technologies, 2022
Financial literacy is a theme that integrates public policies for the social development of a country and an element to be worked on from different aspects to improve people's living standards, providing well-being. In this context, Stima is proposed, a system capable of acquiring knowledge from experts and tutors to help students to follow the…
Descriptors: Financial Literacy, Intelligent Tutoring Systems, Decision Making, Planning
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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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Youngjin Lee – Education and Information Technologies, 2025
This study investigates the development and evaluation of a Retrieval-Augmented Generation (RAG)-based statistics tutor designed to assist students with quantitative analysis methods. The RAG approach was employed to address the well-documented issue of hallucination in Large Language Models (LLMs). A computer tutor was developed that utilizes…
Descriptors: Artificial Intelligence, Intelligent Tutoring Systems, Teachers, Students
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