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Nesra Yannier; Scott E. Hudson; Henry Chang; Kenneth R. Koedinger – International Journal of Artificial Intelligence in Education, 2024
Adaptivity in advanced learning technologies offer the possibility to adapt to different student backgrounds, which is difficult to do in a traditional classroom setting. However, there are mixed results on the effectiveness of adaptivity based on different implementations and contexts. In this paper, we introduce AI adaptivity in the context of a…
Descriptors: Artificial Intelligence, Computer Software, Feedback (Response), Outcomes of Education
Ig Ibert Bittencourt; Geiser Chalco; Jário Santos; Sheyla Fernandes; Jesana Silva; Naricla Batista; Claudio Hutz; Seiji Isotani – International Journal of Artificial Intelligence in Education, 2024
The unprecedented global movement of school education to find technological and intelligent solutions to keep the learning ecosystem working was not enough to recover the impacts of COVID-19, not only due to learning-related challenges but also due to the rise of negative emotions, such as frustration, anxiety, boredom, risk of burnout and the…
Descriptors: Artificial Intelligence, COVID-19, Pandemics, Computer Software
Dimitrova, Vania; Mitrovic, Antonija – International Journal of Artificial Intelligence in Education, 2022
Video-based learning is widely used today in both formal education and informal learning in a variety of contexts. Videos are especially powerful for transferable skills learning (e.g. communicating, negotiating, collaborating), where contextualization in personal experience and ability to see different perspectives are crucial. With the ubiquity…
Descriptors: Artificial Intelligence, Video Technology, Teaching Methods, Transfer of Training
Dermeval, Diego; Paiva, Ranilson; Bittencourt, Ig Ibert; Vassileva, Julita; Borges, Daniel – International Journal of Artificial Intelligence in Education, 2018
Authoring tools have been broadly used to design Intelligent Tutoring Systems (ITS). However, ITS community still lacks a current understanding of how authoring tools are used by non-programmer authors to design ITS. Hence, the objective of this work is to review how authoring tools have been supporting ITS design for non-programmer authors. In…
Descriptors: Intelligent Tutoring Systems, Programming, Computer Software, Evidence
Johnson, W. Lewis – International Journal of Artificial Intelligence in Education, 2019
Cloud computing offers developers of learning environments access to unprecedented amounts of learner data. This makes possible "data-driven development" (D[superscript 3]) of learning environments. In the D[superscript 3] approach the learning environment is a data collection tool as well a learning tool. It continually collects data…
Descriptors: Foreign Countries, Data Use, English (Second Language), Second Language Instruction
Ritter, Steven – International Journal of Artificial Intelligence in Education, 2016
"An Architecture for Plug-in Tutor Agents" (Ritter and Koedinger 1996) proposed a software architecture designed around the idea that tutors could be built as plug-ins for existing software applications. Looking back on the paper now, we can see that certain assumptions about the future of software architecture did not come to be, making…
Descriptors: Intelligent Tutoring Systems, Computer Software, Technology Uses in Education, Teaching Methods
Weber, Gerhard; Brusilovsky, Peter – International Journal of Artificial Intelligence in Education, 2016
This paper present provides a broader view on ELM-ART, one of the first Web-based Intelligent Educational systems that offered a creative combination of two different paradigms--Intelligent Tutoring and Adaptive Hypermedia technologies. The unique dual nature of ELM-ART contributed to its long life and research impact and was a result of…
Descriptors: Intelligent Tutoring Systems, Educational Technology, Technology Uses in Education, Hypermedia
Gilbert, Stephen B.; Blessing, Stephen B.; Guo, Enruo – International Journal of Artificial Intelligence in Education, 2015
The Extensible Problem Specific Tutor (xPST) allows authors who are not cognitive scientists and not programmers to quickly create an intelligent tutoring system that provides instruction akin to a model-tracing tutor. Furthermore, this instruction is overlaid on existing software, so that the learner's interface does not have to be made from…
Descriptors: Intelligent Tutoring Systems, Authors, Computer Software, Computer Interfaces
Murray, Tom – International Journal of Artificial Intelligence in Education, 2016
Intelligent Tutoring Systems authoring tools are highly complex educational software applications used to produce highly complex software applications (i.e. ITSs). How should our assumptions about the target users (authors) impact the design of authoring tools? In this article I first reflect on the factors leading to my original 1999 article on…
Descriptors: Usability, Programming, Computer Software, Intelligent Tutoring Systems
Hoppe, H. Ulrich – International Journal of Artificial Intelligence in Education, 2016
The 1998 paper by Martin Mühlenbrock, Frank Tewissen, and myself introduced a multi-agent architecture and a component engineering approach for building open distributed learning environments to support group learning in different types of classroom settings. It took up prior work on "multiple student modeling" as a method to configure…
Descriptors: Guidelines, Intelligent Tutoring Systems, Cooperative Learning, Modeling (Psychology)
Heffernan, Neil T.; Heffernan, Cristina Lindquist – International Journal of Artificial Intelligence in Education, 2014
The ASSISTments project is an ecosystem of a few hundred teachers, a platform, and researchers working together. Development professionals help train teachers and get teachers to participate in studies. The platform and these teachers help researchers (sometimes explicitly and sometimes implicitly) simply by using content the teacher selects. The…
Descriptors: Intelligent Tutoring Systems, Educational Research, Formative Evaluation, Artificial Intelligence
Benjamin D. Nye; Arthur C. Graesser; Xiangen Hu – International Journal of Artificial Intelligence in Education, 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
Dodigovic, Marina – International Journal of Artificial Intelligence in Education, 2013
This article focuses on the use of natural language processing (NLP) to facilitate second language learning within the context of academic English. It describes a full cycle of educational software development, from needs analysis to software testing. Two studies are included: 1) the needs analysis conducted to develop the Intelligent Sentence…
Descriptors: Intelligent Tutoring Systems, Natural Language Processing, Second Language Learning, English (Second Language)
Valdés Aguirre, Benjamín; Ramírez Uresti, Jorge A.; du Boulay, Benedict – International Journal of Artificial Intelligence in Education, 2016
Sharing user information between systems is an area of interest for every field involving personalization. Recommender Systems are more advanced in this aspect than Intelligent Tutoring Systems (ITSs) and Intelligent Learning Environments (ILEs). A reason for this is that the user models of Intelligent Tutoring Systems and Intelligent Learning…
Descriptors: Intelligent Tutoring Systems, Models, Open Source Technology, Computers
Lynch, Collin; Ashley, Kevin D.; Pinkwart, Niels; Aleven, Vincent – International Journal of Artificial Intelligence in Education, 2009
In this paper we consider prior definitions of the terms "ill-defined domain" and "ill-defined problem". We then present alternate definitions that better support research at the intersection of Artificial Intelligence and Education. In our view both problems and domains are ill-defined when essential concepts, relations, or criteria are un- or…
Descriptors: Definitions, Artificial Intelligence, Problem Solving, Educational Research
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