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Mukesh Kumar Rohil; Saksham Mahajan; Trishna Paul – Education and Information Technologies, 2025
Intelligent Tutoring Systems (ITS) and Augmented Reality (AR) have become greatly popular in current scenario, especially for helping students in mastering difficult subjects through a variety of different methods with the implementation of smart algorithms. There are many papers in the current literature that discuss the ITS architecture and the…
Descriptors: Intelligent Tutoring Systems, Artificial Intelligence, Physical Environment, Simulated Environment
Miguel Ángel Escotet – Prospects, 2024
Artificial Intelligence is a fast-evolving technology with enormous potential for education, higher education, and learning. AI can also negatively impact how societies and their citizens engage ethically with these generated, still-unexplored tools. These technological breakthroughs present both opportunity and potential peril. The problem of any…
Descriptors: Futures (of Society), Artificial Intelligence, Technology Uses in Education, Higher Education
Murat Polat; Ibrahim Hakan Karatas; Nurgün Varol – Leadership and Policy in Schools, 2025
The incorporation of artificial intelligence (AI) into educational management offers personalized learning, adaptive tutoring, and efficient resource management. However, ethical considerations such as fairness, transparency, accountability, and privacy are crucial. This paper reviews literature and conducts a bibliometric analysis on ethical AI…
Descriptors: Ethics, Artificial Intelligence, Technology Uses in Education, Leadership
Hayley Ko; Ewa A. Szyszko Hovden; Unni-Mette Stamnes Köpp; Miriam S. Johnson; Gunn Astrid Baugerud – Applied Cognitive Psychology, 2025
Healthcare professionals often receive limited training in information gathering, especially for cases of suspected child maltreatment. This pilot study evaluated a brief interview training program using an artificial intelligence-driven child avatar chatbot to simulate realistic encounters with children. GPT-3 and one-shot prompting were used to…
Descriptors: Artificial Intelligence, Technology Uses in Education, Dentistry, Graduate Students
Janice D. Gobert; Michael A. Sao Pedro; Haiying Li; Christine Lott – Grantee Submission, 2023
In this entry, we define Intelligent Tutoring Systems (ITSs) and present a description of their core components. We outline a history of the development of ITSs with a focus on key issues that have driven change and innovation in ITSs from their inception to present day. We also present a brief case study on a specific ITS, Inq-ITS (Inquiry…
Descriptors: Intelligent Tutoring Systems, Student Evaluation, Evaluation Methods, Natural Language Processing
Ju, Song; Zhou, Guojing; Barnes, Tiffany; Chi, Min – International Educational Data Mining Society, 2020
Identifying critical decisions is one of the most challenging decision-making problems in real-world applications. In this work, we propose a novel Reinforcement Learning (RL) based Long-Short Term Rewards (LSTR) framework for critical decisions identification. RL is a machine learning area concerning with inducing effective decision-making…
Descriptors: Decision Making, Reinforcement, Artificial Intelligence, Man Machine Systems
Amy Adair; Ellie Segan; Janice Gobert; Michael Sao Pedro – Grantee Submission, 2023
Developing models and using mathematics are two key practices in internationally recognized science education standards, such as the Next Generation Science Standards (NGSS). However, students often struggle with these two intersecting practices, particularly when developing mathematical models about scientific phenomena. Formative…
Descriptors: Artificial Intelligence, Mathematical Models, Science Process Skills, Inquiry
The AI Teacher Test: Measuring the Pedagogical Ability of Blender and GPT-3 in Educational Dialogues
Tack, Anaïs; Piech, Chris – International Educational Data Mining Society, 2022
How can we test whether state-of-the-art generative models, such as Blender and GPT-3, are good AI teachers, capable of replying to a student in an educational dialogue? Designing an AI teacher test is challenging: although evaluation methods are much-needed, there is no off-the-shelf solution to measuring pedagogical ability. This paper reports…
Descriptors: Artificial Intelligence, Dialogs (Language), Bayesian Statistics, Decision Making
Matsuda, Noboru; Cohen, William W.; Koedinger, Kenneth R. – International Journal of Artificial Intelligence in Education, 2015
SimStudent is a machine-learning agent initially developed to help novice authors to create cognitive tutors without heavy programming. Integrated into an existing suite of software tools called Cognitive Tutor Authoring Tools (CTAT), SimStudent helps authors to create an expert model for a cognitive tutor by tutoring SimStudent on how to solve…
Descriptors: Intelligent Tutoring Systems, Programming, Computer Simulation, Models
Song, Donggil – Contemporary Educational Technology, 2017
Learning-by-teaching has been identified as one of the more effective approaches to learning. Recently, educational researchers have investigated virtual environments in order to utilize the learning-by-teaching pedagogy. In a face-to-face learning-by-teaching situation, the role of the learners is to teach their peers or instructors. In virtual…
Descriptors: Intelligent Tutoring Systems, Concept Mapping, Man Machine Systems, Interaction
Shukla, Saurabh; Shivakumar, Ashutosh; Vasoya, Miteshkumar; Pei, Yong; Lyon, Anna F. – International Association for Development of the Information Society, 2019
In this research paper, we present an AR- and AI-based mobile learning tool that provides: 1.) automatic and accurate intelligibility analysis at various levels: letter, word, phrase and sentences, 2.) immediate feedback and multimodal coaching on how to correct pronunciation, and 3.) evidence-based dynamic training curriculum tailored to each…
Descriptors: Bilingualism, Special Education, Pronunciation Instruction, Feedback (Response)
Matsuda, Noboru; Yarzebinski, Evelyn; Keiser, Victoria; Raizada, Rohan; Cohen, William W.; Stylianides, Gabriel J.; Koedinger, Kenneth R. – Journal of Educational Psychology, 2013
This article describes an advanced learning technology used to investigate hypotheses about learning by teaching. The proposed technology is an instance of a teachable agent, called SimStudent, that learns skills (e.g., for solving linear equations) from examples and from feedback on performance. SimStudent has been integrated into an online,…
Descriptors: Intelligent Tutoring Systems, Tutor Training, Computer Simulation, Artificial Intelligence
Ptaszynski, Michal; Dybala, Pawel; Mazur, Michal; Rzepka, Rafal; Araki, Kenji; Momouchi, Yoshio – International Journal of Distance Education Technologies, 2013
This paper presents research in Contextual Affect Analysis (CAA) for the need of future application in intelligent agents, such as conversational agents or artificial tutors. The authors propose a new term, Computational Fronesis (CF), to embrace the tasks included in CAA applied to development of conversational agents such as artificial tutors.…
Descriptors: Educational Technology, Technology Uses in Education, Artificial Intelligence, Intelligent Tutoring Systems
Kumar, R.; Rose, C. P. – IEEE Transactions on Learning Technologies, 2011
Tutorial Dialog Systems that employ Conversational Agents (CAs) to deliver instructional content to learners in one-on-one tutoring settings have been shown to be effective in multiple learning domains by multiple research groups. Our work focuses on extending this successful learning technology to collaborative learning settings involving two or…
Descriptors: Educational Technology, Computer Software, Computer Software Evaluation, Programming
Benjamin D. Nye; Arthur C. Graesser; Xiangen Hu – Grantee Submission, 2014
AutoTutor is a natural language tutoring system that has produced learning gains across multiple domains (e.g., computer literacy, physics, critical thinking). In this paper, we review the development, key research findings, and systems that have evolved from AutoTutor. First, the rationale for developing AutoTutor is outlined and the advantages…
Descriptors: Intelligent Tutoring Systems, Natural Language Processing, Computer Software, Artificial Intelligence