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MacLellan, Christopher J.; Koedinger, Kenneth R. – International Journal of Artificial Intelligence in Education, 2022
Intelligent tutoring systems are effective for improving students' learning outcomes (Pane et al. 2013; Koedinger and Anderson, "International Journal of Artificial Intelligence in Education," 8, 1-14, 1997; Bowen et al. "Journal of Policy Analysis and Management," 1, 94-111 2013). However, constructing tutoring systems that…
Descriptors: Intelligent Tutoring Systems, Artificial Intelligence, Models, Instructional Design
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Bull, Susan – International Journal of Artificial Intelligence in Education, 2021
For the special issue of the International Journal of Artificial Intelligence in Education dedicated to the memory of Jim Greer, this paper highlights some of Jim's extensive and always-timely contributions to the field: from his early AI-focussed research on intelligent tutoring systems, through a variety of applications deployed to support…
Descriptors: Artificial Intelligence, Intelligent Tutoring Systems, Educational Research, College Students
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du Boulay, Benedict – International Journal of Artificial Intelligence in Education, 2021
Mark and Greer's ("International Journal of Artificial Intelligence in Education," 4(2/3), 129-153, 1993) review was very influential in setting out effective goals and methods for evaluating adaptive educational systems of all kinds. A later review brought the story up to date (Greer, "International Journal of Artificial…
Descriptors: Artificial Intelligence, Computer Uses in Education, Evaluation Methods, Student Satisfaction
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Self, John – International Journal of Artificial Intelligence in Education, 2016
This document tries to describe the events of the early days of AIED research that led to the AIED Conferences and Society and, in particular, the "International Journal of Artificial Intelligence in Education."
Descriptors: Periodicals, Artificial Intelligence, Educational Research, Educational History
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Wilson, Joshua; Huang, Yue; Palermo, Corey; Beard, Gaysha; MacArthur, Charles A. – Grantee Submission, 2021
This study examined a naturalistic, districtwide implementation of an automated writing evaluation (AWE) software program called "MI Write" in elementary schools. We specifically examined the degree to which aspects of MI Write were implemented, teacher and student attitudes towards MI Write, and whether MI Write usage along with other…
Descriptors: Automation, Writing Evaluation, Feedback (Response), Computer Software
Balyan, Renu; McCarthy, Kathryn S.; McNamara, Danielle S. – Grantee Submission, 2020
For decades, educators have relied on readability metrics that tend to oversimplify dimensions of text difficulty. This study examines the potential of applying advanced artificial intelligence methods to the educational problem of assessing text difficulty. The combination of hierarchical machine learning and natural language processing (NLP) is…
Descriptors: Natural Language Processing, Artificial Intelligence, Man Machine Systems, Classification
Shute, Valerie J.; Smith, Ginny; Kuba, Renata; Dai, Chih-Pu; Rahimi, Seyedahmad; Liu, Zhichun; Almond, Russell – Grantee Submission, 2020
In honor of Jim Greer, we share our recent work--a design and development study of various learning supports embedded within the game "Physics Playground." This 2-dimensional computer game is designed to help students learn Newtonian physics and uses stealth assessment to measure, in real-time, their physics understanding. The game…
Descriptors: Physics, Educational Games, Computer Games, Science Education
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Fang, Ying; Lippert, Anne; Cai, Zhiqiang; Chen, Su; Frijters, Jan C.; Greenberg, Daphne; Graesser, Arthur C. – Grantee Submission, 2021
A common goal of Intelligent Tutoring Systems (ITS) is to provide learning environments that adapt to the varying abilities and characteristics of users. This type of adaptivity is possible only if the ITS has information that characterizes the learning behaviors of its users and can adjust its pedagogy accordingly. This study investigated an…
Descriptors: Intelligent Tutoring Systems, Educational Technology, Technology Uses in Education, Reading Comprehension
Graesser, Arthur C. – Grantee Submission, 2016
AutoTutor helps students learn by holding a conversation in natural language. AutoTutor is adaptive to the learners' actions, verbal contributions, and in some systems their emotions. Many of AutoTutor's conversation patterns simulate human tutoring, but other patterns implement ideal pedagogies that open the door to computer tutors eclipsing…
Descriptors: Intelligent Tutoring Systems, Artificial Intelligence, Communication Strategies, Dialogs (Language)
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Johnson, W. Lewis; Lester, James C. – International Journal of Artificial Intelligence in Education, 2016
Johnson et al. ("International Journal of Artificial Intelligence in Education," 11, 47-78, 2000) introduced and surveyed a new paradigm for interactive learning environments: animated pedagogical agents. The article argued for combining animated interface agent technologies with intelligent learning environments, yielding intelligent…
Descriptors: Teaching Methods, Intelligent Tutoring Systems, Outcomes of Education, Computer Assisted Instruction
Snow, Erica L.; Likens, Aaron D.; Allen, Laura K.; McNamara, Danielle S. – Grantee Submission, 2015
Game-based environments frequently afford students the opportunity to exert agency over their learning paths by making various choices within the environment. The combination of log data from these systems and dynamic methodologies may serve as a stealth means to assess how students behave (i.e., deterministic or random) within these learning…
Descriptors: High School Students, Pretests Posttests, Teaching Methods, Technology Uses in Education
Weston-Sementelli, Jennifer L.; Allen, Laura K.; McNamara, Danielle S. – Grantee Submission, 2016
Source-based essays are evaluated both on the quality of the writing and the content appropriate interpretation and use of source material. Hence, composing a high-quality source-based essay (an essay written based on source material) relies on skills related to both reading (the sources) and writing (the essay) skills. As such, source-based…
Descriptors: Reading Comprehension, Writing Strategies, Reading Strategies, Content Area Writing
Aleven, Vincent; McLaren, Bruce M.; Sewall, Jonathan; van Velsen, Martin; Popescu, Octav; Demi, Sandra; Ringenberg, Michael; Koedinger, Kenneth R. – Grantee Submission, 2016
In 2009, we reported on a new Intelligent Tutoring Systems (ITS) technology, example-tracing tutors, that can be built without programming using the Cognitive Tutor Authoring Tools (CTAT). Creating example-tracing tutors was shown to be 4-8 times as cost-effective as estimates for ITS development from the literature. Since 2009, CTAT and its…
Descriptors: Intelligent Tutoring Systems, Programming, Artificial Intelligence, Visual Aids
Heffernan, Neil T.; Ostrow, Korinn S.; Kelly, Kim; Selent, Douglas; Van Inwegen, Eric G.; Xiong, Xiaolu; Williams, Joseph Jay – Grantee Submission, 2016
Due to substantial scientific and practical progress, learning technologies can effectively adapt to the characteristics and needs of students. This article considers how learning technologies can adapt over time by crowdsourcing contributions from teachers and students -- explanations, feedback, and other pedagogical interactions. Considering the…
Descriptors: Artificial Intelligence, Educational Technology, Student Needs, Electronic Publishing
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Dimitrova, Vania; Brna, Paul – International Journal of Artificial Intelligence in Education, 2016
STyLE-OLM (Dimitrova 2003 "International Journal of Artificial Intelligence in Education," 13, 35-78) presented a framework for interactive open learner modelling which entails the development of the means by which learners can "inspect," "discuss" and "alter" the learner model that has been jointly…
Descriptors: Artificial Intelligence, Technology Uses in Education, Intelligent Tutoring Systems, Interaction
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